{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wa-sF-rOSP-q"
   },
   "source": [
    "# 第六週：分析PTT軟體工作版相關文章之詞彙關係\n",
    "課程為「文辭和文件分析」，目的為透過TF-IDF, N-gram等方法找出文章以及字詞間的關聯。\n",
    "\n",
    "這裡將會以軟體工程師版的文章做為分析資料。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vFRUrb68SP-t"
   },
   "source": [
    "動機： 由於我們未來有比較大的可能從事軟體相關的工作，因此細部討論軟體版\n",
    "\n",
    "目標： 觀察關係相近詞彙是否能歸類同一主題\n",
    "\n",
    "遇到的困難和解決方法：\n",
    "1. 許多無用字不存在停用字字典中或亂碼難以被篩選掉\n",
    "    - 透過n-gram盡量優化自定義的斷詞字典以及逐個更新停用字字典\n",
    "2. 有部分陌生詞彙不太理解其代表意思\n",
    "    - 查看原始資料集，從上下文語意來判斷\n",
    "3. 在進行圖表視覺化時，預設的中文字型無法正常顯示\n",
    "    - 從網路上下載解壓縮後的字型檔後再加載進去\n",
    "\n",
    "## 大綱\n",
    "1. 套件說明\n",
    "2. 資料前處理\n",
    "    - 2.1 基本資料介紹\n",
    "    - 2.2 資料清理\n",
    "    - 2.3 文章斷詞與整理\n",
    "3. 找出重要詞彙 - 以TFIDF為例\n",
    "    - 3.1 計算TF-IDF示範（公式）\n",
    "    - 3.2 應用於資料集（套件）\n",
    "    - 3.3 檢視結果\n",
    "4.  透過結巴斷詞與N-gram幫助建立斷詞字典\n",
    "    - 4.1 Bigram\n",
    "    - 4.2 Trigram\n",
    "    - 4.3 更新斷詞字典\n",
    "    - 4.4 Bigram視覺化\n",
    "5.  Pairwise correlation\n",
    "    - 5.1 找出相關性高的詞彙\n",
    "    - 5.2 畫出關係圖\n",
    "6. 計算文章相似度\n",
    "7. 補充：建立Ngram預測模型\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dc2WquGNSP-t"
   },
   "source": [
    "## 1. 套件說明"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1744872911010,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "2-YW3jkiSP-v"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import jieba\n",
    "import jieba.analyse\n",
    "import math\n",
    "from nltk import ngrams, FreqDist\n",
    "from collections import Counter, namedtuple\n",
    "\n",
    "import networkx as nx\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfTransformer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1744872911034,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "_twVPz6oSP-v"
   },
   "outputs": [],
   "source": [
    "# # 設定圖的中文字體 (無法顯示的話可以試試‘Microsoft JhengHei’字體)\n",
    "# # 也可參考：https://pyecontech.com/2020/03/27/python_matplotlib_chinese/\n",
    "# plt.rcParams['font.sans-serif'] = ['Arial Unicode Ms'] #使圖中中文能正常顯示\n",
    "# plt.rcParams['axes.unicode_minus'] = False #使負號能夠顯示\n",
    "\n",
    "# 微軟正黑體\n",
    "# !apt-get -y install fonts-noto-cjk\n",
    "# plt.rcParams['font.sans-serif'] = ['Noto Sans CJK TC']\n",
    "# plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o8RkT2wagjfX"
   },
   "source": [
    "原本用作業的方法沒辦法顯示中文，所以換個方法直接下載網路上的字型並做解壓縮後再匯入matplotlib套件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3587,
     "status": "ok",
     "timestamp": 1744873371850,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "JiVJt2mrTszP",
    "outputId": "2344ed73-78dd-458f-bcb9-318f39033f50"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2025-04-17 07:02:48--  https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_\n",
      "Resolving drive.google.com (drive.google.com)... 64.233.179.138, 64.233.179.101, 64.233.179.100, ...\n",
      "Connecting to drive.google.com (drive.google.com)|64.233.179.138|:443... connected.\n",
      "HTTP request sent, awaiting response... 303 See Other\n",
      "Location: https://drive.usercontent.google.com/download?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_ [following]\n",
      "--2025-04-17 07:02:48--  https://drive.usercontent.google.com/download?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_\n",
      "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 74.125.69.132, 2607:f8b0:4001:c01::84\n",
      "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|74.125.69.132|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 20659344 (20M) [application/octet-stream]\n",
      "Saving to: ‘taipei_sans_tc_beta.ttf’\n",
      "\n",
      "taipei_sans_tc_beta 100%[===================>]  19.70M   129MB/s    in 0.2s    \n",
      "\n",
      "2025-04-17 07:02:51 (129 MB/s) - ‘taipei_sans_tc_beta.ttf’ saved [20659344/20659344]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "# cloab 字體設定\n",
    "\n",
    "!wget -O taipei_sans_tc_beta.ttf https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_&export=download\n",
    "\n",
    "# 新增字體\n",
    "matplotlib.font_manager.fontManager.addfont('taipei_sans_tc_beta.ttf')\n",
    "\n",
    "# 將 font-family 設為 Taipei Sans TC Beta\n",
    "# 設定完後，之後的圖表都可以顯示中文了\n",
    "matplotlib.rc('font', family='Taipei Sans TC Beta')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z9bd-IZ7SP-w"
   },
   "source": [
    "## 2. 資料前處理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qiJEnq8lSP-w"
   },
   "source": [
    "### 2.1 基本資料介紹\n",
    "資料來源：\n",
    "+ 工作流程平台蒐集PTT 軟體工作版(Soft_Job)文章\n",
    "+ 關鍵字:\n",
    "+ 時間: 2023-01-01 ~ 2025-03-31\n",
    "+ 資料筆數：共 1547 篇文章"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2220,
     "status": "ok",
     "timestamp": 1744872916227,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "j4MweL-j6HkS",
    "outputId": "50df46fc-2a5f-44a7-a0c6-9f19a23eb438"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'dataset'...\n",
      "remote: Enumerating objects: 25, done.\u001b[K\n",
      "remote: Counting objects: 100% (25/25), done.\u001b[K\n",
      "remote: Compressing objects: 100% (23/23), done.\u001b[K\n",
      "remote: Total 25 (delta 2), reused 0 (delta 0), pack-reused 0 (from 0)\u001b[K\n",
      "Receiving objects: 100% (25/25), 7.19 MiB | 14.56 MiB/s, done.\n",
      "Resolving deltas: 100% (2/2), done.\n",
      "/content/dataset\n",
      "Archive:  softjob_23_25.csv.zip\n",
      "  inflating: softjob_23_25.csv       \n",
      "  inflating: __MACOSX/._softjob_23_25.csv  \n"
     ]
    }
   ],
   "source": [
    "# 下載 GitHub 中的 dataset\n",
    "!git clone https://github.com/leo85741/dataset.git\n",
    "%cd dataset\n",
    "!unzip softjob_23_25.csv.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "executionInfo": {
     "elapsed": 577,
     "status": "ok",
     "timestamp": 1744872916807,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "qh1oSafbSP-w",
    "outputId": "0a7aa995-aeb1-4e28-9214-62aff974f7cf"
   },
   "outputs": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>system_id</th>\n",
       "      <th>artUrl</th>\n",
       "      <th>artTitle</th>\n",
       "      <th>artDate</th>\n",
       "      <th>artPoster</th>\n",
       "      <th>artCatagory</th>\n",
       "      <th>artContent</th>\n",
       "      <th>artComment</th>\n",
       "      <th>e_ip</th>\n",
       "      <th>insertedDate</th>\n",
       "      <th>dataSource</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...</td>\n",
       "      <td>[分享]系統設計:如何取消正在執行的工作任務</td>\n",
       "      <td>2023-01-01 09:39:03</td>\n",
       "      <td>appleboy46</td>\n",
       "      <td>Soft_Job</td>\n",
       "      <td>文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...</td>\n",
       "      <td>[{\"cmtStatus\": \"→\", \"cmtPoster\": \"loadingN\", \"...</td>\n",
       "      <td>123.110.136.13</td>\n",
       "      <td>2023-01-02 02:02:59</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672559293.A...</td>\n",
       "      <td>[請益]北漂Offer金融vs假外商</td>\n",
       "      <td>2023-01-01 15:48:11</td>\n",
       "      <td>carsun00</td>\n",
       "      <td>Soft_Job</td>\n",
       "      <td>背景：\\n  私立資工學士\\n  軟體經驗5Y，後端為主，可以支援前端/CICD\\n\\n  ...</td>\n",
       "      <td>[{\"cmtStatus\": \"推\", \"cmtPoster\": \"xyzb\", \"cmtC...</td>\n",
       "      <td>111.252.104.32</td>\n",
       "      <td>2023-01-02 02:02:59</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672571470.A...</td>\n",
       "      <td>[請益]有人的公司也沒有提供API文件的嗎</td>\n",
       "      <td>2023-01-01 19:11:08</td>\n",
       "      <td>cv123741</td>\n",
       "      <td>Soft_Job</td>\n",
       "      <td>安安\\n\\n小弟剛轉前端，進到一家接案公司寫網頁，工作大概9成都在接API，\\n但公司內部沒...</td>\n",
       "      <td>[{\"cmtStatus\": \"推\", \"cmtPoster\": \"newbout\", \"c...</td>\n",
       "      <td>112.78.88.96</td>\n",
       "      <td>2023-01-02 02:03:00</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1eacdbcd-3a4c-46be-bae0-66d5c88fdeee')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-1eacdbcd-3a4c-46be-bae0-66d5c88fdeee button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-1eacdbcd-3a4c-46be-bae0-66d5c88fdeee');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-3f1ab64d-cf1a-4ef1-b0f8-06a8ffd07923\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3f1ab64d-cf1a-4ef1-b0f8-06a8ffd07923')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-3f1ab64d-cf1a-4ef1-b0f8-06a8ffd07923 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   system_id                                             artUrl  \\\n",
       "0          1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "1          2  https://www.ptt.cc/bbs/Soft_Job/M.1672559293.A...   \n",
       "2          3  https://www.ptt.cc/bbs/Soft_Job/M.1672571470.A...   \n",
       "\n",
       "                 artTitle              artDate   artPoster artCatagory  \\\n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03  appleboy46    Soft_Job   \n",
       "1      [請益]北漂Offer金融vs假外商  2023-01-01 15:48:11    carsun00    Soft_Job   \n",
       "2   [請益]有人的公司也沒有提供API文件的嗎  2023-01-01 19:11:08    cv123741    Soft_Job   \n",
       "\n",
       "                                          artContent  \\\n",
       "0  文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "1  背景：\\n  私立資工學士\\n  軟體經驗5Y，後端為主，可以支援前端/CICD\\n\\n  ...   \n",
       "2  安安\\n\\n小弟剛轉前端，進到一家接案公司寫網頁，工作大概9成都在接API，\\n但公司內部沒...   \n",
       "\n",
       "                                          artComment            e_ip  \\\n",
       "0  [{\"cmtStatus\": \"→\", \"cmtPoster\": \"loadingN\", \"...  123.110.136.13   \n",
       "1  [{\"cmtStatus\": \"推\", \"cmtPoster\": \"xyzb\", \"cmtC...  111.252.104.32   \n",
       "2  [{\"cmtStatus\": \"推\", \"cmtPoster\": \"newbout\", \"c...    112.78.88.96   \n",
       "\n",
       "          insertedDate dataSource  \n",
       "0  2023-01-02 02:02:59        ptt  \n",
       "1  2023-01-02 02:02:59        ptt  \n",
       "2  2023-01-02 02:03:00        ptt  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#匯入資料\n",
    "df = pd.read_csv('softjob_23_25.csv', encoding = 'UTF-8')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rh3ntra1SP-x"
   },
   "source": [
    "### 2.2 資料清理  \n",
    "- 去除特殊字元與標點符號，只留下中文字\n",
    "- \\u4e00-\\u9fff 為Unicode中文漢字字符的範圍"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "executionInfo": {
     "elapsed": 141,
     "status": "ok",
     "timestamp": 1744872916948,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "AiHTeWe3SP-x",
    "outputId": "803b0780-c501-4000-f84c-dba172e581c0"
   },
   "outputs": [
    {
     "data": {
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       ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "MetaData"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-198dbae6-2c6f-43c0-9891-f735d7452338\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>system_id</th>\n",
       "      <th>artUrl</th>\n",
       "      <th>artTitle</th>\n",
       "      <th>artDate</th>\n",
       "      <th>artContent</th>\n",
       "      <th>sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...</td>\n",
       "      <td>[分享]系統設計:如何取消正在執行的工作任務</td>\n",
       "      <td>2023-01-01 09:39:03</td>\n",
       "      <td>文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...</td>\n",
       "      <td>文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672559293.A...</td>\n",
       "      <td>[請益]北漂Offer金融vs假外商</td>\n",
       "      <td>2023-01-01 15:48:11</td>\n",
       "      <td>背景：\\n  私立資工學士\\n  軟體經驗5Y，後端為主，可以支援前端/CICD\\n\\n  ...</td>\n",
       "      <td>背景私立資工學士軟體經驗後端為主可以支援前端是碩士價廢牡蠣假外商單位產險體系資融金融顧問部分...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672571470.A...</td>\n",
       "      <td>[請益]有人的公司也沒有提供API文件的嗎</td>\n",
       "      <td>2023-01-01 19:11:08</td>\n",
       "      <td>安安\\n\\n小弟剛轉前端，進到一家接案公司寫網頁，工作大概9成都在接API，\\n但公司內部沒...</td>\n",
       "      <td>安安小弟剛轉前端進到一家接案公司寫網頁工作大概成都在接但公司內部沒有提供規格文件讓我參考導致...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "    <div class=\"colab-df-buttons\">\n",
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       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-198dbae6-2c6f-43c0-9891-f735d7452338')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
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       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "\n",
       "    <script>\n",
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       "        document.querySelector('#df-198dbae6-2c6f-43c0-9891-f735d7452338 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "        const element = document.querySelector('#df-198dbae6-2c6f-43c0-9891-f735d7452338');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "            title=\"Suggest charts\"\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
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      "text/plain": [
       "   system_id                                             artUrl  \\\n",
       "0          1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "1          2  https://www.ptt.cc/bbs/Soft_Job/M.1672559293.A...   \n",
       "2          3  https://www.ptt.cc/bbs/Soft_Job/M.1672571470.A...   \n",
       "\n",
       "                 artTitle              artDate  \\\n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "1      [請益]北漂Offer金融vs假外商  2023-01-01 15:48:11   \n",
       "2   [請益]有人的公司也沒有提供API文件的嗎  2023-01-01 19:11:08   \n",
       "\n",
       "                                          artContent  \\\n",
       "0  文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "1  背景：\\n  私立資工學士\\n  軟體經驗5Y，後端為主，可以支援前端/CICD\\n\\n  ...   \n",
       "2  安安\\n\\n小弟剛轉前端，進到一家接案公司寫網頁，工作大概9成都在接API，\\n但公司內部沒...   \n",
       "\n",
       "                                            sentence  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...  \n",
       "1  背景私立資工學士軟體經驗後端為主可以支援前端是碩士價廢牡蠣假外商單位產險體系資融金融顧問部分...  \n",
       "2  安安小弟剛轉前端進到一家接案公司寫網頁工作大概成都在接但公司內部沒有提供規格文件讓我參考導致...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MetaData = df.copy()\n",
    "\n",
    "# 去除一些不需要的欄位\n",
    "MetaData = MetaData.drop(['artPoster', 'artCatagory', 'artComment', 'e_ip', 'insertedDate', 'dataSource'], axis=1)\n",
    "# 只留下中文字 (artContent -> sentence)\n",
    "MetaData['sentence'] = MetaData['artContent'].apply(lambda x: re.sub('[^\\u4e00-\\u9fff]+', '', str(x)) if isinstance(x, str) else '')\n",
    "\n",
    "MetaData.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RIfY0fVpSP-x"
   },
   "source": [
    "### 2.3 文章斷詞與整理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1744872916949,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "-N1_B14NSP-x"
   },
   "outputs": [],
   "source": [
    "# 設定繁體中文詞庫\n",
    "jieba.set_dictionary('./dict/dict.txt.big')\n",
    "\n",
    "# 新增stopwords\n",
    "with open('./dict/stopwords.txt',encoding=\"utf-8\") as f:\n",
    "    stopWords = [line.strip() for line in f.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1744873852618,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "Ks668aPASP-x"
   },
   "outputs": [],
   "source": [
    "# 設定斷詞 function\n",
    "def getToken(row):\n",
    "    seg_list = jieba.lcut(row)\n",
    "\n",
    "    exclude_words = {'一三五', '一一列舉', '真的', '</s>'}\n",
    "    seg_list = [\n",
    "        w for w in seg_list\n",
    "        if w not in stopWords\n",
    "        and len(w) > 1\n",
    "        and not re.match(r'(.)\\1+$', w)     # 篩掉重複字詞\n",
    "        and w not in exclude_words          # 自訂排除\n",
    "        # and not w.startswith('一')\n",
    "    ]\n",
    "\n",
    "    return seg_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "executionInfo": {
     "elapsed": 27272,
     "status": "ok",
     "timestamp": 1744873883893,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "K21ZkhjgSP-x",
    "outputId": "5d20391a-ba10-466a-c061-7fd2010978da"
   },
   "outputs": [
    {
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       "      border-bottom-color: var(--fill-color);\n",
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       "\n",
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       "        const charts = await google.colab.kernel.invokeFunction(\n",
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       "\n",
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       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "\n",
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       "0  文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "0  文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
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       "\n",
       "                                            sentence word  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   文字  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   教學  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   教學  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = MetaData.copy()\n",
    "\n",
    "# 斷詞、去除停用字並將word欄位展開\n",
    "# data['word'] = data.sentence.apply(getToken).explode('word')\n",
    "\n",
    "data['word'] = data['sentence'].apply(getToken)\n",
    "data = data.explode('word')\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 771
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1744873883894,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "L46Hobw889ci",
    "outputId": "e65559e3-5b83-47c3-fa2d-67534a1f2118"
   },
   "outputs": [
    {
     "data": {
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       "      <td>2025-03-31 01:03:18</td>\n",
       "      <td>受惠軟體開發板各位大大分享，我也來分享一些在博弈軟體開發工作的心得\\n\\n網頁好讀版\\nht...</td>\n",
       "      <td>受惠軟體開發板各位大大分享我也來分享一些在博弈軟體開發工作的心得網頁好讀版從事軟體開發也有年...</td>\n",
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       "      <td>受惠軟體開發板各位大大分享我也來分享一些在博弈軟體開發工作的心得網頁好讀版從事軟體開發也有年...</td>\n",
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       "      <td>受惠軟體開發板各位大大分享我也來分享一些在博弈軟體開發工作的心得網頁好讀版從事軟體開發也有年...</td>\n",
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       "      <td>受惠軟體開發板各位大大分享我也來分享一些在博弈軟體開發工作的心得網頁好讀版從事軟體開發也有年...</td>\n",
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       "      <td>受惠軟體開發板各位大大分享我也來分享一些在博弈軟體開發工作的心得網頁好讀版從事軟體開發也有年...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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       "</div>\n",
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       "\n",
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       "\n",
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       "\n",
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       "        document.querySelector('#df-15f2a316-aea5-4a81-8759-7c2584d22abb button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-15f2a316-aea5-4a81-8759-7c2584d22abb');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "\n",
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       "\n",
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       "  @keyframes spin {\n",
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       "      border-right-color: var(--fill-color);\n",
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       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-2a3ce5ab-68fb-46a8-817f-9aaca805828e button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
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       "\n",
       "  <div id=\"id_1f878685-d060-4f25-8697-d76518061853\">\n",
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       "      .colab-df-generate {\n",
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       "        border: none;\n",
       "        border-radius: 50%;\n",
       "        cursor: pointer;\n",
       "        display: none;\n",
       "        fill: #1967D2;\n",
       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
       "        background-color: #E2EBFA;\n",
       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "        fill: #174EA6;\n",
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       "\n",
       "      [theme=dark] .colab-df-generate {\n",
       "        background-color: #3B4455;\n",
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       "      }\n",
       "\n",
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      "text/plain": [
       "      system_id                                             artUrl  \\\n",
       "0             1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "0             1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "0             1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "0             1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "0             1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "...         ...                                                ...   \n",
       "1546       1547  https://www.ptt.cc/bbs/Soft_Job/M.1743354201.A...   \n",
       "1546       1547  https://www.ptt.cc/bbs/Soft_Job/M.1743354201.A...   \n",
       "1546       1547  https://www.ptt.cc/bbs/Soft_Job/M.1743354201.A...   \n",
       "1546       1547  https://www.ptt.cc/bbs/Soft_Job/M.1743354201.A...   \n",
       "1546       1547  https://www.ptt.cc/bbs/Soft_Job/M.1743354201.A...   \n",
       "\n",
       "                    artTitle              artDate  \\\n",
       "0     [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "0     [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "0     [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "0     [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "0     [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "...                      ...                  ...   \n",
       "1546          [心得]8年的博弈業工作心得  2025-03-31 01:03:18   \n",
       "1546          [心得]8年的博弈業工作心得  2025-03-31 01:03:18   \n",
       "1546          [心得]8年的博弈業工作心得  2025-03-31 01:03:18   \n",
       "1546          [心得]8年的博弈業工作心得  2025-03-31 01:03:18   \n",
       "1546          [心得]8年的博弈業工作心得  2025-03-31 01:03:18   \n",
       "\n",
       "                                             artContent  \\\n",
       "0     文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "0     文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "0     文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "0     文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "0     文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "...                                                 ...   \n",
       "1546  受惠軟體開發板各位大大分享，我也來分享一些在博弈軟體開發工作的心得\\n\\n網頁好讀版\\nht...   \n",
       "1546  受惠軟體開發板各位大大分享，我也來分享一些在博弈軟體開發工作的心得\\n\\n網頁好讀版\\nht...   \n",
       "1546  受惠軟體開發板各位大大分享，我也來分享一些在博弈軟體開發工作的心得\\n\\n網頁好讀版\\nht...   \n",
       "1546  受惠軟體開發板各位大大分享，我也來分享一些在博弈軟體開發工作的心得\\n\\n網頁好讀版\\nht...   \n",
       "1546  受惠軟體開發板各位大大分享，我也來分享一些在博弈軟體開發工作的心得\\n\\n網頁好讀版\\nht...   \n",
       "\n",
       "                                               sentence word  \n",
       "0     文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   文字  \n",
       "0     文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   教學  \n",
       "0     文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   教學  \n",
       "0     文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   影片  \n",
       "0     文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   範例  \n",
       "...                                                 ...  ...  \n",
       "1546  受惠軟體開發板各位大大分享我也來分享一些在博弈軟體開發工作的心得網頁好讀版從事軟體開發也有年...   猶豫  \n",
       "1546  受惠軟體開發板各位大大分享我也來分享一些在博弈軟體開發工作的心得網頁好讀版從事軟體開發也有年...   博弈  \n",
       "1546  受惠軟體開發板各位大大分享我也來分享一些在博弈軟體開發工作的心得網頁好讀版從事軟體開發也有年...   軟體  \n",
       "1546  受惠軟體開發板各位大大分享我也來分享一些在博弈軟體開發工作的心得網頁好讀版從事軟體開發也有年...   開發  \n",
       "1546  受惠軟體開發板各位大大分享我也來分享一些在博弈軟體開發工作的心得網頁好讀版從事軟體開發也有年...   工作  \n",
       "\n",
       "[168854 rows x 7 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gC6dbx2TSP-x"
   },
   "source": [
    "## 3. 找出重要詞彙 - 以TFIDF為例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QLnlJ47bSP-x"
   },
   "source": [
    "TF-IDF 是一種統計方法，可用來評估單詞對於文件的集合的重要程度  \n",
    "- **TF** (Term Frequency)：某一個詞彙在某一個文件中所出現的頻率  \n",
    "    - TF = 詞彙在該文件中出現次數 / 該文件中詞彙總數  \n",
    "- **IDF** (Inverse Document Frequent)：為文件數除以某特定詞彙有被多少文件所提及的數量取log  \n",
    "    - IDF = log( 總文件數量 / 包含該詞彙的文件數量 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ewN0DtY8SP-x"
   },
   "source": [
    "### 3.1 計算TF-IDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7tsLj2tBSP-x"
   },
   "source": [
    "使用sklearn中計算詞頻與tf-idf的套件。\n",
    "\n",
    "DTM\n",
    "\n",
    "Document term matrix (DTM)，是一種用於自然語言處理的數學矩陣，描述了在一組文件中各個詞彙出現的頻率。\n",
    "DTM 中的每一行代表一個文件(Document)，每一列代表一個詞彙(Term)，每一格的值表示該詞彙在該文件中的出現次數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "executionInfo": {
     "elapsed": 72,
     "status": "ok",
     "timestamp": 1744873893453,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "wHiEqlGDSP-x",
    "outputId": "08e1ea2a-a9c3-4d63-d5f8-e248da9e621d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
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       ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "softjob_df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-cc472616-3a8f-4efc-9acf-75191f13ba96\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>system_id</th>\n",
       "      <th>artUrl</th>\n",
       "      <th>artTitle</th>\n",
       "      <th>artDate</th>\n",
       "      <th>artContent</th>\n",
       "      <th>sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...</td>\n",
       "      <td>[分享]系統設計:如何取消正在執行的工作任務</td>\n",
       "      <td>2023-01-01 09:39:03</td>\n",
       "      <td>文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...</td>\n",
       "      <td>文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672559293.A...</td>\n",
       "      <td>[請益]北漂Offer金融vs假外商</td>\n",
       "      <td>2023-01-01 15:48:11</td>\n",
       "      <td>背景：\\n  私立資工學士\\n  軟體經驗5Y，後端為主，可以支援前端/CICD\\n\\n  ...</td>\n",
       "      <td>背景私立資工學士軟體經驗後端為主可以支援前端是碩士價廢牡蠣假外商單位產險體系資融金融顧問部分...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672571470.A...</td>\n",
       "      <td>[請益]有人的公司也沒有提供API文件的嗎</td>\n",
       "      <td>2023-01-01 19:11:08</td>\n",
       "      <td>安安\\n\\n小弟剛轉前端，進到一家接案公司寫網頁，工作大概9成都在接API，\\n但公司內部沒...</td>\n",
       "      <td>安安小弟剛轉前端進到一家接案公司寫網頁工作大概成都在接但公司內部沒有提供規格文件讓我參考導致...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cc472616-3a8f-4efc-9acf-75191f13ba96')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-cc472616-3a8f-4efc-9acf-75191f13ba96 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-cc472616-3a8f-4efc-9acf-75191f13ba96');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-1f2ab017-3cc9-46db-9c8c-c974a21d0aba\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1f2ab017-3cc9-46db-9c8c-c974a21d0aba')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-1f2ab017-3cc9-46db-9c8c-c974a21d0aba button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   system_id                                             artUrl  \\\n",
       "0          1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "1          2  https://www.ptt.cc/bbs/Soft_Job/M.1672559293.A...   \n",
       "2          3  https://www.ptt.cc/bbs/Soft_Job/M.1672571470.A...   \n",
       "\n",
       "                 artTitle              artDate  \\\n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "1      [請益]北漂Offer金融vs假外商  2023-01-01 15:48:11   \n",
       "2   [請益]有人的公司也沒有提供API文件的嗎  2023-01-01 19:11:08   \n",
       "\n",
       "                                          artContent  \\\n",
       "0  文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "1  背景：\\n  私立資工學士\\n  軟體經驗5Y，後端為主，可以支援前端/CICD\\n\\n  ...   \n",
       "2  安安\\n\\n小弟剛轉前端，進到一家接案公司寫網頁，工作大概9成都在接API，\\n但公司內部沒...   \n",
       "\n",
       "                                            sentence  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...  \n",
       "1  背景私立資工學士軟體經驗後端為主可以支援前端是碩士價廢牡蠣假外商單位產險體系資融金融顧問部分...  \n",
       "2  安安小弟剛轉前端進到一家接案公司寫網頁工作大概成都在接但公司內部沒有提供規格文件讓我參考導致...  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softjob_df = MetaData.copy()\n",
    "softjob_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 27621,
     "status": "ok",
     "timestamp": 1744873922705,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "1y3cIN80SP-y",
    "outputId": "f87af6a5-f6bf-4a32-99ec-8ba8e452fc0b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"softjob_df\",\n  \"rows\": 1547,\n  \"fields\": [\n    {\n      \"column\": \"system_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 446,\n        \"min\": 1,\n        \"max\": 1547,\n        \"num_unique_values\": 1547,\n        \"samples\": [\n          31,\n          778,\n          1011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentence\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1536,\n        \"samples\": [\n          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       "type": "dataframe",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>system_id</th>\n",
       "      <th>sentence</th>\n",
       "      <th>word</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>背景私立資工學士軟體經驗後端為主可以支援前端是碩士價廢牡蠣假外商單位產險體系資融金融顧問部分...</td>\n",
       "      <td>背景 私立 資工 學士 軟體 經驗 支援 前端 碩士 價廢 牡蠣 外商 單位 產險 體系 資...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>安安小弟剛轉前端進到一家接案公司寫網頁工作大概成都在接但公司內部沒有提供規格文件讓我參考導致...</td>\n",
       "      <td>小弟 剛轉 前端 一家 接案 公司 網頁 工作 成都 公司 內部 提供 規格 文件 參考 導...</td>\n",
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       "      <td>從網路上的資訊得知如果是從事韌體開發則用的都是如果是從事開發則用的都是之類的語言那麼的發展空...</td>\n",
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       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-80f0089c-e8f7-44bb-b6c6-b43ee663dba6 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   system_id                                           sentence  \\\n",
       "0          1  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   \n",
       "1          2  背景私立資工學士軟體經驗後端為主可以支援前端是碩士價廢牡蠣假外商單位產險體系資融金融顧問部分...   \n",
       "2          3  安安小弟剛轉前端進到一家接案公司寫網頁工作大概成都在接但公司內部沒有提供規格文件讓我參考導致...   \n",
       "3          4  幾種可能做法寫在假如有用開給後端做請後端完成後將他測試時的貼上去這對新應該沒什麼問題舊有的就...   \n",
       "4          5  從網路上的資訊得知如果是從事韌體開發則用的都是如果是從事開發則用的都是之類的語言那麼的發展空...   \n",
       "\n",
       "                                                word  \n",
       "0  文字 教學 教學 影片 範例 程式 系統 架構圖 本篇 取消 執行 工作 系統 內有 資源 ...  \n",
       "1  背景 私立 資工 學士 軟體 經驗 支援 前端 碩士 價廢 牡蠣 外商 單位 產險 體系 資...  \n",
       "2  小弟 剛轉 前端 一家 接案 公司 網頁 工作 成都 公司 內部 提供 規格 文件 參考 導...  \n",
       "3  幾種 做法 有用 開給後端 請後端 將他 測試 貼上去 這對 沒什麼 舊有 記在 帳號 下次...  \n",
       "4            網路上 資訊 得知 韌體 開發 則用 開發 則用 語言 發展 空間 領域 有人  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 留下要用到的欄位\n",
    "softjob_df = softjob_df.loc[:,[\"system_id\", \"sentence\"]]\n",
    "\n",
    "# 使用空格連接斷完的詞\n",
    "softjob_df['word'] = softjob_df.sentence.apply(getToken).map(' '.join)\n",
    "\n",
    "softjob_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 645,
     "status": "ok",
     "timestamp": 1744873923351,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "OgdFI-zpSP-4",
    "outputId": "b2e9716d-9e26-4f9b-e330-29039f95bea0"
   },
   "outputs": [
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       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "  .colab-df-quickchart {\n",
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       "  [theme=dark] .colab-df-quickchart {\n",
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       "\n",
       "[1547 rows x 24580 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bag of Word\n",
    "vectorizer = CountVectorizer()\n",
    "X = vectorizer.fit_transform(softjob_df[\"word\"])\n",
    "vocabulary = vectorizer.get_feature_names_out()\n",
    "\n",
    "# 轉成 dataframe\n",
    "DTM_df = pd.DataFrame(columns = vocabulary, data = X.toarray())\n",
    "DTM_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 437,
     "status": "ok",
     "timestamp": 1744873923788,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "Vpr2I-8XSP-4",
    "outputId": "5bb17146-00f3-4956-eed2-b740d50acd10"
   },
   "outputs": [
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       "<p>1547 rows × 24580 columns</p>\n",
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       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('TFIDF_df')\"\n",
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      ],
      "text/plain": [
       "      一下下  一下全部  一下子  一不小心  一不鳥   一並   一串  一事無成   一二  一二一  ...  齊聚一堂   龍年  \\\n",
       "0     0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0  0.0  ...   0.0  0.0   \n",
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       "4     0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0  0.0  ...   0.0  0.0   \n",
       "...   ...   ...  ...   ...  ...  ...  ...   ...  ...  ...  ...   ...  ...   \n",
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       "\n",
       "       龍心   龍滑   龍潭   龍頭   龐大   龜大   龜山   龜毛  \n",
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       "\n",
       "[1547 rows x 24580 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer = TfidfTransformer()\n",
    "# 將詞頻矩陣X統計成TF-IDF值\n",
    "tfidf = transformer.fit_transform(X)\n",
    "\n",
    "# 轉成dataframe\n",
    "TFIDF_df = pd.DataFrame(columns = vocabulary, data = tfidf.toarray())\n",
    "\n",
    "TFIDF_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "msTnhK0vSP-4"
   },
   "source": [
    "### 3.2 檢視結果   \n",
    "根據tfidf值來挑出重要詞彙：\n",
    "1. 取每一個字詞在所有文件裡的TF-IDF平均值\n",
    "2. 取每份文件裡TF-IDF值最大的前10個字詞當為常用字詞，再取每一個字詞在常用字詞中出現頻率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dE4UzPXeSP-4"
   },
   "source": [
    "#### 方法1： 取每一個字詞在所有文件裡的TF-IDF平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "executionInfo": {
     "elapsed": 146,
     "status": "ok",
     "timestamp": 1744873923935,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "i6c77gT8SP-4",
    "outputId": "93c21c11-5a27-4df1-8054-88a69168487f"
   },
   "outputs": [
    {
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       "      <td>0.015202</td>\n",
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       "      <th>21324</th>\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
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       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
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       "        document.querySelector('#df-5a919b41-863b-471c-87ff-1a3bb8e6873d button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-5a919b41-863b-471c-87ff-1a3bb8e6873d');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "  [theme=dark] .colab-df-quickchart {\n",
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       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
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       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
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       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-c896fc4c-eac6-42cd-a569-d270272078c2 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      word       avg\n",
       "3484    公司  0.041759\n",
       "8525    工作  0.030058\n",
       "23816   面試  0.025904\n",
       "23082   開發  0.020176\n",
       "8582   工程師  0.018448\n",
       "10634   技術  0.018176\n",
       "16857   程式  0.016935\n",
       "12423   時間  0.016600\n",
       "17469   系統  0.015202\n",
       "21324   軟體  0.015185"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softjob_tfidf = TFIDF_df.mean().to_frame().reset_index() ## !!\n",
    "softjob_tfidf.columns = [\"word\", \"avg\"]\n",
    "\n",
    "softjob_tfidf.sort_values('avg', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0nD13GngSP-4"
   },
   "source": [
    "#### 方法2： 取每份文件裡TF-IDF值最大的前10個字詞當為常用字詞，再取每一個字詞在常用字詞中出現頻率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 552,
     "status": "ok",
     "timestamp": 1744873924486,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "7ZAtAvoISP-4",
    "outputId": "0ba76b3a-fb06-4252-8d49-a0ee13b704f5"
   },
   "outputs": [
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      ],
      "text/plain": [
       "      doc_id  一下下  一下全部  一下子  一不小心  一不鳥   一並   一串  一事無成   一二  ...  齊聚一堂   龍年  \\\n",
       "0          1  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0  ...   0.0  0.0   \n",
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       "4          5  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0  ...   0.0  0.0   \n",
       "...      ...  ...   ...  ...   ...  ...  ...  ...   ...  ...  ...   ...  ...   \n",
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       "\n",
       "       龍心   龍滑   龍潭   龍頭   龐大   龜大   龜山   龜毛  \n",
       "0     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
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       "\n",
       "[1547 rows x 24581 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toptens = TFIDF_df.copy()\n",
    "toptens.insert(0, 'doc_id', toptens.index+1)\n",
    "\n",
    "toptens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 5004,
     "status": "ok",
     "timestamp": 1744873929491,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "0gVeJGn7SP-4",
    "outputId": "d6c5d4db-9592-4b73-ccd0-a2f626cf2f06"
   },
   "outputs": [
    {
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       "      <th>38025258</th>\n",
       "      <td>1546</td>\n",
       "      <td>龜毛</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38025259</th>\n",
       "      <td>1547</td>\n",
       "      <td>龜毛</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38025260 rows × 3 columns</p>\n",
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       "\n",
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       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
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       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
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       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
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       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "    (() => {\n",
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       "        document.querySelector('#df-21db3e12-8f0d-44c3-8081-940111e601ec button');\n",
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       "\n",
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       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('toptens')\"\n",
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       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "          doc_id word  tfidf\n",
       "0              1  一下下    0.0\n",
       "1              2  一下下    0.0\n",
       "2              3  一下下    0.0\n",
       "3              4  一下下    0.0\n",
       "4              5  一下下    0.0\n",
       "...          ...  ...    ...\n",
       "38025255    1543   龜毛    0.0\n",
       "38025256    1544   龜毛    0.0\n",
       "38025257    1545   龜毛    0.0\n",
       "38025258    1546   龜毛    0.0\n",
       "38025259    1547   龜毛    0.0\n",
       "\n",
       "[38025260 rows x 3 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toptens = toptens.melt(id_vars = \"doc_id\", var_name = \"word\", value_name = 'tfidf')\n",
    "toptens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "executionInfo": {
     "elapsed": 17510,
     "status": "ok",
     "timestamp": 1744873947002,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "nh7BpzMcSP-4",
    "outputId": "e617493d-851a-4133-a342-05c72b7e3587"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-81-1a4971485870>:3: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  toptens.groupby(\"doc_id\").apply(lambda x : x.nlargest(10, \"tfidf\")).reset_index(drop=True)\n"
     ]
    },
    {
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       "summary": "{\n  \"name\": \")\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"word\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"\\u5de5\\u7a0b\\u5e2b\",\n          \"\\u9762\\u8a66\",\n          \"\\u7cfb\\u7d71\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 34,\n        \"max\": 111,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          35,\n          76,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-7506188e-9daa-4551-912c-c9ee41b597be\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7506188e-9daa-4551-912c-c9ee41b597be')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-7506188e-9daa-4551-912c-c9ee41b597be button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      word  size\n",
       "1334    公司   111\n",
       "8086    面試    76\n",
       "0      一下下    53\n",
       "7891    開發    53\n",
       "3160    工作    48\n",
       "6156    系統    45\n",
       "1     一下全部    43\n",
       "3924    技術    42\n",
       "3193   工程師    35\n",
       "2023    台灣    34"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    # 從每篇文章挑選出tf-idf最大的前十個詞\n",
    "    toptens.groupby(\"doc_id\").apply(lambda x : x.nlargest(10, \"tfidf\")).reset_index(drop=True)\n",
    "\n",
    "    # 計算每個詞被選中的次數\n",
    "    .groupby(['word'],as_index=False).size()\n",
    "\n",
    ").sort_values('size', ascending=False).head(10) # 排序看前十名\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4RJlXPXijXtH"
   },
   "source": [
    "以上結果可以發現討論的焦點都是軟體工程師、工作相關的字眼，如 面試、工作、程式、系統開發等等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0uLsLgv2SP-4"
   },
   "source": [
    "## 4. 透過結巴斷詞與N-gram幫助建立斷詞字典\n",
    "N-gram 指文本中連續出現的n個語詞。   \n",
    "透過N-gram我們可以找出有哪些詞彙較常一起出現，檢查是否需要加入自定義字典中。   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q7jIuXKWSP-4"
   },
   "source": [
    "### 4.1 Bigram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1744873947020,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "VxVbMLCjSP-4"
   },
   "outputs": [],
   "source": [
    "# 設定 ngram 斷詞 function\n",
    "def ngram_getToken(row, n):\n",
    "    # 進行斷詞\n",
    "    seg_list = jieba.lcut(row)\n",
    "\n",
    "    # 篩選掉停用字與字元數小於1的詞彙\n",
    "    seg_list = [w for w in seg_list if w not in stopWords and len(w)>1]\n",
    "\n",
    "    # ngram斷詞\n",
    "    seg_list = ngrams(seg_list, n)\n",
    "    seg_list = [\" \".join(w) for w in list(seg_list)]\n",
    "\n",
    "    return seg_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "executionInfo": {
     "elapsed": 26818,
     "status": "ok",
     "timestamp": 1744873973839,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "E3MVfMdySP-5",
    "outputId": "8bed0c03-533f-4bcc-f2f7-6fb3e07a9276"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "variable_name": "softjob_bigram"
      },
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       "      <th></th>\n",
       "      <th>system_id</th>\n",
       "      <th>artUrl</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...</td>\n",
       "      <td>[分享]系統設計:如何取消正在執行的工作任務</td>\n",
       "      <td>2023-01-01 09:39:03</td>\n",
       "      <td>文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...</td>\n",
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       "      <td>2023-01-01 09:39:03</td>\n",
       "      <td>文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...</td>\n",
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       "    .colab-df-container {\n",
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       "      gap: 12px;\n",
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       "\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
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       "        document.querySelector('#df-58bd472b-1381-4a70-ba25-7fe70076d011 button.colab-df-convert');\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
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       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
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       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
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       "      --disabled-fill-color: #666;\n",
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       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
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       "    height: 32px;\n",
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       "\n",
       "  .colab-df-quickchart:hover {\n",
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       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
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       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
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       "    animation:\n",
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       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
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       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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       "    (() => {\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   system_id                                             artUrl  \\\n",
       "0          1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "0          1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "0          1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "\n",
       "                 artTitle              artDate  \\\n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "\n",
       "                                          artContent  \\\n",
       "0  文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "0  文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "0  文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "\n",
       "                                            sentence   word  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...  文字 教學  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...  教學 教學  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...  教學 影片  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softjob_bigram = MetaData.copy()\n",
    "\n",
    "softjob_bigram[\"word\"] = softjob_bigram['sentence'].apply(lambda row: ngram_getToken(row, 2))\n",
    "softjob_bigram = softjob_bigram.explode('word')\n",
    "\n",
    "softjob_bigram.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QUzdtPI6SP-5"
   },
   "source": [
    "#### 統計最常出現的bigram組合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
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     "height": 363
    },
    "executionInfo": {
     "elapsed": 49,
     "status": "ok",
     "timestamp": 1744873973973,
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      "userId": "07722237456953242478"
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    },
    "id": "l91I9F5tSP-5",
    "outputId": "83d84b92-0299-47ec-d6c7-595a75c8c25d"
   },
   "outputs": [
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       "\n",
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      ],
      "text/plain": [
       "      word  count\n",
       "0    軟體 開發     83\n",
       "1    自備 工具     82\n",
       "2    員工 自備     81\n",
       "3   加班費 制度     79\n",
       "5  保證 最低年薪     72\n",
       "4    能力 經歷     72\n",
       "7  徵才 聯絡方式     71\n",
       "6    薪資 保證     71\n",
       "9  最低年薪 必填     68\n",
       "8    必填 項目     68"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 計算每個組合出現的次數\n",
    "softjob_bigram_count = softjob_bigram['word'].value_counts().reset_index()\n",
    "\n",
    "softjob_bigram_count.sort_values('count', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
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     "height": 1000
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    "executionInfo": {
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    "id": "BbtKvgjxOJI1",
    "outputId": "6dd1847b-64c7-4caf-9f21-8c7f6e4a6d9c"
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   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>必填 項目</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>每日 工作時間</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>職缺 能力</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>工作 福利</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>接案 公司</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>計算 方式</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>詳細 至號</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>公司名稱 統編</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>填寫 詳細</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>公司 介紹</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>薪資 月薪</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>每周 工作時間</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>註冊 可免</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>中華民國 註冊</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>統編 中華民國</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>年終獎金 計算</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>人資 徵才</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>工作環境 職缺</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>工具 薪資</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>職缺 團隊</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>團隊 介紹</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>公司地址 填寫</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>公司 分紅</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>資深 工程師</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>超過 小時</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>分紅 獎金</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>公司 公司</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>項目 年終獎金</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>面試 過程</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>公司 面試</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>面試 流程</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>面試 面試</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>第一份 工作</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>前端 工程師</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>新創 公司</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>相關 經驗</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>全薪 計算</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>邀約 面試</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>工時 每日</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>系統 設計</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>開發 工程師</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>中午 休息</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>這件 事情</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>科技 公司</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>系統 開發</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>程式 設計師</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>工作時間 加班費</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>技術 面試</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>開發 流程</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>公司 工作</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>內部 系統</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4eafee63-c143-47c2-b744-36d4b8471eae')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-4eafee63-c143-47c2-b744-36d4b8471eae button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-4eafee63-c143-47c2-b744-36d4b8471eae');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-b4cc4025-84b3-476e-afda-1a8443e88d5f\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b4cc4025-84b3-476e-afda-1a8443e88d5f')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-b4cc4025-84b3-476e-afda-1a8443e88d5f button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "        word  count\n",
       "0      軟體 開發     83\n",
       "1      自備 工具     82\n",
       "2      員工 自備     81\n",
       "3     加班費 制度     79\n",
       "5    保證 最低年薪     72\n",
       "4      能力 經歷     72\n",
       "7    徵才 聯絡方式     71\n",
       "6      薪資 保證     71\n",
       "9    最低年薪 必填     68\n",
       "8      必填 項目     68\n",
       "10   每日 工作時間     66\n",
       "11     職缺 能力     64\n",
       "12     工作 福利     62\n",
       "14     接案 公司     60\n",
       "13     計算 方式     60\n",
       "17     詳細 至號     59\n",
       "15   公司名稱 統編     59\n",
       "16     填寫 詳細     59\n",
       "18     公司 介紹     58\n",
       "19     薪資 月薪     57\n",
       "20   每周 工作時間     57\n",
       "21     註冊 可免     56\n",
       "22   中華民國 註冊     56\n",
       "23   統編 中華民國     56\n",
       "24   年終獎金 計算     56\n",
       "25     人資 徵才     55\n",
       "30   工作環境 職缺     54\n",
       "29     工具 薪資     54\n",
       "28     職缺 團隊     54\n",
       "27     團隊 介紹     54\n",
       "26   公司地址 填寫     54\n",
       "31     公司 分紅     50\n",
       "32    資深 工程師     50\n",
       "33     超過 小時     49\n",
       "34     分紅 獎金     49\n",
       "36     公司 公司     46\n",
       "35   項目 年終獎金     46\n",
       "37     面試 過程     44\n",
       "38     公司 面試     43\n",
       "39     面試 流程     41\n",
       "40     面試 面試     41\n",
       "41    第一份 工作     38\n",
       "42    前端 工程師     38\n",
       "43     新創 公司     37\n",
       "44     相關 經驗     37\n",
       "45     全薪 計算     37\n",
       "46     邀約 面試     36\n",
       "47     工時 每日     36\n",
       "48     系統 設計     35\n",
       "49    開發 工程師     34\n",
       "50     中午 休息     34\n",
       "51     這件 事情     34\n",
       "53     科技 公司     33\n",
       "54     系統 開發     33\n",
       "52    程式 設計師     33\n",
       "55  工作時間 加班費     32\n",
       "56     技術 面試     32\n",
       "57     開發 流程     32\n",
       "58     公司 工作     32\n",
       "59     內部 系統     31"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softjob_bigram_count.sort_values('count', ascending=False).head(60) # 方便後續做lexicon"
   ]
  },
  {
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    "id": "EkYUixs7SP-5"
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   "source": [
    "### 4.2 Trigram"
   ]
  },
  {
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     "elapsed": 27967,
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    "id": "1lSjqdV6SP-5",
    "outputId": "6e52bf30-02e4-4a36-cb25-5f9abd1bf448"
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   "outputs": [
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      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softjob_trigram = MetaData.copy()\n",
    "\n",
    "softjob_trigram[\"word\"] = softjob_trigram.sentence.apply(lambda row: ngram_getToken(row, 3))\n",
    "softjob_trigram = softjob_trigram.explode('word')\n",
    "\n",
    "softjob_trigram.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dA69JQDZSP-5"
   },
   "source": [
    "#### 統計最常出現的trigram組合"
   ]
  },
  {
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       "  </svg>\n",
       "    </button>\n",
       "\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "      padding: 0 0 0 0;\n",
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       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-9aeb8a2c-b447-48cd-834f-a2f2c67765e2 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-9aeb8a2c-b447-48cd-834f-a2f2c67765e2');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
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       "\n",
       "\n",
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       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-50f7ed65-dd5e-40f7-b621-4ae1bc745e2e')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
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       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-50f7ed65-dd5e-40f7-b621-4ae1bc745e2e button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "           word  count\n",
       "0      員工 自備 工具     81\n",
       "1    薪資 保證 最低年薪     71\n",
       "2    最低年薪 必填 項目     68\n",
       "3    保證 最低年薪 必填     68\n",
       "4      職缺 能力 經歷     64\n",
       "5      填寫 詳細 至號     59\n",
       "6    統編 中華民國 註冊     56\n",
       "7    中華民國 註冊 可免     56\n",
       "8    年終獎金 計算 方式     56\n",
       "9  公司名稱 統編 中華民國     56"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 計算每個組合出現的次數\n",
    "softjob_trigram_count = softjob_trigram['word'].value_counts().reset_index()\n",
    "\n",
    "softjob_trigram_count.sort_values('count', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 49,
     "status": "ok",
     "timestamp": 1744874002191,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "nFQLfQliMt3T",
    "outputId": "4f6363c8-e176-48d0-f613-bd8a4226bc13"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"softjob_trigram_count\",\n  \"rows\": 40,\n  \"fields\": [\n    {\n      \"column\": \"word\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"\\u5de5\\u4f5c\\u6642\\u9593 \\u52a0\\u73ed\\u8cbb \\u5236\\u5ea6\",\n          \"\\u5fc5\\u586b \\u9805\\u76ee \\u5e74\\u7d42\\u734e\\u91d1\",\n          \"\\u516c\\u53f8 \\u5206\\u7d05 \\u734e\\u91d1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 22,\n        \"max\": 81,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          81,\n          56,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-a833cdd0-78e0-48f7-9948-dfdc3008bc26\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>員工 自備 工具</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>薪資 保證 最低年薪</td>\n",
       "      <td>71</td>\n",
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       "      <td>最低年薪 必填 項目</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>保證 最低年薪 必填</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>職缺 能力 經歷</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>填寫 詳細 至號</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>統編 中華民國 註冊</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>中華民國 註冊 可免</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>年終獎金 計算 方式</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>公司名稱 統編 中華民國</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>人資 徵才 聯絡方式</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>公司地址 填寫 詳細</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>工作環境 職缺 團隊</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>自備 工具 薪資</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>職缺 團隊 介紹</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>公司 分紅 獎金</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>必填 項目 年終獎金</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>項目 年終獎金 計算</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>工時 每日 工作時間</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>工作時間 加班費 制度</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>工具 薪資 月薪</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>加班費 制度 勞基法</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>每周 工作時間 加班費</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>中午 休息 每周</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>休息 每周 工作時間</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>平日 小時 工資額</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>小時 以內 平日</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>以內 平日 小時</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>小時 工資額 加給</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>工作時間 小時 以內</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>延長 工作時間 小時</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>工時 加班費 必填</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>八小時 中午 休息</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>加班費 必填 不填</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>必填 不填 刪文</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>每日 工作時間 八小時</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>不填 刪文 水桶</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>刪文 水桶 工時</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>工作時間 八小時 中午</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>計算 方式 全薪</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "  .colab-df-quickchart {\n",
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       "    }\n",
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       "\n",
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       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "      quickchartButtonEl.style.display =\n",
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       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "            word  count\n",
       "0       員工 自備 工具     81\n",
       "1     薪資 保證 最低年薪     71\n",
       "2     最低年薪 必填 項目     68\n",
       "3     保證 最低年薪 必填     68\n",
       "4       職缺 能力 經歷     64\n",
       "5       填寫 詳細 至號     59\n",
       "6     統編 中華民國 註冊     56\n",
       "7     中華民國 註冊 可免     56\n",
       "8     年終獎金 計算 方式     56\n",
       "9   公司名稱 統編 中華民國     56\n",
       "10    人資 徵才 聯絡方式     55\n",
       "11    公司地址 填寫 詳細     54\n",
       "12    工作環境 職缺 團隊     54\n",
       "13      自備 工具 薪資     54\n",
       "14      職缺 團隊 介紹     54\n",
       "15      公司 分紅 獎金     49\n",
       "16    必填 項目 年終獎金     46\n",
       "17    項目 年終獎金 計算     45\n",
       "18    工時 每日 工作時間     35\n",
       "19   工作時間 加班費 制度     31\n",
       "20      工具 薪資 月薪     29\n",
       "21    加班費 制度 勞基法     28\n",
       "22   每周 工作時間 加班費     27\n",
       "24      中午 休息 每周     27\n",
       "23    休息 每周 工作時間     27\n",
       "28     平日 小時 工資額     24\n",
       "30      小時 以內 平日     24\n",
       "29      以內 平日 小時     24\n",
       "27     小時 工資額 加給     24\n",
       "26    工作時間 小時 以內     24\n",
       "25    延長 工作時間 小時     24\n",
       "31     工時 加班費 必填     23\n",
       "32     八小時 中午 休息     23\n",
       "33     加班費 必填 不填     23\n",
       "34      必填 不填 刪文     23\n",
       "35   每日 工作時間 八小時     23\n",
       "36      不填 刪文 水桶     23\n",
       "37      刪文 水桶 工時     22\n",
       "38   工作時間 八小時 中午     22\n",
       "39      計算 方式 全薪     22"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softjob_trigram_count.sort_values('count', ascending=False).head(40) # 方便後續做lexicon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Q_4g8VvSP-5"
   },
   "source": [
    "從上面的 bigram 和 trigram 的結果中，我們發現\"餐廳 名稱\"、\"服務 人員\"等詞可以組合在一起，所以我們增加自定義字典來幫助斷詞能更準確。  \n",
    "我們將詞彙整理好存在 dict 文件夾中的 buffet_lexicon.txt 中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5uA6cnIoSP-5"
   },
   "source": [
    "### 4.3 使用自建字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1744874002192,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "pjnYzoWsSP-5"
   },
   "outputs": [],
   "source": [
    "# 新增軟體工作相關自定義字典\n",
    "jieba.load_userdict('./dict/softjob_lexicon.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aBy0u6heSP-5"
   },
   "source": [
    "**使用自建辭典進行斷詞與計算**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "executionInfo": {
     "elapsed": 26840,
     "status": "ok",
     "timestamp": 1744874029046,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "wDUv_udlSP-5",
    "outputId": "d34de337-93f5-4891-f451-15f07da444a1"
   },
   "outputs": [
    {
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       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "    30% {\n",
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       "      border-top-color: var(--fill-color);\n",
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       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
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       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   system_id                                             artUrl  \\\n",
       "0          1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "0          1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
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       "\n",
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       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
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       "\n",
       "                                          artContent  \\\n",
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       "0  文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "\n",
       "                                            sentence word  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   文字  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   教學  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   教學  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 剛才的斷詞結果沒有使用新增的辭典，因此我們重新進行斷詞\n",
    "data2 = MetaData.copy()\n",
    "data2['word'] = data2.sentence.apply(getToken)\n",
    "data2 = data2.explode('word')\n",
    "\n",
    "data2.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QyPZ8xtwSP-5"
   },
   "source": [
    "### 4.4 Bigram視覺化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AlVXRd2sSP-5"
   },
   "source": [
    "更新斷詞字典後重新進行bigram斷詞"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "executionInfo": {
     "elapsed": 26843,
     "status": "ok",
     "timestamp": 1744874055888,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "wFAO3qS7SP-5",
    "outputId": "d6e68426-0d04-4594-be17-d7433561e091"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "variable_name": "bigramfdist"
      },
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       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...</td>\n",
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       "      <td>2023-01-01 09:39:03</td>\n",
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       "      <td>2023-01-01 09:39:03</td>\n",
       "      <td>文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...</td>\n",
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       "    <div class=\"colab-df-buttons\">\n",
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       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a962231c-593c-4683-bed9-b49e3d8cfbf5')\"\n",
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       "\n",
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       "      border-radius: 50%;\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
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       "        document.querySelector('#df-a962231c-593c-4683-bed9-b49e3d8cfbf5 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-a962231c-593c-4683-bed9-b49e3d8cfbf5');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "\n",
       "\n",
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       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
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       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
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       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
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       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
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       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
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       "    fill: var(--button-hover-fill-color);\n",
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       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "      border-top-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-e49f9433-59bb-4180-8e39-3ea33b73cf21 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   system_id                                             artUrl  \\\n",
       "0          1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "0          1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
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       "\n",
       "                 artTitle              artDate  \\\n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "\n",
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       "0  文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "\n",
       "                                            sentence   word  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...  文字 教學  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...  教學 教學  \n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...  教學 影片  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigramfdist = MetaData.copy()\n",
    "\n",
    "bigramfdist[\"word\"] = bigramfdist['sentence'].apply(lambda row: ngram_getToken(row, 2))\n",
    "bigramfdist = bigramfdist.explode('word')\n",
    "\n",
    "bigramfdist.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 186,
     "status": "ok",
     "timestamp": 1744874056073,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "S_OmN8tGSP-5",
    "outputId": "037d2c5d-5fa1-4fb2-87b3-3a93f03453ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('軟體', '開發'), 83),\n",
       " (('自備', '工具'), 82),\n",
       " (('員工', '自備'), 81),\n",
       " (('加班費', '制度'), 79),\n",
       " (('能力', '經歷'), 72)]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigramfdist['word'] = bigramfdist['word'].astype(str)\n",
    "\n",
    "# 使用FreqDist 取得 bigram 斷詞 與 bigram 出現頻率\n",
    "bigramfdist = FreqDist(bigramfdist['word'].apply(lambda x: tuple(x.split(' '))))\n",
    "bigramfdist.most_common(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lLG79qMDSP-5"
   },
   "source": [
    "針對重新斷詞後的bigram出現頻率最高的前50對進行視覺化，觀察文章的關鍵詞對"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1744874056107,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "bMiMS0neSP-6"
   },
   "outputs": [],
   "source": [
    "# 建立bigram和count的dictionary\n",
    "# 這裡取最多的前50項\n",
    "d = {k:v for k,v in bigramfdist.most_common(50)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1744874056108,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "8dAFFEiASP-6"
   },
   "outputs": [],
   "source": [
    "# Create network plot\n",
    "G = nx.Graph()\n",
    "\n",
    "# 建立 nodes 間的連結\n",
    "for k, v in [d][0].items():\n",
    "    G.add_edge(k[0], k[1], weight=v) # nodes：詞彙，weight：組合出現頻率\n",
    "\n",
    "# 取得調整edge權重\n",
    "weights = [w[2]['weight']*0.01 for w in  G.edges(data=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 807
    },
    "executionInfo": {
     "elapsed": 411,
     "status": "ok",
     "timestamp": 1744874056518,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "emEAjGAuSP-6",
    "outputId": "9735bdf5-ce67-4303-8cd1-d867b7d90af2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "\n",
    "pos = nx.spring_layout(G, k=1.5)\n",
    "\n",
    "# networks\n",
    "nx.draw_networkx(G, pos,\n",
    "                 font_size=16,\n",
    "                 width=weights,\n",
    "                 edge_color='grey',\n",
    "                 node_color='purple',\n",
    "                 with_labels = False,\n",
    "                 ax=ax)\n",
    "\n",
    "# 增加 labels\n",
    "for key, value in pos.items():\n",
    "    x, y = value[0]+.07, value[1]+.045\n",
    "    ax.text(x, y,\n",
    "            s=key,\n",
    "            bbox=dict(facecolor='red', alpha=0.25),\n",
    "            horizontalalignment='center', fontsize=13)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ylIz717MUw6K"
   },
   "source": [
    "1. 左邊\n",
    "* 是偏向「技術工作內容」的群組\n",
    "  * 例如：工程師、團隊、工具、模型、前端、語言\n",
    "\n",
    "2. 右下\n",
    "* 這群跟「面試與薪資條件」有關\n",
    "  * 例如：經驗、制度、福利\n",
    "\n",
    "3. 右上\n",
    "* 是「招募與行政」相關\n",
    "  * 例如：人資、聯絡方式、制度、加班費、微才\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XC_MXIpRSP-6"
   },
   "source": [
    "## 5. Pairwise correlation\n",
    "計算兩個詞彙間的相關性 Pearson correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 26802,
     "status": "ok",
     "timestamp": 1744874083321,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "1PHy-RZxSP-6",
    "outputId": "60e81a67-9011-47ad-a5b7-f2f3dc0a51ec"
   },
   "outputs": [
    {
     "data": {
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       "type": "dataframe",
       "variable_name": "data_cor"
      },
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...</td>\n",
       "      <td>[分享]系統設計:如何取消正在執行的工作任務</td>\n",
       "      <td>2023-01-01 09:39:03</td>\n",
       "      <td>文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...</td>\n",
       "      <td>文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...</td>\n",
       "      <td>文字 教學 教學 影片 範例 程式 系統 架構圖 本篇 取消 執行 工作 系統 內有 資源 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672559293.A...</td>\n",
       "      <td>[請益]北漂Offer金融vs假外商</td>\n",
       "      <td>2023-01-01 15:48:11</td>\n",
       "      <td>背景：\\n  私立資工學士\\n  軟體經驗5Y，後端為主，可以支援前端/CICD\\n\\n  ...</td>\n",
       "      <td>背景私立資工學士軟體經驗後端為主可以支援前端是碩士價廢牡蠣假外商單位產險體系資融金融顧問部分...</td>\n",
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       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
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       "        console.error('Error during call to suggestCharts:', error);\n",
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       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
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       "</div>\n",
       "\n",
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      ],
      "text/plain": [
       "   system_id                                             artUrl  \\\n",
       "0          1  https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...   \n",
       "1          2  https://www.ptt.cc/bbs/Soft_Job/M.1672559293.A...   \n",
       "2          3  https://www.ptt.cc/bbs/Soft_Job/M.1672571470.A...   \n",
       "\n",
       "                 artTitle              artDate  \\\n",
       "0  [分享]系統設計:如何取消正在執行的工作任務  2023-01-01 09:39:03   \n",
       "1      [請益]北漂Offer金融vs假外商  2023-01-01 15:48:11   \n",
       "2   [請益]有人的公司也沒有提供API文件的嗎  2023-01-01 19:11:08   \n",
       "\n",
       "                                          artContent  \\\n",
       "0  文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...   \n",
       "1  背景：\\n  私立資工學士\\n  軟體經驗5Y，後端為主，可以支援前端/CICD\\n\\n  ...   \n",
       "2  安安\\n\\n小弟剛轉前端，進到一家接案公司寫網頁，工作大概9成都在接API，\\n但公司內部沒...   \n",
       "\n",
       "                                            sentence  \\\n",
       "0  文字教學教學影片範例程式系統架構圖本篇來聊聊如何取消正在執行的工作任務當系統內有需要處理比較...   \n",
       "1  背景私立資工學士軟體經驗後端為主可以支援前端是碩士價廢牡蠣假外商單位產險體系資融金融顧問部分...   \n",
       "2  安安小弟剛轉前端進到一家接案公司寫網頁工作大概成都在接但公司內部沒有提供規格文件讓我參考導致...   \n",
       "\n",
       "                                                word  \n",
       "0  文字 教學 教學 影片 範例 程式 系統 架構圖 本篇 取消 執行 工作 系統 內有 資源 ...  \n",
       "1  背景 私立 資工 學士 軟體 經驗 支援 前端 碩士 價廢 牡蠣 外商 單位 產險 體系 資...  \n",
       "2  小弟 剛轉 前端 一家 接案 公司 網頁 工作 成都 公司 內部 提供 規格 文件 參考 導...  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cor = MetaData.copy()\n",
    "\n",
    "# 需要改成使用空格連接斷好的詞\n",
    "data_cor['word'] = data_cor.sentence.apply(getToken).map(' '.join)\n",
    "data_cor.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 530,
     "status": "ok",
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    "id": "8njArUXYSP-6",
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       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "          + ' to learn more about interactive tables.';\n",
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       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "      border-right-color: var(--fill-color);\n",
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       "    60% {\n",
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       "      border-right-color: var(--fill-color);\n",
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       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
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       "        console.error('Error during call to suggestCharts:', error);\n",
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       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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       "        display: none;\n",
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       "\n",
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      "text/plain": [
       "      一位  一個月  一堆  一家  一年  三個  上班  上課  下班  不好  ...  雲端  電腦  需求  面試  面試官  項目  \\\n",
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       "\n",
       "[1547 rows x 300 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bag of Word\n",
    "# 篩選至少出現在5篇文章以上且詞頻前300的詞彙\n",
    "vectorizer = CountVectorizer(min_df = 5, max_features = 300)\n",
    "X = vectorizer.fit_transform(data_cor[\"word\"])\n",
    "vocabulary = vectorizer.get_feature_names_out()\n",
    "\n",
    "# 轉成dataframe\n",
    "DTM_df = pd.DataFrame(columns = vocabulary, data = X.toarray())\n",
    "DTM_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {
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     "height": 565
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1744874083852,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "QMl_tQQ_SP-6",
    "outputId": "294a71ea-8025-47ab-d09c-7412c07a6622"
   },
   "outputs": [
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      ],
      "text/plain": [
       "    word1        一位       一個月            一堆        一家        一年        三個  \\\n",
       "0      一位  1.000000  0.159846 -1.765706e-03  0.104942  0.033851  0.099022   \n",
       "1     一個月  0.159846  1.000000  3.571678e-02  0.127704  0.145364  0.224112   \n",
       "2      一堆 -0.001766  0.035717  1.000000e+00  0.037403  0.026721  0.028623   \n",
       "3      一家  0.104942  0.127704  3.740258e-02  1.000000  0.093270  0.121934   \n",
       "4      一年  0.033851  0.145364  2.672114e-02  0.093270  1.000000  0.252865   \n",
       "..    ...       ...       ...           ...       ...       ...       ...   \n",
       "295    項目 -0.012056  0.029049 -6.230745e-02  0.001990  0.041406  0.004227   \n",
       "296    領域  0.028774  0.013733  3.785825e-02  0.068545  0.057836  0.086005   \n",
       "297    題目  0.270608  0.185146  1.890225e-02  0.086312  0.078071  0.153779   \n",
       "298    類似  0.177241  0.127701  1.607578e-01  0.083040  0.168020  0.140712   \n",
       "299    體驗  0.064487  0.099476 -1.053095e-16  0.057018  0.241222  0.145803   \n",
       "\n",
       "           上班        上課        下班  ...        雲端        電腦        需求  \\\n",
       "0    0.032960 -0.014102 -0.008807  ...  0.065139  0.051655  0.199537   \n",
       "1    0.049390  0.011228  0.071302  ... -0.025550  0.079238  0.111684   \n",
       "2   -0.019040  0.038307  0.067070  ... -0.033117  0.004233  0.074356   \n",
       "3    0.033317 -0.013980 -0.005988  ...  0.021367  0.026858  0.067133   \n",
       "4    0.113868  0.026349  0.157107  ...  0.053547  0.035168  0.061017   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "295  0.092823 -0.014001  0.000815  ...  0.032305  0.043758  0.087745   \n",
       "296  0.038893  0.008542  0.054025  ...  0.095111  0.028211  0.064299   \n",
       "297 -0.004477  0.013957  0.007473  ...  0.039196  0.075840  0.209068   \n",
       "298  0.038829  0.004475  0.070476  ...  0.036193  0.058158  0.116715   \n",
       "299  0.017102 -0.012292  0.045976  ...  0.090822  0.044098  0.104110   \n",
       "\n",
       "           面試       面試官        項目        領域        題目        類似            體驗  \n",
       "0    0.415864  0.334704 -0.012056  0.028774  0.270608  0.177241  6.448712e-02  \n",
       "1    0.171787  0.112428  0.029049  0.013733  0.185146  0.127701  9.947623e-02  \n",
       "2    0.009608  0.003247 -0.062307  0.037858  0.018902  0.160758 -1.053095e-16  \n",
       "3    0.153141  0.071573  0.001990  0.068545  0.086312  0.083040  5.701817e-02  \n",
       "4    0.123327  0.043871  0.041406  0.057836  0.078071  0.168020  2.412222e-01  \n",
       "..        ...       ...       ...       ...       ...       ...           ...  \n",
       "295  0.005791 -0.019543  1.000000  0.060876  0.041924  0.030155  1.582617e-02  \n",
       "296  0.083822  0.068510  0.060876  1.000000  0.046278  0.159528  8.207269e-02  \n",
       "297  0.478958  0.447179  0.041924  0.046278  1.000000  0.227189  1.148807e-01  \n",
       "298  0.126621  0.067306  0.030155  0.159528  0.227189  1.000000  7.717720e-03  \n",
       "299  0.283397  0.209098  0.015826  0.082073  0.114881  0.007718  1.000000e+00  \n",
       "\n",
       "[300 rows x 301 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 計算詞之間的相關係數\n",
    "corr_matrix = np.corrcoef(DTM_df.T)\n",
    "\n",
    "# 轉成dataframe\n",
    "Cor_df = pd.DataFrame(corr_matrix, index = DTM_df.columns, columns = DTM_df.columns)\n",
    "\n",
    "Cor_df.insert(0, 'word1', Cor_df.columns)\n",
    "Cor_df.reset_index(inplace = True, drop = True)\n",
    "\n",
    "Cor_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1744874083865,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "9bvMZtgKSP-6",
    "outputId": "96470fd9-21a7-44d3-8885-f04330fe183f"
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   "outputs": [
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-ef4247d8-cde6-43f3-8623-8ef163b65c2f button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-ef4247d8-cde6-43f3-8623-8ef163b65c2f');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-5f5661e3-a746-4adc-8c4e-cc85ecec0972\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5f5661e3-a746-4adc-8c4e-cc85ecec0972')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-5f5661e3-a746-4adc-8c4e-cc85ecec0972 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      word1 word2       cor\n",
       "50793  工作時間    每日  0.911766\n",
       "28069    每日  工作時間  0.911766\n",
       "27947   加班費  工作時間  0.857897\n",
       "14193  工作時間   加班費  0.857897\n",
       "74313    結束    詢問  0.812984\n",
       "64147    詢問    結束  0.812984\n",
       "50747   加班費    每日  0.800851\n",
       "14269    每日   加班費  0.800851\n",
       "10725  聯絡方式  公司名稱  0.781778\n",
       "67535  公司名稱  聯絡方式  0.781778"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_cor_df = Cor_df.melt(id_vars = 'word1', var_name = 'word2', value_name = 'cor')\n",
    "\n",
    "# 去除兩個詞相同的情況\n",
    "word_cor_df = word_cor_df[word_cor_df[\"word1\"] != word_cor_df[\"word2\"]]\n",
    "\n",
    "word_cor_df.sort_values('cor', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vBzltJkLSP-6"
   },
   "source": [
    "### 5.1 和 「語言」, 「工時」 相關性最高的 10 個詞彙"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "executionInfo": {
     "elapsed": 28,
     "status": "ok",
     "timestamp": 1744874083894,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "Fsy0v9tkSP-6"
   },
   "outputs": [],
   "source": [
    "language_sum = word_cor_df[(word_cor_df[\"word1\"] == \"語言\")].sort_values(by = ['cor'], ascending = False).head(10)\n",
    "workinghours_sum = word_cor_df[(word_cor_df[\"word1\"] == \"工時\")].sort_values(by = ['cor'], ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 723
    },
    "executionInfo": {
     "elapsed": 515,
     "status": "ok",
     "timestamp": 1744874084410,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "o3jPA_vCSP-6",
    "outputId": "96c99e1f-bca0-4433-e9d0-33b63a6f503d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))   # 顯示圖框架大小 (寬,高)\n",
    "plt.style.use(\"ggplot\")     # 使用ggplot主題樣式\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.title('語言')\n",
    "plt.xlabel('cor')\n",
    "plt.barh(language_sum['word2'],language_sum['cor'])\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title('工時')\n",
    "plt.xlabel('cor')\n",
    "plt.barh(workinghours_sum['word2'],workinghours_sum['cor'],color=\"darkblue\")\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x4Ol8q5napP9"
   },
   "source": [
    "### ”語言“ 相關詞（技術層面）\n",
    "* 「語言」相關的詞包括：\n",
    "模型、訓練、資料、開源等\n",
    "\n",
    "* 可以發現談論方向的是偏向：\n",
    "  * AI/機器學習領域的應用，像是大型語言模型、開源訓練資料等\n",
    "  * 不只是程式語言，也跟深度學習與資料科學高度關聯\n",
    "\n",
    "### ”工時“ 相關詞（制度與職場文化）\n",
    "* 「工時」相關的詞包括：\n",
    "必填、加班費、制度、勞基法\n",
    "\n",
    "* 可以發現談論方向的是偏向：\n",
    "  * 公司規章制度、合約條款、招募資訊\n",
    "  * 經常被拿來探討職場條件與法律保障"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4L8Pu3KESP-6"
   },
   "source": [
    "### 5.2 使用詞彙關係圖畫出以詞頻前60為節點且相關性高於0.3的組合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1744874084411,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "atC1wIPHSP-6"
   },
   "outputs": [],
   "source": [
    "# 透過DTM找出詞頻前60高的詞彙\n",
    "most_freq_df = DTM_df.sum().sort_values(ascending=False).head(60).reset_index().rename(columns={'index':'word', 0:'count'})\n",
    "\n",
    "most_freq_word = most_freq_df['word'].tolist()"
   ]
  },
  {
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   "execution_count": 103,
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    "executionInfo": {
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     "timestamp": 1744874084438,
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      "userId": "07722237456953242478"
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     "user_tz": -480
    },
    "id": "P7eOdeBsSP-6",
    "outputId": "90228907-804a-4a09-a3d5-d9d0849401b6"
   },
   "outputs": [
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       "\n",
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      ],
      "text/plain": [
       "    word1 word2       cor\n",
       "0      系統    主管  0.328367\n",
       "1      面試    主管  0.318299\n",
       "2      團隊    公司  0.354824\n",
       "3      工作    公司  0.432953\n",
       "4     工程師    公司  0.304760\n",
       "..    ...   ...       ...\n",
       "203    流程    面試  0.317183\n",
       "204    簡單    面試  0.454456\n",
       "205    經歷    面試  0.526000\n",
       "206    經驗    面試  0.553045\n",
       "207    英文    面試  0.362818\n",
       "\n",
       "[208 rows x 3 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 保留存在詞頻前60高之詞彙的組合\n",
    "filtered_df = word_cor_df[(word_cor_df['word1'].isin(most_freq_word)) & (word_cor_df['word2'].isin(most_freq_word))]\n",
    "\n",
    "# 篩選出相關係數大於0.3的組合\n",
    "filtered_df = filtered_df[filtered_df['cor'] > 0.3]\n",
    "filtered_df.reset_index(inplace=True, drop=True)\n",
    "filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1744874084439,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "xzfL0i9WSP-6"
   },
   "outputs": [],
   "source": [
    "# Create network plot\n",
    "g = nx.Graph()\n",
    "\n",
    "# 建立 nodes 間的連結\n",
    "for i in range(len(filtered_df)):\n",
    "    g.add_edge(filtered_df[\"word1\"][i], filtered_df[\"word2\"][i], weight=filtered_df[\"cor\"][i])\n",
    "\n",
    "# 取得edge權重\n",
    "weights = [w[2]['weight']*5 for w in  g.edges(data=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 653
    },
    "executionInfo": {
     "elapsed": 486,
     "status": "ok",
     "timestamp": 1744874084925,
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      "displayName": "YC",
      "userId": "07722237456953242478"
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    },
    "id": "uf_ouI2ZSP-6",
    "outputId": "87e0eba0-3fb1-413b-eb15-cef0329deef5"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "pos = nx.spring_layout(g, k=0.3)\n",
    "\n",
    "# networks\n",
    "nx.draw_networkx(g, pos,\n",
    "                 font_size=16,\n",
    "                 width=weights,\n",
    "                 edge_color='grey',\n",
    "                 node_color='lightblue',\n",
    "                 with_labels = False,\n",
    "                 ax=ax)\n",
    "\n",
    "# 增加 labels\n",
    "for key, value in pos.items():\n",
    "    x, y = value[0]+.07, value[1]+.045\n",
    "    ax.text(x, y,\n",
    "            s=key,\n",
    "            bbox=dict(facecolor='red', alpha=0.25),\n",
    "            horizontalalignment='center', fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Lyewqask9bD"
   },
   "source": [
    "從圖中可以清楚看出具有高度相連關係的詞彙。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2BuC-86QSP-7"
   },
   "source": [
    "## 6. 計算文章相似度\n",
    "以TF-IDF的結果當作文章的向量，計算 Cosine Similarity 找出相似的文章   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1744874084939,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "wVOPjEWJSP-7",
    "outputId": "a0249bdd-2e2e-4fe3-84f3-a666d8d77ffd"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"data_cos\",\n  \"rows\": 1547,\n  \"fields\": [\n    {\n      \"column\": \"system_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 446,\n        \"min\": 1,\n        \"max\": 1547,\n        \"num_unique_values\": 1547,\n        \"samples\": [\n          31,\n          778,\n          1011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artUrl\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1547,\n        \"samples\": [\n          \"https://www.ptt.cc/bbs/Soft_Job/M.1673836442.A.F17.html\",\n          \"https://www.ptt.cc/bbs/Soft_Job/M.1702522526.A.5F6.html\",\n          \"https://www.ptt.cc/bbs/Soft_Job/M.1715358397.A.31F.html\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artTitle\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1154,\n        \"samples\": [\n          \"[\\u554f\\u5377]IG\\u4f7f\\u7528\\u8005\\u63a1\\u7528\\u5f37\\u5bc6\\u78bc\\u9632\\u7bc4\\u76dc\\u5e33\\u4e4b\\u7814\\u7a76\",\n          \"Re:[\\u8acb\\u76ca]\\u5de5\\u63a7\\u80cc\\u666f\\u5de5\\u4f5c\\u5341\\u5e74\\u4e0d\\u7406\\u60f3\\uff0c\\u8acb\\u6559\\u672a\\u4f86\\u51fa\\u8def\",\n          \"[\\u8a0e\\u8ad6]12\\u79d2\\u7aca\\u8d702500\\u842c\\u7f8e\\u5143\\u52a0\\u5bc6\\u8ca8\\u5e63\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artDate\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1547,\n        \"samples\": [\n          \"2023-01-16 10:34:00\",\n          \"2023-12-14 10:55:24\",\n          \"2024-05-11 00:26:32\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artContent\",\n      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       "type": "dataframe",
       "variable_name": "data_cos"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-5b114bab-6276-4d34-994c-a529070d0fc9\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>system_id</th>\n",
       "      <th>artUrl</th>\n",
       "      <th>artTitle</th>\n",
       "      <th>artDate</th>\n",
       "      <th>artContent</th>\n",
       "      <th>sentence</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>文字教學:\\nhttps://bit.ly/3jFMwvS\\n教學影片:\\nhttps://...</td>\n",
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      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cos = data_cor.copy()\n",
    "data_cos.head(3)"
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  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "executionInfo": {
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     "status": "ok",
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    "id": "P2YOFjp_SP-7",
    "outputId": "d9a2b11c-9cd8-4ae9-c569-ae6e5f13b4c0"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TfidfTransformer()\n"
     ]
    },
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       "<p>1547 rows × 300 columns</p>\n",
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       "\n",
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       "\n",
       "[1547 rows x 300 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer = TfidfTransformer()\n",
    "print(transformer)\n",
    "# 將詞頻矩陣X統計成TF-IDF值\n",
    "tfidf = transformer.fit_transform(X)\n",
    "\n",
    "# 轉成dataframe\n",
    "TFIDF_df = pd.DataFrame(columns = vocabulary, data = tfidf.toarray())\n",
    "TFIDF_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z7uZuaA2SP-7"
   },
   "source": [
    "計算文章間的cosine similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "executionInfo": {
     "elapsed": 103,
     "status": "ok",
     "timestamp": 1744874085074,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "JG7CvSb7SP-7"
   },
   "outputs": [],
   "source": [
    "cosine_matrix = cosine_similarity(tfidf.toarray(), tfidf.toarray())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MAyvKQLQSP-7"
   },
   "source": [
    "檢視與第一篇文章相似的文章"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 75,
     "status": "ok",
     "timestamp": 1744874085149,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
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    },
    "id": "6MhCf9zbSP-7",
    "outputId": "5b24fd69-9edf-4134-d2fb-08db52149576"
   },
   "outputs": [
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       "        google.colab.notebook.generateWithVariable('cos_df');\n",
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       "\n",
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      ],
      "text/plain": [
       "      cos_similarity\n",
       "0           1.000000\n",
       "1           0.053813\n",
       "2           0.046821\n",
       "3           0.012354\n",
       "4           0.047206\n",
       "...              ...\n",
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       "\n",
       "[1547 rows x 1 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos_df = pd.DataFrame(cosine_matrix[0], columns = ['cos_similarity'])\n",
    "cos_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
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     "height": 363
    },
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1744874085189,
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    },
    "id": "JECRHR0lSP-7",
    "outputId": "ab4607ee-45db-4e34-bb2b-96c8f1a2ea9a"
   },
   "outputs": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cos_similarity</th>\n",
       "      <th>artTitle</th>\n",
       "      <th>artUrl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>[分享]系統設計:如何取消正在執行的工作任務</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1156</th>\n",
       "      <td>0.569822</td>\n",
       "      <td>Re:[討論]國泰、玉山的IT部門是不是皮皮剉？</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1722141381.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>0.552698</td>\n",
       "      <td>[心得]自動更新執行中的Docker容器解決方案</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1677928855.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>0.542625</td>\n",
       "      <td>[討論]Youtube的SLA是幾個9?</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1703210676.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>0.450926</td>\n",
       "      <td>[討論]AWS帳戶被盜</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1727630526.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>0.440927</td>\n",
       "      <td>[討論]關於挖角跳槽一事。</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1677851305.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>0.428680</td>\n",
       "      <td>[討論].NETFramework跨平台是不是假議題</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1718260802.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>0.425261</td>\n",
       "      <td>Re:[討論]有可能不學coding就可以取得前後端工作?</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1703165264.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>0.418454</td>\n",
       "      <td>[新聞]百年郵局全面數位大轉型</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1701558922.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.401656</td>\n",
       "      <td>[心得]用ChatGPT幫忙整理CodeChanges</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1678703464.A...</td>\n",
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       "\n",
       "  .colab-df-quickchart {\n",
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       "\n",
       "  .colab-df-quickchart:hover {\n",
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       "\n",
       "  @keyframes spin {\n",
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       "    }\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
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       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
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       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      cos_similarity                       artTitle  \\\n",
       "0           1.000000         [分享]系統設計:如何取消正在執行的工作任務   \n",
       "1156        0.569822       Re:[討論]國泰、玉山的IT部門是不是皮皮剉？   \n",
       "133         0.552698       [心得]自動更新執行中的Docker容器解決方案   \n",
       "800         0.542625           [討論]Youtube的SLA是幾個9?   \n",
       "1272        0.450926                    [討論]AWS帳戶被盜   \n",
       "132         0.440927                  [討論]關於挖角跳槽一事。   \n",
       "1073        0.428680     [討論].NETFramework跨平台是不是假議題   \n",
       "796         0.425261  Re:[討論]有可能不學coding就可以取得前後端工作?   \n",
       "744         0.418454                [新聞]百年郵局全面數位大轉型   \n",
       "159         0.401656    [心得]用ChatGPT幫忙整理CodeChanges   \n",
       "\n",
       "                                                 artUrl  \n",
       "0     https://www.ptt.cc/bbs/Soft_Job/M.1672537150.A...  \n",
       "1156  https://www.ptt.cc/bbs/Soft_Job/M.1722141381.A...  \n",
       "133   https://www.ptt.cc/bbs/Soft_Job/M.1677928855.A...  \n",
       "800   https://www.ptt.cc/bbs/Soft_Job/M.1703210676.A...  \n",
       "1272  https://www.ptt.cc/bbs/Soft_Job/M.1727630526.A...  \n",
       "132   https://www.ptt.cc/bbs/Soft_Job/M.1677851305.A...  \n",
       "1073  https://www.ptt.cc/bbs/Soft_Job/M.1718260802.A...  \n",
       "796   https://www.ptt.cc/bbs/Soft_Job/M.1703165264.A...  \n",
       "744   https://www.ptt.cc/bbs/Soft_Job/M.1701558922.A...  \n",
       "159   https://www.ptt.cc/bbs/Soft_Job/M.1678703464.A...  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos_df = cos_df.merge(data_cos, how = 'left', left_index=True, right_index=True)\n",
    "cos_df.loc[:,[\"cos_similarity\", \"artTitle\", \"artUrl\"]].sort_values(by=['cos_similarity'], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qZ8LEiNoSP-7"
   },
   "source": [
    "檢視與第14篇文章相似的文章"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1744874085219,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "PIiSoGEsSP-7",
    "outputId": "b4636a2d-44b5-4c71-d757-0f3d0dbabde4"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"cos_df_14\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"cos_similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1762317837297578,\n        \"min\": 0.44949354350444876,\n        \"max\": 1.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.45211227845818736,\n          0.7166858582522507,\n          0.4678142988285602\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artTitle\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"[\\u8acb\\u76ca]\\u7d14\\u8edf\\u7814\\u7a76\\u6240\\u4e2d\\u8208\\u57fa\\u8cc7vs\\u5317\\u79d1\\u96fb\\u5b50\\u7532\",\n          \"[\\u8acb\\u76ca]\\u8acb\\u6559\\u95dc\\u65bc\\\"\\u7d71\\u4e00\\u8cc7\\u8a0a\\\"\",\n          \"Re:[\\u60c5\\u5831]\\u9ad8\\u96c4\\u53f0\\u5357\\u514d\\u8cbb\\u7a0b\\u5f0f\\u5165\\u9580\\u6559\\u5b78\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artUrl\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"https://www.ptt.cc/bbs/Soft_Job/M.1739165453.A.9EB.html\",\n          \"https://www.ptt.cc/bbs/Soft_Job/M.1721364214.A.54F.html\",\n          \"https://www.ptt.cc/bbs/Soft_Job/M.1743221251.A.499.html\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
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       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cos_similarity</th>\n",
       "      <th>artTitle</th>\n",
       "      <th>artUrl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>[請益]什麼語言是用ppt跟word寫還可以月入100K?</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1673243236.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134</th>\n",
       "      <td>0.716686</td>\n",
       "      <td>[請益]請教關於\"統一資訊\"</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1721364214.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>0.543876</td>\n",
       "      <td>[請益]請益關於\"統一資訊\"，謝謝</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1678755633.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>0.532129</td>\n",
       "      <td>[請益]請問Wireshark如何plotfilter數值?</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1695009578.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>0.480419</td>\n",
       "      <td>[請益]技能樹怎麼點</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1681442265.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>0.467814</td>\n",
       "      <td>Re:[情報]高雄台南免費程式入門教學</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1743221251.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0.456777</td>\n",
       "      <td>[請益]jsthedefinitiveguide3rd價值？</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1677476198.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>0.454119</td>\n",
       "      <td>Re:[請益]想學程式但數學基礎很差怎麼進步</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1709995806.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>0.452112</td>\n",
       "      <td>[請益]純軟研究所中興基資vs北科電子甲</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1739165453.A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>0.449494</td>\n",
       "      <td>[請益]請問資策會地區會有很大差別嗎？</td>\n",
       "      <td>https://www.ptt.cc/bbs/Soft_Job/M.1680458545.A...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d803cfa2-c2bb-44ac-9e33-52d915c982a6')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-d803cfa2-c2bb-44ac-9e33-52d915c982a6 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-d803cfa2-c2bb-44ac-9e33-52d915c982a6');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-0e768551-023a-4aa1-b42e-2f2edf29e0b1\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0e768551-023a-4aa1-b42e-2f2edf29e0b1')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-0e768551-023a-4aa1-b42e-2f2edf29e0b1 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      cos_similarity                        artTitle  \\\n",
       "14          1.000000   [請益]什麼語言是用ppt跟word寫還可以月入100K?   \n",
       "1134        0.716686                  [請益]請教關於\"統一資訊\"   \n",
       "163         0.543876               [請益]請益關於\"統一資訊\"，謝謝   \n",
       "629         0.532129  [請益]請問Wireshark如何plotfilter數值?   \n",
       "272         0.480419                      [請益]技能樹怎麼點   \n",
       "1544        0.467814             Re:[情報]高雄台南免費程式入門教學   \n",
       "124         0.456777  [請益]jsthedefinitiveguide3rd價值？   \n",
       "898         0.454119          Re:[請益]想學程式但數學基礎很差怎麼進步   \n",
       "1469        0.452112            [請益]純軟研究所中興基資vs北科電子甲   \n",
       "237         0.449494             [請益]請問資策會地區會有很大差別嗎？   \n",
       "\n",
       "                                                 artUrl  \n",
       "14    https://www.ptt.cc/bbs/Soft_Job/M.1673243236.A...  \n",
       "1134  https://www.ptt.cc/bbs/Soft_Job/M.1721364214.A...  \n",
       "163   https://www.ptt.cc/bbs/Soft_Job/M.1678755633.A...  \n",
       "629   https://www.ptt.cc/bbs/Soft_Job/M.1695009578.A...  \n",
       "272   https://www.ptt.cc/bbs/Soft_Job/M.1681442265.A...  \n",
       "1544  https://www.ptt.cc/bbs/Soft_Job/M.1743221251.A...  \n",
       "124   https://www.ptt.cc/bbs/Soft_Job/M.1677476198.A...  \n",
       "898   https://www.ptt.cc/bbs/Soft_Job/M.1709995806.A...  \n",
       "1469  https://www.ptt.cc/bbs/Soft_Job/M.1739165453.A...  \n",
       "237   https://www.ptt.cc/bbs/Soft_Job/M.1680458545.A...  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos_df_14 = pd.DataFrame(cosine_matrix[14], columns=['cos_similarity'])\n",
    "\n",
    "cos_df_14 = cos_df_14.merge(data_cos, how = 'left', left_index=True, right_index=True)\n",
    "cos_df_14.loc[:,[\"cos_similarity\", \"artTitle\", \"artUrl\"]].sort_values(by=['cos_similarity'], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mCRrQiSqSP-7"
   },
   "source": [
    "## 7. 補充：建立Ngram預測模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H4a4jwI5SP-8"
   },
   "source": [
    "**使用我們的抓取的 PTT 軟體工程師版資料集**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "executionInfo": {
     "elapsed": 31272,
     "status": "ok",
     "timestamp": 1744874116493,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "tpANPDlMSP-8"
   },
   "outputs": [],
   "source": [
    "data3 = MetaData.copy()\n",
    "sen_tokens = data3.sentence.apply(getToken).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1744874116498,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "SIO7rBRVSP-8"
   },
   "outputs": [],
   "source": [
    "def ngram(documents, N=2):\n",
    "    ngram_prediction = dict()\n",
    "    total_grams = list()\n",
    "    words = list()\n",
    "    Word = namedtuple('Word', ['word', 'prob'])\n",
    "\n",
    "    for doc in documents:\n",
    "        # 加上開頭和結尾 tag\n",
    "        split_words = ['<s>'] + list(doc) + ['</s>']\n",
    "        # 計算分子\n",
    "        [total_grams.append(tuple(split_words[i:i+N])) for i in range(len(split_words)-N+1)]\n",
    "        # 計算分母\n",
    "        [words.append(tuple(split_words[i:i+N-1])) for i in range(len(split_words)-N+2)]\n",
    "\n",
    "    total_word_counter = Counter(total_grams)\n",
    "    word_counter = Counter(words)\n",
    "\n",
    "    for key in total_word_counter:\n",
    "        word = ''.join(key[:N-1])\n",
    "        if word not in ngram_prediction:\n",
    "            ngram_prediction.update({word: set()})\n",
    "\n",
    "        next_word_prob = total_word_counter[key]/word_counter[key[:N-1]] #P(B|A)\n",
    "        w = Word(key[-1], '{:.3g}'.format(next_word_prob))\n",
    "        ngram_prediction[word].add(w)\n",
    "\n",
    "    return ngram_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "executionInfo": {
     "elapsed": 825,
     "status": "ok",
     "timestamp": 1744874117324,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "rMx7vh-KSP-8"
   },
   "outputs": [],
   "source": [
    "# Bigram預測模型為例\n",
    "bi_prediction = ngram(sen_tokens, N=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iwtaVzG5SP-8"
   },
   "source": [
    "**預測下一個出現的詞**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1744874117331,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "qp6AW-XpSP-8",
    "outputId": "3afbf689-c0a8-40f3-f1f3-2bbf04888ccd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next word: 工作, probability: 0.0309\n",
      "next word: </s>, probability: 0.0247\n",
      "next word: 加班, probability: 0.0185\n",
      "next word: 接觸, probability: 0.0185\n",
      "next word: 鑽研, probability: 0.0123\n"
     ]
    }
   ],
   "source": [
    "text = '喜歡'\n",
    "next_words = list(bi_prediction[text])\n",
    "next_words.sort(key = lambda s: s[1], reverse = True)\n",
    "for next_word in next_words[:5]:\n",
    "    print('next word: {}, probability: {}'.format(next_word.word, next_word.prob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1744874117339,
     "user": {
      "displayName": "YC",
      "userId": "07722237456953242478"
     },
     "user_tz": -480
    },
    "id": "PNICU6ImSP-8",
    "outputId": "d2373028-3697-45cb-9628-a98d91d25c37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next word: 建議, probability: 0.0253\n",
      "next word: 意見, probability: 0.0253\n",
      "next word: 服務, probability: 0.0228\n",
      "next word: 相關, probability: 0.0177\n",
      "next word: 完整, probability: 0.0152\n"
     ]
    }
   ],
   "source": [
    "text = '提供'\n",
    "next_words = list(bi_prediction[text])\n",
    "next_words.sort(key = lambda s: s[1], reverse = True)\n",
    "for next_word in next_words[:5]:\n",
    "    print('next word: {}, probability: {}'.format(next_word.word, next_word.prob))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **第二組讀書會專案 - 文件分類**\n",
    "課程：社群媒體分析\n",
    "\n",
    "授課教授：黃三益老師\n",
    "\n",
    "組別：Group_2\n",
    "\n",
    "組員：M124020028,何允中、M134020016,王予芙、M134020034,黃沛萱、M134020037,陳宥齊、B104020032,翁武麟、M124111057,張伶宣\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "資料來源：ptt\n",
    "\n",
    "版別：打工、軟體工作、科技工作\n",
    "\n",
    "+ 分析動機：我們想探討不同類型工作的討論重點是否有明顯的差異，特別是軟體工作及科技工作是否存在一定程度的相似性。\n",
    "\n",
    "+ 分析目標：將三個版別的文章合起來，訓練模型能預測版別，並觀察哪些詞是被模型認為屬於其中一版的\n",
    "\n",
    "+ 步驟：\n",
    "    * 載入套件、資料\n",
    "    * 基本的分類模型流程，包含以下三步驟：\n",
    "      * 前處理 (preprocess)\n",
    "      * 建模 (train model)\n",
    "      * 評估與預測 (evaluation and predict)\n",
    "    * cross validation\n",
    "    * 不同分類器的效果\n",
    "    * 分析可解釋模型的結果\n",
    "\n",
    "+ 遇到的困難和解決方式\n",
    "\n",
    "  軟體工作版的資料筆數少於其他兩個版7-8倍，同樣從2024/01/01-2025/03/31的資料，軟體工作只有700+筆，另外兩個版都有5000+，以這樣資料筆數存在極大差異下訓練出來的模型，都很明顯在軟體工作版的預測上比較糟糕，因此我們將時間延長至2020/01/01-2025/03/31，讓三個版別的資料筆數相近，訓練出的模型也更為準確。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from pprint import pprint\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import jieba\n",
    "from sklearn.model_selection import train_test_split, cross_validate, cross_val_predict, KFold\n",
    "from sklearn.metrics import (\n",
    "    confusion_matrix,\n",
    "    classification_report,\n",
    "    roc_curve,\n",
    "    auc,\n",
    "    precision_recall_curve,\n",
    "    RocCurveDisplay\n",
    ")\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2025-04-15 07:20:03--  https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_\n",
      "Resolving drive.google.com (drive.google.com)... 142.250.152.139, 142.250.152.101, 142.250.152.100, ...\n",
      "Connecting to drive.google.com (drive.google.com)|142.250.152.139|:443... connected.\n",
      "HTTP request sent, awaiting response... 303 See Other\n",
      "Location: https://drive.usercontent.google.com/download?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_ [following]\n",
      "--2025-04-15 07:20:03--  https://drive.usercontent.google.com/download?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_\n",
      "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 74.125.202.132, 2607:f8b0:4001:c06::84\n",
      "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|74.125.202.132|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 20659344 (20M) [application/octet-stream]\n",
      "Saving to: ‘TaipeiSansTCBeta-Regular.ttf’\n",
      "\n",
      "TaipeiSansTCBeta-Re 100%[===================>]  19.70M  --.-KB/s    in 0.1s    \n",
      "\n",
      "2025-04-15 07:20:06 (176 MB/s) - ‘TaipeiSansTCBeta-Regular.ttf’ saved [20659344/20659344]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 設定圖的中文字體\n",
    "!wget -O TaipeiSansTCBeta-Regular.ttf https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_&export=download\n",
    "\n",
    "import matplotlib\n",
    "\n",
    "matplotlib.font_manager.fontManager.addfont('TaipeiSansTCBeta-Regular.ttf')\n",
    "matplotlib.rc('font', family='Taipei Sans TC Beta')\n",
    "\n",
    "\n",
    "# 設定圖的中文字體 (無法顯示的話可以試試‘Microsoft JhengHei’字體)\n",
    "# 也可參考：https://pyecontech.com/2020/03/27/python_matplotlib_chinese/\n",
    "#plt.rcParams['font.sans-serif'] = ['Noto Sans CJK TC'] #使圖中中文能正常顯示\n",
    "plt.rcParams['axes.unicode_minus'] = False #使負號能夠顯示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\\\":\\u516c\\u53f8\\u5728\\u77fd\\u8c37\\u8001\\u95c6\\u548c\\u696d\\u52d9\\u90fd\\u5728\\u77fd\\u8c37$$\\u4e5f\\u662f\\u77fd\\u8c37\\u7684\\\", \\\"cmtDate\\\": \\\"2022-06-29 22:53:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"apolkingg8\\\", \\\"cmtContent\\\": \\\":\\u8001\\u5be6\\u8aaa\\u6211\\u89ba\\u5f97\\u81ea\\u5df1\\u5f37\\u8abf\\u77fd\\u8c37\\u7684\\u96f7\\u5305\\u6a5f\\u7387\\u9817\\u9ad8...\\\", \\\"cmtDate\\\": \\\"2022-06-29 23:50:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"YabukiNako\\\", \\\"cmtContent\\\": \\\":\\u9019\\u7a2e\\u53ea\\u9069\\u540835\\u4ee5\\u524d\\u6c92\\u59bb\\u5c0f\\u7684\\u5c24\\u5176\\u6709\\u5c0f\\u5b69\\u4e86\\u9084\\u662f\\u4e0d\\u8981\\\", \\\"cmtDate\\\": \\\"2022-06-30 00:11:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"fr75\\\", \\\"cmtContent\\\": \\\":\\u554f\\u4ed6\\u5011\\u6709\\u591a\\u5c11\\u8cc7\\u91d1\\u73fe\\u5728\\u5728\\u7b2c\\u5e7e\\u52df\\u8cc7\\u9084\\u53ef\\u4ee5\\u6490\\u591a\\u4e45\\\", \\\"cmtDate\\\": \\\"2022-06-30 00:56:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"fr75\\\", \\\"cmtContent\\\": \\\":\\u5de5\\u4f5c\\u662f\\u5728\\u53f0\\u7063\\u9060\\u7aef\\u55ce\\\", \\\"cmtDate\\\": \\\"2022-06-30 00:57:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"watashino\\\", \\\"cmtContent\\\": \\\":\\u8ddf\\u4e0a\\u9762\\u5927\\u5927\\u540c\\u611f\\u5f37\\u8abf\\u81ea\\u5df1\\u77fd\\u8c37\\\", \\\"cmtDate\\\": \\\"2022-06-30 01:11:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"watashino\\\", \\\"cmtContent\\\": \\\":\\u7136\\u5f8c\\u5de5\\u7a0b\\u5e2b\\u5168\\u90fd\\u627e\\u53f0\\u7063\\u7684\\\", \\\"cmtDate\\\": \\\"2022-06-30 01:11:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"watashino\\\", \\\"cmtContent\\\": 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\\\":\\u53f0\\u7063\\u8fa6\\u516c\\u5ba4/\\u9060\\u7aef\\u4e0d\\u904e\\u6211\\u5f8c\\u4f86\\u627e\\u5230\\u540c\\u50f9\\u683c\\u7684\\u53f0\\u7063\\u4e2d\\u578b\\u516c\\u53f8\\u4e86\\\", \\\"cmtDate\\\": \\\"2022-06-30 12:42:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"alden\\\", \\\"cmtContent\\\": \\\":40W\\u662f\\u7f8e\\u91d1\\u9084\\u53f0\\u5e63\\u5440\\uff1f\\\", \\\"cmtDate\\\": \\\"2022-06-30 21:46:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"BBSealion\\\", \\\"cmtContent\\\": \\\":\\u6c92\\u6709relocate\\u53bb\\u7f8e\\u570b\\u7684\\u8a71\\uff0c\\u61c9\\u8a72\\u662f\\u53f0\\u5e63\\u5427\\\", \\\"cmtDate\\\": \\\"2022-07-01 10:17:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"BBSealion\\\", \\\"cmtContent\\\": \\\":\\u96d6\\u7136\\u6211\\u7b2c\\u4e00\\u76f4\\u89ba\\u4e5f\\u662f\\u4ee5\\u70ba\\u7f8e\\u91d1XD\\\", \\\"cmtDate\\\": \\\"2022-07-01 10:17:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"Romulus\\\", \\\"cmtContent\\\": \\\":\\u53f0\\u5e63\\\", \\\"cmtDate\\\": \\\"2022-07-01 15:55:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"OyodoKai\\\", \\\"cmtContent\\\": \\\":\\u5927\\u5bb6\\u770b\\u5230\\u77fd\\u8c37\\u5c31\\u8208\\u596e\\u8d77\\u4f86\\u4e86\\u9a19\\u9a19\\u4eba\\u883b\\u6709\\u7528\\u7684\\\", \\\"cmtDate\\\": \\\"2022-07-01 18:54:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"Apache\\\", \\\"cmtContent\\\": \\\":\\u81f3\\u5c11\\u8981\\u9a19\\u5230\\u6295\\u8cc7\\u4eba\\\", \\\"cmtDate\\\": \\\"2022-07-02 00:30:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"daddy29\\\", \\\"cmtContent\\\": \\\":\\u53bb\\u5427\\u6c92\\u78b0\\u904e\\u5275\\u6295\\u6839\\u672c\\u4e0d\\u77e5\\u9053\\u80fd\\u62ff\\u591a\\u5c11\\\", \\\"cmtDate\\\": \\\"2022-07-02 00:58:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"hakkacandy\\\", \\\"cmtContent\\\": \\\":\\u73fe\\u5728\\u9019\\u7a2e\\u72c0\\u6cc1\\u53bbstartup\\u5f88\\u8ced\\u5427...\\\", \\\"cmtDate\\\": \\\"2022-07-03 09:38:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"jobintan\\\", \\\"cmtContent\\\": \\\":\\u9019\\u6642\\u671f\\u5c31\\u5118\\u91cf\\u5f80GAM\\uff08Google/Amazon/Microsoft\\uff09\\u627e\\uff0c\\u6700\\u6700\\\", \\\"cmtDate\\\": \\\"2022-07-04 10:54:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"jobintan\\\", \\\"cmtContent\\\": \\\":\\u8d77\\u78bc\\u4e5f\\u6490\\u904e\\u5bd2\\u51ac\\u5148\\uff0c\\u7b49\\u5e02\\u5834\\u8b8a\\u597d\\u5f8c\\u518d\\u8df3\\u672a\\u9072\\u3002\\\", \\\"cmtDate\\\": \\\"2022-07-04 10:55:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"Harlequin727\\\", \\\"cmtContent\\\": \\\":\\u7f8e\\u570b\\u7d93\\u6fdf\\u8870\\u9000\\u4e2d\\u5c24\\u5176\\u8edf\\u9ad4\\u696dstartup\\u73fe\\u5728\\u9019\\u6642\\u9593\\u53bb\\u98a8\\\", \\\"cmtDate\\\": \\\"2022-07-05 12:53:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"Harlequin727\\\", \\\"cmtContent\\\": \\\":\\u96aa\\u6975\\u9ad8\\u5427\\\", \\\"cmtDate\\\": \\\"2022-07-05 12:53:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"kyuudonut\\\", \\\"cmtContent\\\": \\\":\\u9084\\u662f\\u8981\\u770b\\u516c\\u53f8\\u554a\\u5982\\u679cstartup\\u662fModular\\u722c\\u8457\\u90fd\\u8981\\u9032\\u53bb\\\", \\\"cmtDate\\\": \\\"2022-07-05 15:50:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"doranako\\\", \\\"cmtContent\\\": \\\":\\u4f60\\u8981\\u8ddf\\u6211\\u5011\\u8aaa\\u5168\\u7403\\u958b\\u767c\\u5718\\u968a\\u591a\\u5c11\\u4eba\\uff1f\\u8aaa\\u4e0d\\u5b9a\\u53f0\\u7063\\u53ea\\u662f\\u4e00\\u500bt\\\", \\\"cmtDate\\\": \\\"2022-07-05 21:11:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"doranako\\\", \\\"cmtContent\\\": \\\":eam\\\", \\\"cmtDate\\\": \\\"2022-07-05 21:11:00\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"e_ip\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11977,\n        \"samples\": [\n          \"124.218.38.207\",\n          \"123.194.93.183\",\n          \"27.52.126.20\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"insertedDate\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2632,\n        \"samples\": [\n          \"2024-06-01 02:16:07\",\n          \"2024-08-02 02:13:41\",\n          \"2024-05-31 02:10:32\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dataSource\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"ptt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-3809e9dd-18ad-4beb-8f19-77f08a40783b\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>system_id</th>\n",
       "      <th>artUrl</th>\n",
       "      <th>artTitle</th>\n",
       "      <th>artDate</th>\n",
       "      <th>artPoster</th>\n",
       "      <th>artCatagory</th>\n",
       "      <th>artContent</th>\n",
       "      <th>artComment</th>\n",
       "      <th>e_ip</th>\n",
       "      <th>insertedDate</th>\n",
       "      <th>dataSource</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704076606.A...</td>\n",
       "      <td>Re:[請益]在新竹上班到底有什麼優點</td>\n",
       "      <td>2024-01-01 10:36:44</td>\n",
       "      <td>dilson</td>\n",
       "      <td>Tech_Job</td>\n",
       "      <td>:\\n\\n不見得喔\\n\\n我看過私校學店正妹在科技業的男友也是私校學店\\n\\n因為沒腦的跟有...</td>\n",
       "      <td>[{\"cmtStatus\": \"推\", \"cmtPoster\": \"k11k\", \"cmtC...</td>\n",
       "      <td>42.79.72.237</td>\n",
       "      <td>2024-01-02 02:21:08</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704078788.A...</td>\n",
       "      <td>[新聞]台積電效應！日本半導體廠開2024第1槍</td>\n",
       "      <td>2024-01-01 11:13:06</td>\n",
       "      <td>qazxc1156892</td>\n",
       "      <td>Tech_Job</td>\n",
       "      <td>新聞標題： 台積電效應！日本半導體廠開2024第1槍　宣告新進員工加薪40%\\n\\n\\n記者...</td>\n",
       "      <td>[{\"cmtStatus\": \"推\", \"cmtPoster\": \"sunnyhung\", ...</td>\n",
       "      <td>114.136.154.65</td>\n",
       "      <td>2024-01-02 02:21:08</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704080503.A...</td>\n",
       "      <td>Re:[請益]在新竹上班到底有什麼優點</td>\n",
       "      <td>2024-01-01 11:41:41</td>\n",
       "      <td>francej</td>\n",
       "      <td>Tech_Job</td>\n",
       "      <td>如果有要生小孩的 新竹大概是目前全國最適合學齡小孩成長的環境吧\\n\\n人口平均素質高 別的縣...</td>\n",
       "      <td>[{\"cmtStatus\": \"→\", \"cmtPoster\": \"SpongebobMac...</td>\n",
       "      <td>36.230.152.131</td>\n",
       "      <td>2024-01-02 02:21:08</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704100050.A...</td>\n",
       "      <td>Re:[請益]在新竹上班到底有什麼優點</td>\n",
       "      <td>2024-01-01 17:07:27</td>\n",
       "      <td>Onnnnnnnnnnn</td>\n",
       "      <td>Tech_Job</td>\n",
       "      <td>講新竹太籠統\\n\\n是新竹市 還是新竹縣 還是以前被割地的竹南？\\n\\n學區來說\\n只要在新...</td>\n",
       "      <td>[{\"cmtStatus\": \"推\", \"cmtPoster\": \"marsonele\", ...</td>\n",
       "      <td>61.230.11.219</td>\n",
       "      <td>2024-01-02 02:21:08</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704106015.A...</td>\n",
       "      <td>[新聞]「股王製造機」王雪紅不看一時成敗、拚</td>\n",
       "      <td>2024-01-01 18:46:53</td>\n",
       "      <td>GuanLaoBan</td>\n",
       "      <td>Tech_Job</td>\n",
       "      <td>https://www.nownews.com/news/6331938\\n2023年12月...</td>\n",
       "      <td>[{\"cmtStatus\": \"推\", \"cmtPoster\": \"forestsea722...</td>\n",
       "      <td>138.199.22.107</td>\n",
       "      <td>2024-01-02 02:21:10</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3809e9dd-18ad-4beb-8f19-77f08a40783b')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
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       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-3809e9dd-18ad-4beb-8f19-77f08a40783b button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-3809e9dd-18ad-4beb-8f19-77f08a40783b');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
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       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
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       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
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       "      --hover-bg-color: #434B5C;\n",
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       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
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       "    border-radius: 50%;\n",
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       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-44ca1039-eb1a-4499-be56-6396fb1df6b1 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   system_id                                             artUrl  \\\n",
       "0          1  https://www.ptt.cc/bbs/Tech_Job/M.1704076606.A...   \n",
       "1          2  https://www.ptt.cc/bbs/Tech_Job/M.1704078788.A...   \n",
       "2          3  https://www.ptt.cc/bbs/Tech_Job/M.1704080503.A...   \n",
       "3          4  https://www.ptt.cc/bbs/Tech_Job/M.1704100050.A...   \n",
       "4          5  https://www.ptt.cc/bbs/Tech_Job/M.1704106015.A...   \n",
       "\n",
       "                    artTitle              artDate     artPoster artCatagory  \\\n",
       "0        Re:[請益]在新竹上班到底有什麼優點  2024-01-01 10:36:44        dilson    Tech_Job   \n",
       "1  [新聞]台積電效應！日本半導體廠開2024第1槍　  2024-01-01 11:13:06  qazxc1156892    Tech_Job   \n",
       "2        Re:[請益]在新竹上班到底有什麼優點  2024-01-01 11:41:41       francej    Tech_Job   \n",
       "3        Re:[請益]在新竹上班到底有什麼優點  2024-01-01 17:07:27  Onnnnnnnnnnn    Tech_Job   \n",
       "4     [新聞]「股王製造機」王雪紅不看一時成敗、拚  2024-01-01 18:46:53    GuanLaoBan    Tech_Job   \n",
       "\n",
       "                                          artContent  \\\n",
       "0  :\\n\\n不見得喔\\n\\n我看過私校學店正妹在科技業的男友也是私校學店\\n\\n因為沒腦的跟有...   \n",
       "1  新聞標題： 台積電效應！日本半導體廠開2024第1槍　宣告新進員工加薪40%\\n\\n\\n記者...   \n",
       "2  如果有要生小孩的 新竹大概是目前全國最適合學齡小孩成長的環境吧\\n\\n人口平均素質高 別的縣...   \n",
       "3  講新竹太籠統\\n\\n是新竹市 還是新竹縣 還是以前被割地的竹南？\\n\\n學區來說\\n只要在新...   \n",
       "4  https://www.nownews.com/news/6331938\\n2023年12月...   \n",
       "\n",
       "                                          artComment            e_ip  \\\n",
       "0  [{\"cmtStatus\": \"推\", \"cmtPoster\": \"k11k\", \"cmtC...    42.79.72.237   \n",
       "1  [{\"cmtStatus\": \"推\", \"cmtPoster\": \"sunnyhung\", ...  114.136.154.65   \n",
       "2  [{\"cmtStatus\": \"→\", \"cmtPoster\": \"SpongebobMac...  36.230.152.131   \n",
       "3  [{\"cmtStatus\": \"推\", \"cmtPoster\": \"marsonele\", ...   61.230.11.219   \n",
       "4  [{\"cmtStatus\": \"推\", \"cmtPoster\": \"forestsea722...  138.199.22.107   \n",
       "\n",
       "          insertedDate dataSource  \n",
       "0  2024-01-02 02:21:08        ptt  \n",
       "1  2024-01-02 02:21:08        ptt  \n",
       "2  2024-01-02 02:21:08        ptt  \n",
       "3  2024-01-02 02:21:08        ptt  \n",
       "4  2024-01-02 02:21:10        ptt  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "folder_path = \"/content/drive/MyDrive/SMA_2025S-main/\"\n",
    "csv_files = glob.glob(os.path.join(folder_path, '*.csv'))\n",
    "\n",
    "# 用來存放每一個 DataFrame\n",
    "df_list = []\n",
    "\n",
    "# 讀取每個 CSV 並加到 list 裡\n",
    "for file in csv_files:\n",
    "    df = pd.read_csv(file)\n",
    "    df_list.append(df)\n",
    "\n",
    "# 合併所有 DataFrame\n",
    "df = pd.concat(df_list, ignore_index=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of posts: 16332\n",
      "date range: ('2020-06-01 11:49:04', '2025-03-31 23:33:43')\n",
      "============================================================\n",
      "(ptt_techjob.csv)\n",
      "number of posts: 5401\n",
      "date range: 2024-01-01 10:36:44 ~ 2025-03-31 23:33:43\n",
      "\n",
      "(ptt_parttime.csv)\n",
      "number of posts: 5570\n",
      "date range: 2024-01-01 00:25:21 ~ 2025-03-31 22:54:22\n",
      "\n",
      "(ptt_softjob.csv)\n",
      "number of posts: 5361\n",
      "date range: 2020-06-01 11:49:04 ~ 2025-03-31 03:14:57\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 幾篇文章\n",
    "print(f\"number of posts: {df.shape[0]}\")\n",
    "print(f\"date range: {(df['artDate'].min(), df['artDate'].max())}\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "for file in csv_files:\n",
    "    csv = pd.read_csv(file)\n",
    "    post_count = csv.shape[0]\n",
    "    date_range = (csv['artDate'].min(), csv['artDate'].max())\n",
    "\n",
    "    filename = os.path.basename(file)\n",
    "\n",
    "    print(f\"({filename})\")\n",
    "    print(f\"number of posts: {post_count}\")\n",
    "    print(f\"date range: {date_range[0]} ~ {date_range[1]}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-62c67f3139c0>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"artContent\"] = df.artContent.apply(\n",
      "<ipython-input-7-62c67f3139c0>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"artTitle\"] = df[\"artTitle\"].apply(\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
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\"dtype\": \"string\",\n        \"num_unique_values\": 11970,\n        \"samples\": [\n          \"\\u500b\\u4eba\\u5fb5\\u4e00\\u9031\\u901a\\u8b6f\\u7c21\\u55ae\\u65e5\\u8a9e\\u7a0b\\u5ea6\\u5373\\u53ef\",\n          \"\\u8acb\\u76ca\\u8acb\\u76ca\\u8089\\u9b06\\u6d77\\u908a\",\n          \"\\u5fb5\\u624d\\u767e\\u777f\\u9054\\u8aa0\\u5fb5\\u8edf\\u9ad4\\u54c1\\u4fdd\\u5de5\\u7a0b\\u5e2b\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artDate\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 16304,\n        \"samples\": [\n          \"2024-12-14 04:35:59\",\n          \"2024-08-22 12:01:19\",\n          \"2024-07-19 09:27:18\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artPoster\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6356,\n        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20:33:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"bm112622\\\", \\\"cmtContent\\\": \\\":\\u8acb\\u770b\\u7ad9\\u5167\\u4fe1\\\", \\\"cmtDate\\\": \\\"2025-02-11 20:45:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"glwl40039\\\", \\\"cmtContent\\\": \\\":\\u4ee5\\u524d\\u57f9\\u8a13\\u73ed\\u7684\\u7562\\u696d\\u5c08\\u984c\\u4f9d\\u7167\\u8868\\u55ae\\u63a8\\u85a6\\u500b\\u4eba\\u5316\\u4fe1\\u7528\\u5361\\\", \\\"cmtDate\\\": \\\"2025-02-11 21:03:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"allena840214\\\", \\\"cmtContent\\\": \\\":\\u770b\\u4e86\\u8cc7\\u8a0a\\uff0c\\u9650\\u5b78\\u751f\\u6b98\\u5ff5\\\", \\\"cmtDate\\\": \\\"2025-02-11 22:13:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u63a8\\\", \\\"cmtPoster\\\": \\\"selfvalue\\\", \\\"cmtContent\\\": \\\":==\\u6211\\u4ee5\\u70ba\\u662f\\u7d66\\u6211\\u5011\\u9019\\u4e9b\\u9802\\u7d1a\\u99ed\\u5ba2\\u53c3\\u52a0\\u7684\\\", \\\"cmtDate\\\": \\\"2025-02-11 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\\\":Richard\\u771f\\u7684\\u5f88\\u5f37\\uff0c\\u4e4b\\u524d\\u53c3\\u52a0\\u6d3b\\u52d5\\uff0c\\u6709\\u807d\\u4ed6\\u5206\\u4eab\\u904e\\uff0c\\u601d\\u8def\\u5f88\\u6e05\\\", \\\"cmtDate\\\": \\\"2025-02-12 21:16:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"YYYWei\\\", \\\"cmtContent\\\": \\\":\\u6670\\\", \\\"cmtDate\\\": \\\"2025-02-12 21:16:00\\\"}, {\\\"cmtStatus\\\": \\\"\\u5653\\\", \\\"cmtPoster\\\": \\\"waldo870\\\", \\\"cmtContent\\\": \\\":\\u9650\\u5b78\\u751f\\\", \\\"cmtDate\\\": \\\"2025-02-22 13:30:00\\\"}]\",\n          \"[{\\\"cmtStatus\\\": \\\"\\u2192\\\", \\\"cmtPoster\\\": \\\"Maguire8251\\\", \\\"cmtContent\\\": \\\":\\u5df2\\u6536\\u5230\\u591a\\u4efd\\u5c65\\u6b77\\uff0c\\u8b1d\\u8b1d\\u5927\\u5bb6\\u5c0f\\u6642\\u5019\\u90fd\\u611b\\u8cbc\\u8cbc\\u7d19\\uff01\\\", \\\"cmtDate\\\": \\\"2025-03-26 00:35:00\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"e_ip\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11959,\n        \"samples\": [\n          \"111.71.100.22\",\n          \"218.172.138.186\",\n          \"118.168.59.65\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"insertedDate\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2632,\n        \"samples\": [\n          \"2024-06-01 02:16:07\",\n          \"2024-08-02 02:13:41\",\n          \"2024-05-31 02:10:32\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dataSource\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"ptt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-625d86ac-d461-4a30-a0c8-786fbe04aed5\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>system_id</th>\n",
       "      <th>artUrl</th>\n",
       "      <th>artTitle</th>\n",
       "      <th>artDate</th>\n",
       "      <th>artPoster</th>\n",
       "      <th>artCatagory</th>\n",
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       "      <th>e_ip</th>\n",
       "      <th>insertedDate</th>\n",
       "      <th>dataSource</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704076606.A...</td>\n",
       "      <td>請益在新竹上班到底有什麼優點</td>\n",
       "      <td>2024-01-01 10:36:44</td>\n",
       "      <td>dilson</td>\n",
       "      <td>Tech_Job</td>\n",
       "      <td>不見得喔我看過私校學店正妹在科技業的男友也是私校學店因為沒腦的跟有腦的怎麼可能有話聊台清交電...</td>\n",
       "      <td>[{\"cmtStatus\": \"推\", \"cmtPoster\": \"k11k\", \"cmtC...</td>\n",
       "      <td>42.79.72.237</td>\n",
       "      <td>2024-01-02 02:21:08</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704078788.A...</td>\n",
       "      <td>新聞台積電效應日本半導體廠開第槍</td>\n",
       "      <td>2024-01-01 11:13:06</td>\n",
       "      <td>qazxc1156892</td>\n",
       "      <td>Tech_Job</td>\n",
       "      <td>新聞標題台積電效應日本半導體廠開第槍宣告新進員工加薪記者陳瑩欣台北報導台積電等大型半導體廠赴...</td>\n",
       "      <td>[{\"cmtStatus\": \"推\", \"cmtPoster\": \"sunnyhung\", ...</td>\n",
       "      <td>114.136.154.65</td>\n",
       "      <td>2024-01-02 02:21:08</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704080503.A...</td>\n",
       "      <td>請益在新竹上班到底有什麼優點</td>\n",
       "      <td>2024-01-01 11:41:41</td>\n",
       "      <td>francej</td>\n",
       "      <td>Tech_Job</td>\n",
       "      <td>如果有要生小孩的新竹大概是目前全國最適合學齡小孩成長的環境吧人口平均素質高別的縣市包括雙北擔...</td>\n",
       "      <td>[{\"cmtStatus\": \"→\", \"cmtPoster\": \"SpongebobMac...</td>\n",
       "      <td>36.230.152.131</td>\n",
       "      <td>2024-01-02 02:21:08</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-625d86ac-d461-4a30-a0c8-786fbe04aed5')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
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       "  </svg>\n",
       "    </button>\n",
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       "      display:flex;\n",
       "      gap: 12px;\n",
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       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "      fill: #1967D2;\n",
       "      height: 32px;\n",
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       "      width: 32px;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-625d86ac-d461-4a30-a0c8-786fbe04aed5 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-625d86ac-d461-4a30-a0c8-786fbe04aed5');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-0c969c59-a4e3-4018-960b-d8687824cd5e\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0c969c59-a4e3-4018-960b-d8687824cd5e')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-0c969c59-a4e3-4018-960b-d8687824cd5e button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   system_id                                             artUrl  \\\n",
       "0          1  https://www.ptt.cc/bbs/Tech_Job/M.1704076606.A...   \n",
       "1          2  https://www.ptt.cc/bbs/Tech_Job/M.1704078788.A...   \n",
       "2          3  https://www.ptt.cc/bbs/Tech_Job/M.1704080503.A...   \n",
       "\n",
       "           artTitle              artDate     artPoster artCatagory  \\\n",
       "0    請益在新竹上班到底有什麼優點  2024-01-01 10:36:44        dilson    Tech_Job   \n",
       "1  新聞台積電效應日本半導體廠開第槍  2024-01-01 11:13:06  qazxc1156892    Tech_Job   \n",
       "2    請益在新竹上班到底有什麼優點  2024-01-01 11:41:41       francej    Tech_Job   \n",
       "\n",
       "                                          artContent  \\\n",
       "0  不見得喔我看過私校學店正妹在科技業的男友也是私校學店因為沒腦的跟有腦的怎麼可能有話聊台清交電...   \n",
       "1  新聞標題台積電效應日本半導體廠開第槍宣告新進員工加薪記者陳瑩欣台北報導台積電等大型半導體廠赴...   \n",
       "2  如果有要生小孩的新竹大概是目前全國最適合學齡小孩成長的環境吧人口平均素質高別的縣市包括雙北擔...   \n",
       "\n",
       "                                          artComment            e_ip  \\\n",
       "0  [{\"cmtStatus\": \"推\", \"cmtPoster\": \"k11k\", \"cmtC...    42.79.72.237   \n",
       "1  [{\"cmtStatus\": \"推\", \"cmtPoster\": \"sunnyhung\", ...  114.136.154.65   \n",
       "2  [{\"cmtStatus\": \"→\", \"cmtPoster\": \"SpongebobMac...  36.230.152.131   \n",
       "\n",
       "          insertedDate dataSource  \n",
       "0  2024-01-02 02:21:08        ptt  \n",
       "1  2024-01-02 02:21:08        ptt  \n",
       "2  2024-01-02 02:21:08        ptt  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 過濾 nan 的資料\n",
    "df = df.dropna(subset=['artTitle'])\n",
    "df = df.dropna(subset=['artContent'])\n",
    "# 移除網址格式\n",
    "df[\"artContent\"] = df.artContent.apply(\n",
    "    lambda x: re.sub(\"(http|https)://.*\", \"\", x)\n",
    ")\n",
    "df[\"artTitle\"] = df[\"artTitle\"].apply(\n",
    "    lambda x: re.sub(\"(http|https)://.*\", \"\", x)\n",
    ")\n",
    "# 只留下中文字\n",
    "df[\"artContent\"] = df.artContent.apply(\n",
    "    lambda x: re.sub(\"[^\\u4e00-\\u9fa5]+\", \"\", x)\n",
    ")\n",
    "df[\"artTitle\"] = df[\"artTitle\"].apply(\n",
    "    lambda x: re.sub(\"[^\\u4e00-\\u9fa5]+\", \"\", x)\n",
    ")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
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       ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artUrl\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16310,\n        \"samples\": [\n          \"https://www.ptt.cc/bbs/Soft_Job/M.1641403612.A.22B.html\",\n          \"https://www.ptt.cc/bbs/Tech_Job/M.1709571181.A.D6D.html\",\n          \"https://www.ptt.cc/bbs/part-time/M.1707836410.A.FAB.html\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"artCatagory\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Tech_Job\",\n          \"part_time\",\n          \"Soft_Job\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-520b47a0-d82a-4b3a-bc54-6c9c3ccc8ab9\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>artUrl</th>\n",
       "      <th>artCatagory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>請益在新竹上班到底有什麼優點不見得喔我看過私校學店正妹在科技業的男友也是私校學店因為沒腦的跟...</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704076606.A...</td>\n",
       "      <td>Tech_Job</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>新聞台積電效應日本半導體廠開第槍新聞標題台積電效應日本半導體廠開第槍宣告新進員工加薪記者陳瑩...</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704078788.A...</td>\n",
       "      <td>Tech_Job</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>請益在新竹上班到底有什麼優點如果有要生小孩的新竹大概是目前全國最適合學齡小孩成長的環境吧人口...</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704080503.A...</td>\n",
       "      <td>Tech_Job</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>請益在新竹上班到底有什麼優點講新竹太籠統是新竹市還是新竹縣還是以前被割地的竹南學區來說只要在...</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704100050.A...</td>\n",
       "      <td>Tech_Job</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>新聞股王製造機王雪紅不看一時成敗拚年月日記者許家禎特稿股王製造機王雪紅不看一時成敗拚氣長宏達...</td>\n",
       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704106015.A...</td>\n",
       "      <td>Tech_Job</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-520b47a0-d82a-4b3a-bc54-6c9c3ccc8ab9')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-520b47a0-d82a-4b3a-bc54-6c9c3ccc8ab9 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-520b47a0-d82a-4b3a-bc54-6c9c3ccc8ab9');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-9f047833-3329-49a7-a035-124da3969824\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9f047833-3329-49a7-a035-124da3969824')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-9f047833-3329-49a7-a035-124da3969824 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                             content  \\\n",
       "0  請益在新竹上班到底有什麼優點不見得喔我看過私校學店正妹在科技業的男友也是私校學店因為沒腦的跟...   \n",
       "1  新聞台積電效應日本半導體廠開第槍新聞標題台積電效應日本半導體廠開第槍宣告新進員工加薪記者陳瑩...   \n",
       "2  請益在新竹上班到底有什麼優點如果有要生小孩的新竹大概是目前全國最適合學齡小孩成長的環境吧人口...   \n",
       "3  請益在新竹上班到底有什麼優點講新竹太籠統是新竹市還是新竹縣還是以前被割地的竹南學區來說只要在...   \n",
       "4  新聞股王製造機王雪紅不看一時成敗拚年月日記者許家禎特稿股王製造機王雪紅不看一時成敗拚氣長宏達...   \n",
       "\n",
       "                                              artUrl artCatagory  \n",
       "0  https://www.ptt.cc/bbs/Tech_Job/M.1704076606.A...    Tech_Job  \n",
       "1  https://www.ptt.cc/bbs/Tech_Job/M.1704078788.A...    Tech_Job  \n",
       "2  https://www.ptt.cc/bbs/Tech_Job/M.1704080503.A...    Tech_Job  \n",
       "3  https://www.ptt.cc/bbs/Tech_Job/M.1704100050.A...    Tech_Job  \n",
       "4  https://www.ptt.cc/bbs/Tech_Job/M.1704106015.A...    Tech_Job  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 留下 content\n",
    "df[\"content\"] = df[\"artTitle\"] + df[\"artContent\"]\n",
    "df = df[[\"content\", \"artUrl\", \"artCatagory\"]]  # 文章內容 文章連結\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total docs: 16310\n"
     ]
    }
   ],
   "source": [
    "print(f\"total docs: {df.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **斷詞**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 設定繁體中文詞庫\n",
    "jieba.set_dictionary(\"/content/drive/MyDrive/SMA_2025S-main/week07/dict/dict.txt.big\")\n",
    "\n",
    "# 新增stopwords\n",
    "# jieba.analyse.set_stop_words('./dict/stop_words.txt') #jieba.analyse.extract_tags才會作用\n",
    "with open(\"/content/drive/MyDrive/SMA_2025S-main/week07/dict/stop_words.txt\", encoding=\"utf-8\") as f:\n",
    "    stopWords = [line.strip() for line in f.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from /content/drive/MyDrive/SMA_2025S-main/week07/dict/dict.txt.big ...\n",
      "DEBUG:jieba:Building prefix dict from /content/drive/MyDrive/SMA_2025S-main/week07/dict/dict.txt.big ...\n",
      "Dumping model to file cache /tmp/jieba.u7fdbfa156d71dde3366dc327b32adc37.cache\n",
      "DEBUG:jieba:Dumping model to file cache /tmp/jieba.u7fdbfa156d71dde3366dc327b32adc37.cache\n",
      "Loading model cost 5.069 seconds.\n",
      "DEBUG:jieba:Loading model cost 5.069 seconds.\n",
      "Prefix dict has been built successfully.\n",
      "DEBUG:jieba:Prefix dict has been built successfully.\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
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       "      <td>請益在新竹上班到底有什麼優點不見得喔我看過私校學店正妹在科技業的男友也是私校學店因為沒腦的跟...</td>\n",
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       "      <td>https://www.ptt.cc/bbs/Tech_Job/M.1704078788.A...</td>\n",
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       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8efdcf19-df73-4562-a81f-28acf734be21')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-8efdcf19-df73-4562-a81f-28acf734be21 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-8efdcf19-df73-4562-a81f-28acf734be21');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-46712acb-ce36-4126-bca0-225397e02acc\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-46712acb-ce36-4126-bca0-225397e02acc')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-46712acb-ce36-4126-bca0-225397e02acc button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                             content  \\\n",
       "0  請益在新竹上班到底有什麼優點不見得喔我看過私校學店正妹在科技業的男友也是私校學店因為沒腦的跟...   \n",
       "1  新聞台積電效應日本半導體廠開第槍新聞標題台積電效應日本半導體廠開第槍宣告新進員工加薪記者陳瑩...   \n",
       "2  請益在新竹上班到底有什麼優點如果有要生小孩的新竹大概是目前全國最適合學齡小孩成長的環境吧人口...   \n",
       "3  請益在新竹上班到底有什麼優點講新竹太籠統是新竹市還是新竹縣還是以前被割地的竹南學區來說只要在...   \n",
       "4  新聞股王製造機王雪紅不看一時成敗拚年月日記者許家禎特稿股王製造機王雪紅不看一時成敗拚氣長宏達...   \n",
       "\n",
       "                                              artUrl artCatagory  \\\n",
       "0  https://www.ptt.cc/bbs/Tech_Job/M.1704076606.A...    Tech_Job   \n",
       "1  https://www.ptt.cc/bbs/Tech_Job/M.1704078788.A...    Tech_Job   \n",
       "2  https://www.ptt.cc/bbs/Tech_Job/M.1704080503.A...    Tech_Job   \n",
       "3  https://www.ptt.cc/bbs/Tech_Job/M.1704100050.A...    Tech_Job   \n",
       "4  https://www.ptt.cc/bbs/Tech_Job/M.1704106015.A...    Tech_Job   \n",
       "\n",
       "                                               words  \n",
       "0  請益 新竹 上班 優點 不見得 看過 私校 學店 正妹 科技 業的 男友 私校 學店 沒腦 ...  \n",
       "1  新聞 台積電 效應 日本 半導體 廠開 第槍 新聞標題 台積電 效應 日本 半導體 廠開 第...  \n",
       "2  請益 新竹 上班 優點 有要 生小孩 新竹 目前 全國 適合 學齡 小孩 成長 環境 人口 ...  \n",
       "3  請益 新竹 上班 優點 新竹 籠統 新竹市 新竹縣 以前 割地 竹南 學區 來說 新竹市 東...  \n",
       "4  新聞 股王 製造機 王雪紅 一時 成敗 年月日 記者 許家 特稿 股王 製造機 王雪紅 一時...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 設定斷詞 function\n",
    "def getToken(row):\n",
    "    seg_list = jieba.cut(row, cut_all=False)\n",
    "    seg_list = [\n",
    "        w for w in seg_list if w not in stopWords and len(w) > 1\n",
    "    ]  # 篩選掉停用字與字元數大於1的詞彙\n",
    "    return seg_list\n",
    "\n",
    "df[\"words\"] = df[\"content\"].apply(getToken).map(\" \".join)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **訓練**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total posts: 16310\n",
      "category: \n",
      "artCatagory\n",
      "part_time    5567\n",
      "Tech_Job     5395\n",
      "Soft_Job     5348\n",
      "Name: count, dtype: int64\n",
      "====================================================================================================\n",
      "11414    請益 大學 休學 全心 工作 覺得 聰明 讀書 兼顧 帶來 回報 讀書 自然 選擇 輟學 天...\n",
      "7721     新北 新店 裕隆 活動 派票 時薪 同意 願意 遵守 現行 法律 本站 使用者 條款 本站 ...\n",
      "3256     新聞 中國 實現 國產 曝光 機可 生產 奈米 中國 實現 國產 曝光 機可 生產 奈米 以...\n",
      "15141    徵才 遠端 美商 公司 名稱 統編 中華民國 以外 註冊 可免 公司地址 填寫 詳細 至號 ...\n",
      "13980    問卷 藍牙 耳機 進修 網之 職涯 進修 調查 人力 銀行 今年 針對 上班族 進修 調查 ...\n",
      "Name: words, dtype: object\n",
      "11414     Soft_Job\n",
      "7721     part_time\n",
      "3256      Tech_Job\n",
      "15141     Soft_Job\n",
      "13980     Soft_Job\n",
      "Name: artCatagory, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 檢視資料\n",
    "print(f\"total posts: {len(df['artUrl'].unique())}\")\n",
    "print(f\"category: \\n{df['artCatagory'].value_counts()}\")\n",
    "print(\"=\"*100)\n",
    "\n",
    "data = df\n",
    "X = data[\"words\"]\n",
    "y = data[\"artCatagory\"]\n",
    "\n",
    "# 把整個資料集七三切\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.3, random_state=777\n",
    ")\n",
    "\n",
    "print(X_train.head())\n",
    "print(y_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw data percentage :\n",
      "artCatagory\n",
      "part_time    34.132434\n",
      "Tech_Job     33.077866\n",
      "Soft_Job     32.789700\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "train percentage :\n",
      "artCatagory\n",
      "part_time    34.229658\n",
      "Tech_Job     33.432601\n",
      "Soft_Job     32.337742\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "test percentage :\n",
      "artCatagory\n",
      "part_time    33.905579\n",
      "Soft_Job     33.844267\n",
      "Tech_Job     32.250153\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 看一下各個資料集切分的比例，應該要一致\n",
    "print(\n",
    "    f\"raw data percentage :\\n{data['artCatagory'].value_counts(normalize=True) * 100}\"\n",
    ")\n",
    "print(f\"\\ntrain percentage :\\n{y_train.value_counts(normalize=True) * 100}\")\n",
    "print(f\"\\ntest percentage :\\n{y_test.value_counts(normalize=True) * 100}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **DTM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "</div><br><label><b>dtype:</b> object</label>"
      ],
      "text/plain": [
       "11414    請益 大學 休學 全心 工作 覺得 聰明 讀書 兼顧 帶來 回報 讀書 自然 選擇 輟學 天...\n",
       "7721     新北 新店 裕隆 活動 派票 時薪 同意 願意 遵守 現行 法律 本站 使用者 條款 本站 ...\n",
       "3256     新聞 中國 實現 國產 曝光 機可 生產 奈米 中國 實現 國產 曝光 機可 生產 奈米 以...\n",
       "15141    徵才 遠端 美商 公司 名稱 統編 中華民國 以外 註冊 可免 公司地址 填寫 詳細 至號 ...\n",
       "13980    問卷 藍牙 耳機 進修 網之 職涯 進修 調查 人力 銀行 今年 針對 上班族 進修 調查 ...\n",
       "Name: words, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(max_features=1000)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "variable_name": "Count_df"
      },
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       "      <th>一家</th>\n",
       "      <th>...</th>\n",
       "      <th>願意</th>\n",
       "      <th>類似</th>\n",
       "      <th>類別</th>\n",
       "      <th>顧問</th>\n",
       "      <th>顯示</th>\n",
       "      <th>風險</th>\n",
       "      <th>餐廳</th>\n",
       "      <th>馬斯克</th>\n",
       "      <th>體驗</th>\n",
       "      <th>高雄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11412</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11413</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11414</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11415</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11416</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11417 rows × 1000 columns</p>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1d672d8-ddae-4730-9458-945c0fe293a4')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-e1d672d8-ddae-4730-9458-945c0fe293a4 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-e1d672d8-ddae-4730-9458-945c0fe293a4');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-6f9a75c8-e3e6-4a51-93ac-583661f0cf2b\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f9a75c8-e3e6-4a51-93ac-583661f0cf2b')\"\n",
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       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "    <g>\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
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       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
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       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-6f9a75c8-e3e6-4a51-93ac-583661f0cf2b button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "  <div id=\"id_3cb597ef-c212-455f-8739-30c9c9ed5494\">\n",
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       "      .colab-df-generate {\n",
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       "        border: none;\n",
       "        border-radius: 50%;\n",
       "        cursor: pointer;\n",
       "        display: none;\n",
       "        fill: #1967D2;\n",
       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
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       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "        fill: #174EA6;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate {\n",
       "        background-color: #3B4455;\n",
       "        fill: #D2E3FC;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate:hover {\n",
       "        background-color: #434B5C;\n",
       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "        fill: #FFFFFF;\n",
       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('Count_df')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_3cb597ef-c212-455f-8739-30c9c9ed5494 button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('Count_df');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "       一下  一些  一任  一份  一位  一個月  一堆  一天  一定  一家  ...  願意  類似  類別  顧問  顯示  風險  \\\n",
       "0       0   0   0   0   0    0   0   0   0   0  ...   0   0   0   0   0   0   \n",
       "1       0   0   0   0   0    0   0   0   0   0  ...   2   0   0   0   0   0   \n",
       "2       0   0   0   0   0    0   0   0   0   0  ...   0   0   0   0   2   0   \n",
       "3       0   1   0   0   0    0   0   0   0   0  ...   0   0   0   0   0   0   \n",
       "4       0   0   0   0   0    0   0   0   0   0  ...   0   0   0   0   0   0   \n",
       "...    ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ..   \n",
       "11412   0   0   1   0   0    0   0   0   0   0  ...   2   0   1   1   1   1   \n",
       "11413   0   0   0   0   0    0   0   0   0   0  ...   0   0   0   0   0   0   \n",
       "11414   0   0   0   0   0    0   0   0   0   0  ...   0   0   0   0   0   0   \n",
       "11415   0   0   0   0   0    0   0   0   0   1  ...   0   0   0   0   0   0   \n",
       "11416   0   0   0   0   0    0   0   0   0   0  ...   0   0   0   0   0   0   \n",
       "\n",
       "       餐廳  馬斯克  體驗  高雄  \n",
       "0       0    0   0   0  \n",
       "1       0    0   0   0  \n",
       "2       0    0   0   0  \n",
       "3       0    0   0   0  \n",
       "4       0    0   0   0  \n",
       "...    ..  ...  ..  ..  \n",
       "11412   1    0   0   0  \n",
       "11413   0    0   0   0  \n",
       "11414   0    0   0   0  \n",
       "11415   0    0   0   0  \n",
       "11416   0    0   0   0  \n",
       "\n",
       "[11417 rows x 1000 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vec_train = vectorizer.fit_transform(X_train)\n",
    "vocabulary = vectorizer.get_feature_names_out()\n",
    "\n",
    "Count_df = pd.DataFrame(columns = vocabulary, data = vec_train.toarray())\n",
    "Count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11417, 1000)\n",
      "(4893, 1000)\n"
     ]
    }
   ],
   "source": [
    "vec_test = vectorizer.transform(X_test)\n",
    "print(vec_train.shape)\n",
    "print(vec_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **邏輯式回歸分類器**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 建立分類器模型\n",
    "clf = LogisticRegression()\n",
    "clf.fit(vec_train, y_train)\n",
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Soft_Job', 'Tech_Job', 'part_time'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**使用train set訓練完後，用測試集試試看模型的分類結果**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['part_time' 'part_time' 'Tech_Job' 'part_time' 'Soft_Job' 'Soft_Job'\n",
      " 'Soft_Job' 'part_time' 'Tech_Job' 'Tech_Job']\n",
      "(4893, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([7.48678735e-23, 1.11943780e-22, 1.00000000e+00])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = clf.predict(vec_test)\n",
    "y_pred_proba = clf.predict_proba(vec_test)\n",
    "print(y_pred[:10])\n",
    "\n",
    "print(y_pred_proba.shape)\n",
    "y_pred_proba[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **模型評估**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Soft_Job       0.87      0.85      0.86      1656\n",
      "    Tech_Job       0.85      0.87      0.86      1578\n",
      "   part_time       1.00      1.00      1.00      1659\n",
      "\n",
      "    accuracy                           0.91      4893\n",
      "   macro avg       0.91      0.91      0.91      4893\n",
      "weighted avg       0.91      0.91      0.91      4893\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Accuracy, Precision, Recall, F1-score\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.5, 1.5, 2.5]),\n",
       " [Text(0, 0.5, 'Soft_Job'),\n",
       "  Text(0, 1.5, 'Tech_Job'),\n",
       "  Text(0, 2.5, 'part_time')])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = clf.classes_\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm\n",
    "\n",
    "## Plot confusion matrix\n",
    "fig, ax = plt.subplots()\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", ax=ax, cmap=plt.cm.Blues, cbar=False)\n",
    "ax.set(\n",
    "    xlabel=\"Pred\",\n",
    "    ylabel=\"True\",\n",
    "    xticklabels=classes,\n",
    "    yticklabels=classes,\n",
    "    title=\"Confusion matrix\",\n",
    ")\n",
    "plt.yticks(rotation=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **TF-IDF DTM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "variable_name": "tfidf_df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-ae3d7483-e24c-432c-9249-a0e05538dca3\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>一下</th>\n",
       "      <th>一些</th>\n",
       "      <th>一任</th>\n",
       "      <th>一份</th>\n",
       "      <th>一位</th>\n",
       "      <th>一個月</th>\n",
       "      <th>一堆</th>\n",
       "      <th>一天</th>\n",
       "      <th>一定</th>\n",
       "      <th>一家</th>\n",
       "      <th>...</th>\n",
       "      <th>願意</th>\n",
       "      <th>類似</th>\n",
       "      <th>類別</th>\n",
       "      <th>顧問</th>\n",
       "      <th>顯示</th>\n",
       "      <th>風險</th>\n",
       "      <th>餐廳</th>\n",
       "      <th>馬斯克</th>\n",
       "      <th>體驗</th>\n",
       "      <th>高雄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.125851</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.064781</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>11417 rows × 1000 columns</p>\n",
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       "\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
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       "\n",
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       "11416  0.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0  0.0  0.000000  ...   \n",
       "\n",
       "             願意   類似        類別        顧問        顯示        風險        餐廳  馬斯克  \\\n",
       "0      0.000000  0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.0   \n",
       "1      0.125851  0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.0   \n",
       "2      0.000000  0.0  0.000000  0.000000  0.064781  0.000000  0.000000  0.0   \n",
       "3      0.000000  0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.0   \n",
       "4      0.000000  0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.0   \n",
       "...         ...  ...       ...       ...       ...       ...       ...  ...   \n",
       "11412  0.038270  0.0  0.024139  0.043534  0.023576  0.023945  0.024656  0.0   \n",
       "11413  0.000000  0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.0   \n",
       "11414  0.000000  0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.0   \n",
       "11415  0.000000  0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.0   \n",
       "11416  0.000000  0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.0   \n",
       "\n",
       "        體驗   高雄  \n",
       "0      0.0  0.0  \n",
       "1      0.0  0.0  \n",
       "2      0.0  0.0  \n",
       "3      0.0  0.0  \n",
       "4      0.0  0.0  \n",
       "...    ...  ...  \n",
       "11412  0.0  0.0  \n",
       "11413  0.0  0.0  \n",
       "11414  0.0  0.0  \n",
       "11415  0.0  0.0  \n",
       "11416  0.0  0.0  \n",
       "\n",
       "[11417 rows x 1000 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(max_features=1000)\n",
    "vec_train = vectorizer.fit_transform(X_train)\n",
    "vec_test = vectorizer.transform(X_test)\n",
    "vocabulary = vectorizer.get_feature_names_out()\n",
    "\n",
    "tfidf_df = pd.DataFrame(columns = vocabulary, data = vec_train.toarray())\n",
    "tfidf_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Soft_Job       0.89      0.89      0.89      1656\n",
      "    Tech_Job       0.88      0.88      0.88      1578\n",
      "   part_time       1.00      0.99      1.00      1659\n",
      "\n",
      "    accuracy                           0.92      4893\n",
      "   macro avg       0.92      0.92      0.92      4893\n",
      "weighted avg       0.92      0.92      0.92      4893\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf.fit(vec_train, y_train)\n",
    "y_pred = clf.predict(vec_test)\n",
    "y_pred_proba = clf.predict_proba(vec_test)\n",
    "\n",
    "# results\n",
    "## Accuracy, Precision, Recall, F1-score\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 比較\n",
    "\n",
    "明顯看出軟體及科技工作的預測相對於打工更易使模型混淆，表示兩者在被討論時有著重疊的用詞\n",
    "\n",
    "\n",
    "**依據詞頻**\n",
    "\n",
    "![image.png](data:image/png;base64,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)\n",
    "\n",
    "\n",
    "**依據TF-IDF**\n",
    "\n",
    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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **CV cross-validation**\n",
    "切成 k 組 train-test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'estimator': [LogisticRegression(),\n",
      "               LogisticRegression(),\n",
      "               LogisticRegression(),\n",
      "               LogisticRegression(),\n",
      "               LogisticRegression()],\n",
      " 'fit_time': array([0.23089862, 0.27636576, 0.25236869, 0.34397149, 0.27416658]),\n",
      " 'score_time': array([0.02289605, 0.024647  , 0.02181077, 0.0233047 , 0.0221231 ]),\n",
      " 'test_f1_macro': array([0.91902789, 0.9123334 , 0.91506679, 0.91453077, 0.90227382]),\n",
      " 'test_precision_macro': array([0.91950184, 0.91240797, 0.91527236, 0.91474791, 0.90267443]),\n",
      " 'test_recall_macro': array([0.9191661 , 0.91231691, 0.91495399, 0.91452967, 0.90217693])}\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression()\n",
    "vec_train = TfidfVectorizer(max_features=1000).fit_transform(X_train)\n",
    "\n",
    "scores = cross_validate(clf, vec_train, y_train, cv=5, scoring=(\"f1_macro\", \"recall_macro\", \"precision_macro\"), return_estimator=True)\n",
    "pprint(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Soft_Job       0.86      0.88      0.87      3692\n",
      "    Tech_Job       0.88      0.86      0.87      3817\n",
      "   part_time       1.00      0.99      0.99      3908\n",
      "\n",
      "    accuracy                           0.91     11417\n",
      "   macro avg       0.91      0.91      0.91     11417\n",
      "weighted avg       0.91      0.91      0.91     11417\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = cross_val_predict(clf, vec_train, y_train, cv=5)\n",
    "print(classification_report(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **比較不同模型**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定義模型訓練組合\n",
    "## pipeline: 資料處理 vectorizer + 分類器 clf\n",
    "## 由於 cross-validation 會自動將資料分成 train/test，因此 input 只要給 X, y 即可\n",
    "\n",
    "def train_cv(vectorizer, clf, X, y):\n",
    "\n",
    "    ## train classifier\n",
    "    vec_X = vectorizer.fit_transform(X).toarray()\n",
    "\n",
    "    ## get cv results\n",
    "    cv_results = cross_validate(clf, vec_X, y, cv=5, return_estimator=True)\n",
    "    y_pred = cross_val_predict(clf, vec_X, y, cv=5)\n",
    "    y_pred_proba = cross_val_predict(clf, vec_X, y, cv=5, method=\"predict_proba\")\n",
    "\n",
    "    ## Accuracy, Precision, Recall, F1-score\n",
    "    cls_report = classification_report(y, y_pred, output_dict=True)\n",
    "    print(classification_report(y, y_pred))\n",
    "\n",
    "    classes = cv_results['estimator'][0].classes_\n",
    "\n",
    "    ## Plot confusion matrix\n",
    "    cm = confusion_matrix(y, y_pred)\n",
    "    fig, ax = plt.subplots()\n",
    "    sns.heatmap(cm, annot=True, fmt=\"d\", ax=ax, cmap=plt.cm.Blues, cbar=False)\n",
    "    ax.set(\n",
    "        xlabel=\"Pred\",\n",
    "        ylabel=\"True\",\n",
    "        xticklabels=classes,\n",
    "        yticklabels=classes,\n",
    "        title= str(clf) + \"Confusion matrix\",\n",
    "    )\n",
    "    plt.yticks(rotation=0)\n",
    "\n",
    "    clf.fit(vec_X, y)\n",
    "    # return the model object\n",
    "    return cls_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Soft_Job       0.86      0.88      0.87      3692\n",
      "    Tech_Job       0.88      0.86      0.87      3817\n",
      "   part_time       1.00      0.99      0.99      3908\n",
      "\n",
      "    accuracy                           0.91     11417\n",
      "   macro avg       0.91      0.91      0.91     11417\n",
      "weighted avg       0.91      0.91      0.91     11417\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(max_features=1000)\n",
    "clf = LogisticRegression()\n",
    "result = train_cv(vectorizer, clf, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================================================\n",
      "now training: clf_logistic\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Soft_Job       0.86      0.88      0.87      3692\n",
      "    Tech_Job       0.88      0.86      0.87      3817\n",
      "   part_time       1.00      0.99      0.99      3908\n",
      "\n",
      "    accuracy                           0.91     11417\n",
      "   macro avg       0.91      0.91      0.91     11417\n",
      "weighted avg       0.91      0.91      0.91     11417\n",
      "\n",
      "====================================================================================================\n",
      "====================================================================================================\n",
      "now training: clf_dtree\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Soft_Job       0.79      0.80      0.80      3692\n",
      "    Tech_Job       0.80      0.80      0.80      3817\n",
      "   part_time       0.99      0.99      0.99      3908\n",
      "\n",
      "    accuracy                           0.86     11417\n",
      "   macro avg       0.86      0.86      0.86     11417\n",
      "weighted avg       0.86      0.86      0.86     11417\n",
      "\n",
      "====================================================================================================\n",
      "====================================================================================================\n",
      "now training: clf_svm\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Soft_Job       0.85      0.90      0.87      3692\n",
      "    Tech_Job       0.89      0.86      0.87      3817\n",
      "   part_time       1.00      0.99      0.99      3908\n",
      "\n",
      "    accuracy                           0.91     11417\n",
      "   macro avg       0.91      0.91      0.91     11417\n",
      "weighted avg       0.92      0.91      0.92     11417\n",
      "\n",
      "====================================================================================================\n",
      "====================================================================================================\n",
      "now training: clf_rf\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Soft_Job       0.84      0.91      0.87      3692\n",
      "    Tech_Job       0.90      0.84      0.87      3817\n",
      "   part_time       1.00      0.99      0.99      3908\n",
      "\n",
      "    accuracy                           0.91     11417\n",
      "   macro avg       0.91      0.91      0.91     11417\n",
      "weighted avg       0.91      0.91      0.91     11417\n",
      "\n",
      "====================================================================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 準備訓練資料\n",
    "X = data[\"words\"]\n",
    "y = data[\"artCatagory\"]\n",
    "\n",
    "# 把整個資料集七三切\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.3, random_state=777\n",
    ")\n",
    "# 定義模型訓練組合\n",
    "model_set = dict()\n",
    "model_set['clf_logistic'] = LogisticRegression()\n",
    "model_set['clf_dtree'] = DecisionTreeClassifier()\n",
    "model_set['clf_svm'] = svm.SVC(probability=True) # 要使用SVM的predict_proba的話，必須在叫出SVC的時候就將probability設為True\n",
    "model_set['clf_rf'] = RandomForestClassifier()\n",
    "# 定義 vectorizer\n",
    "# vectorizer = CountVectorizer(max_features=1000)\n",
    "vectorizer = TfidfVectorizer(max_features=1000)\n",
    "# 存結果\n",
    "result_set = dict()\n",
    "\n",
    "for k, model in model_set.items():\n",
    "    print(\"=\"*100)\n",
    "    print(f\"now training: {k}\")\n",
    "    result_set[k] = train_cv(vectorizer, model, X_train, y_train)\n",
    "    print(\"=\"*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**分別觀察各個分類模型在不同類別的評估指標表現如何**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Soft_Job': {'precision': 0.8581560283687943,\n",
       "  'recall': 0.8848862405200434,\n",
       "  'f1-score': 0.8713161754900653,\n",
       "  'support': 3692.0},\n",
       " 'Tech_Job': {'precision': 0.8807486631016043,\n",
       "  'recall': 0.8629813990044538,\n",
       "  'f1-score': 0.8717745136959111,\n",
       "  'support': 3817.0},\n",
       " 'part_time': {'precision': 0.9997416020671834,\n",
       "  'recall': 0.9900204708290685,\n",
       "  'f1-score': 0.9948572897917203,\n",
       "  'support': 3908.0},\n",
       " 'accuracy': 0.9135499693439607,\n",
       " 'macro avg': {'precision': 0.9128820978458606,\n",
       "  'recall': 0.9126293701178553,\n",
       "  'f1-score': 0.9126493263258988,\n",
       "  'support': 11417.0},\n",
       " 'weighted avg': {'precision': 0.9141735906696125,\n",
       "  'recall': 0.9135499693439607,\n",
       "  'f1-score': 0.9137571102034384,\n",
       "  'support': 11417.0}}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_set['clf_logistic']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**找出f1-score表現最好的模型是哪個，作為我們最終得到的分類器**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model: clf_svm\n",
      "{'Soft_Job': {'f1-score': 0.8743718592964824,\n",
      "              'precision': 0.8542635658914729,\n",
      "              'recall': 0.8954496208017335,\n",
      "              'support': 3692.0},\n",
      " 'Tech_Job': {'f1-score': 0.8735662843424913,\n",
      "              'precision': 0.8897038848139093,\n",
      "              'recall': 0.8580036678019387,\n",
      "              'support': 3817.0},\n",
      " 'accuracy': 0.9149513882806342,\n",
      " 'macro avg': {'f1-score': 0.9140927505638188,\n",
      "               'precision': 0.9145695951394212,\n",
      "               'recall': 0.9141500726597706,\n",
      "               'support': 11417.0},\n",
      " 'part_time': {'f1-score': 0.9943401080524826,\n",
      "               'precision': 0.9997413347128815,\n",
      "               'recall': 0.9889969293756398,\n",
      "               'support': 3908.0},\n",
      " 'weighted avg': {'f1-score': 0.9151672553321366,\n",
      "                  'precision': 0.9159087281828808,\n",
      "                  'recall': 0.9149513882806342,\n",
      "                  'support': 11417.0}}\n"
     ]
    }
   ],
   "source": [
    "max = 0\n",
    "best_model_name = \"\"\n",
    "best_model_metric = \"f1-score\"\n",
    "\n",
    "## choose max f1-score model from result_set\n",
    "for k, v in result_set.items():\n",
    "    if v['weighted avg'][best_model_metric] > max:\n",
    "        max = v['weighted avg'][best_model_metric]\n",
    "        best_model_name = k\n",
    "print(f\"best model: {best_model_name}\")\n",
    "pprint(result_set[best_model_name])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **各字詞特徵的迴歸係數**\n",
    "\n",
    "+ 迴歸係數（coefficient estimates）反映了每個特徵對預測結果的影響程度和方向。​當其他變數保持不變時，某一特徵的值增加一個單位（例如詞頻增加1），其對應的迴歸係數表示該特徵對事件發生機率的影響。\n",
    "+ 計算後得到勝算比（odds ratio），這表示該特徵每增加一個單位，事件發生的勝算（odds）相對於未增加時的倍數變化。\n",
    "+ 這裡舉logistic regression + cv tokenizer 為例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_coef(logistic_reg_model, feature_names, top_n=10):\n",
    "    # 選出某個類別的前10大影響力字詞\n",
    "    log_odds = logistic_reg_model.coef_.T\n",
    "    coef_df = pd.DataFrame(\n",
    "        log_odds,\n",
    "        columns=logistic_reg_model.classes_, index=feature_names\n",
    "    )\n",
    "    for label in coef_df.columns:\n",
    "        select_words = (\n",
    "            coef_df[[label]]\n",
    "            .sort_values(by=label, ascending=False)\n",
    "            .iloc[np.r_[0:top_n, -top_n:0]]\n",
    "        )\n",
    "        word = select_words.index\n",
    "        count = select_words[label]\n",
    "        category_colors = np.where(\n",
    "            select_words[label] >= 0, \"darkseagreen\", \"rosybrown\"\n",
    "        )  # 設定顏色\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(8, top_n*0.8))  # 設定畫布\n",
    "        plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "        ax.barh(word, count, color=category_colors)\n",
    "        ax.invert_yaxis()\n",
    "        ax.set_title(\n",
    "            \"Coeff increase/decrease odds ratio of 「\" + label + \"」 label the most\",\n",
    "            loc=\"left\",\n",
    "            size=16,\n",
    "        )\n",
    "        ax.set_ylabel(\"word\", size=14)\n",
    "        ax.set_xlabel(\"odds ratio\", size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **第二組讀書會專案 - 文件分類**\n",
    "課程：社群媒體分析\n",
    "\n",
    "授課教授：黃三益老師\n",
    "\n",
    "組別：Group_2\n",
    "\n",
    "組員：M124020028,何允中、M134020016,王予芙、M134020034,黃沛萱、M134020037,陳宥齊、B104020032,翁武麟、M124111057,張伶宣\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "資料來源：ptt\n",
    "\n",
    "版別：打工、軟體工作、科技工作\n",
    "\n",
    "資料筆數：16310\n",
    "\n",
    "+ 分析動機：我們想探討不同類型工作的討論重點是否有明顯的差異，特別是軟體工作及科技工作是否存在一定程度的相似性。\n",
    "\n",
    "+ 分析目標：將三個版別的文章合起來，訓練模型能預測潛在主題\n",
    "\n",
    "+ 步驟：\n",
    "  1. 資料前處理 \n",
    "  2. lexicon-based 的主題模型\n",
    "  3. LDA 主題模型 \n",
    "      - 整理 dictionary 和corpus\n",
    "      - 訓練LDA topic model\n",
    "      - 透過LDA模型指標尋找最佳主題數\n",
    "      - 視覺化呈現分析結果\n",
    "  4. GuidedLDA\n",
    "\n",
    "+ 遇到的困難和解決方式:\n",
    "  - 發現打工版的文章都含有一些相同的警告標語，影響到了後續的分類準確性，將警告標語移除\n",
    "  - 時間跨度上，軟體版的分布較長，會影響分析時的聚焦性:分析時僅看2024之後的\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time \n",
    "from functools import reduce\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import jieba\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from gensim.corpora import Dictionary\n",
    "from gensim.models import LdaModel, CoherenceModel\n",
    "from gensim.models.ldamulticore import LdaMulticore\n",
    "from gensim.matutils import corpus2csc, corpus2dense, Sparse2Corpus\n",
    "\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 資料前處理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>125.229.192.37</td>\n",
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       "      <td>https://www.ptt.cc/bbs/part-time/M.1704077270....</td>\n",
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       "      <td>2024-01-01 10:47:48</td>\n",
       "      <td>bonzi42</td>\n",
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       "      <td>本人同意並願意遵守現行法律、本站使用者條款、本站各級規定、本板所有規範，\\n本人願意為本文內...</td>\n",
       "      <td>[]</td>\n",
       "      <td>118.165.136.221</td>\n",
       "      <td>2024-01-02 02:11:21</td>\n",
       "      <td>ptt</td>\n",
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       "  </tbody>\n",
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       "1          2  https://www.ptt.cc/bbs/part-time/M.1704068173....   \n",
       "2          3  https://www.ptt.cc/bbs/part-time/M.1704077270....   \n",
       "\n",
       "                artTitle              artDate  artPoster artCatagory  \\\n",
       "0        [個人]桃園搬家助手1/1兩位  2024-01-01 00:25:21     snk236   part_time   \n",
       "1  [台北/市調]維他命座談會車馬費1500元  2024-01-01 08:16:11  Portmento   part_time   \n",
       "2           [多區/個人]PPT製作  2024-01-01 10:47:48    bonzi42   part_time   \n",
       "\n",
       "                                          artContent  \\\n",
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       "1  本人同意並願意遵守現行法律、本站使用者條款、本站各級規定、本板所有規範，\\n本人願意為本文內...   \n",
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       "\n",
       "                                          artComment             e_ip  \\\n",
       "0                                                 []    114.136.39.28   \n",
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       "2                                                 []  118.165.136.221   \n",
       "\n",
       "          insertedDate dataSource  \n",
       "0  2024-01-01 02:09:00        ptt  \n",
       "1  2024-01-02 02:11:21        ptt  \n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parttime = pd.read_csv(\"raw_data/ptt_parttime.csv\")  # 匯資料\n",
    "softjob = pd.read_csv(\"raw_data/ptt_softjob.csv\")\n",
    "techjob = pd.read_csv(\"raw_data/ptt_techjob.csv\")\n",
    "data = pd.concat([parttime,softjob,techjob])\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <td>118.165.136.221</td>\n",
       "      <td>2024-01-02 02:11:21</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704078649....</td>\n",
       "      <td>[多區/個人]網頁Logo設計</td>\n",
       "      <td>2024-01-01 11:10:47</td>\n",
       "      <td>ymd124783</td>\n",
       "      <td>part_time</td>\n",
       "      <td>，\\n。\\n，。。\\n★\\n，，。\\n★\\n。\\n。\\n。\\n。\\n。\\n。\\n。\\n工作或...</td>\n",
       "      <td>[]</td>\n",
       "      <td>111.241.75.141</td>\n",
       "      <td>2024-01-02 02:11:21</td>\n",
       "      <td>ptt</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704084465....</td>\n",
       "      <td>[個人]西語電視台徵求攝影師跟拍(學生可)</td>\n",
       "      <td>2024-01-01 12:47:43</td>\n",
       "      <td>addisababa</td>\n",
       "      <td>part_time</td>\n",
       "      <td>，\\n。\\n工作或交件期：台灣大選(1/13)前後與當日，約三日(實際時間可議)\\n預定排班...</td>\n",
       "      <td>[]</td>\n",
       "      <td>116.98.255.131</td>\n",
       "      <td>2024-01-02 02:11:21</td>\n",
       "      <td>ptt</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704085782....</td>\n",
       "      <td>[台北/個人]</td>\n",
       "      <td>2024-01-01 13:09:40</td>\n",
       "      <td>richman888</td>\n",
       "      <td>part_time</td>\n",
       "      <td>，\\n。\\n，。。\\n★\\n，，。\\n★\\n。\\n。\\n。\\n。\\n。\\n。\\n。\\n工作或...</td>\n",
       "      <td>[]</td>\n",
       "      <td>223.138.206.210</td>\n",
       "      <td>2024-01-02 02:11:21</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704097191....</td>\n",
       "      <td>[個人/桃園]幫忙撕除壁貼（僅一面牆）</td>\n",
       "      <td>2024-01-01 16:19:49</td>\n",
       "      <td>Madoona</td>\n",
       "      <td>part_time</td>\n",
       "      <td>，\\n。\\n，。。\\n★\\n，，。\\n★\\n。\\n。\\n。\\n。\\n。\\n。\\n。\\n工作或...</td>\n",
       "      <td>[]</td>\n",
       "      <td>114.140.80.105</td>\n",
       "      <td>2024-01-02 02:11:21</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704101881....</td>\n",
       "      <td>[台北/一般]北車蛋糕店徵寒假與長期工讀生</td>\n",
       "      <td>2024-01-01 17:37:59</td>\n",
       "      <td>jj5481</td>\n",
       "      <td>part_time</td>\n",
       "      <td>，\\n。\\n工作或交件期：寒假期間\\n預定排班方式：輪班制\\n每日工作時間：10:00-22...</td>\n",
       "      <td>[]</td>\n",
       "      <td>118.168.160.165</td>\n",
       "      <td>2024-01-02 02:11:23</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704115238....</td>\n",
       "      <td>[高雄/個人]高樹1/3-1/4安裝工人2名2500</td>\n",
       "      <td>2024-01-01 21:20:36</td>\n",
       "      <td>cocawowa</td>\n",
       "      <td>part_time</td>\n",
       "      <td>，\\n。\\n，。。\\n★\\n，，。\\n★\\n。\\n。\\n。\\n。\\n。\\n。\\n。\\n工作或...</td>\n",
       "      <td>[{\"cmtStatus\": \"推\", \"cmtPoster\": \"a00000763\", ...</td>\n",
       "      <td>36.239.1.221</td>\n",
       "      <td>2024-01-02 02:11:23</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704116155....</td>\n",
       "      <td>[台北/一般]中山區魔術酒吧PT</td>\n",
       "      <td>2024-01-01 21:35:53</td>\n",
       "      <td>seoegg2</td>\n",
       "      <td>part_time</td>\n",
       "      <td>，\\n。\\n，，。\\n★\\n工作或交件期：長期-周一至周六\\n預定排班方式：月排班\\n每日工...</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-01-02 02:11:23</td>\n",
       "      <td>ptt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   system_id                                             artUrl  \\\n",
       "0          1  https://www.ptt.cc/bbs/part-time/M.1704039923....   \n",
       "1          2  https://www.ptt.cc/bbs/part-time/M.1704068173....   \n",
       "2          3  https://www.ptt.cc/bbs/part-time/M.1704077270....   \n",
       "3          4  https://www.ptt.cc/bbs/part-time/M.1704078649....   \n",
       "4          5  https://www.ptt.cc/bbs/part-time/M.1704084465....   \n",
       "5          6  https://www.ptt.cc/bbs/part-time/M.1704085782....   \n",
       "6          7  https://www.ptt.cc/bbs/part-time/M.1704097191....   \n",
       "7          8  https://www.ptt.cc/bbs/part-time/M.1704101881....   \n",
       "8          9  https://www.ptt.cc/bbs/part-time/M.1704115238....   \n",
       "9         10  https://www.ptt.cc/bbs/part-time/M.1704116155....   \n",
       "\n",
       "                     artTitle              artDate   artPoster artCatagory  \\\n",
       "0             [個人]桃園搬家助手1/1兩位  2024-01-01 00:25:21      snk236   part_time   \n",
       "1       [台北/市調]維他命座談會車馬費1500元  2024-01-01 08:16:11   Portmento   part_time   \n",
       "2                [多區/個人]PPT製作  2024-01-01 10:47:48     bonzi42   part_time   \n",
       "3             [多區/個人]網頁Logo設計  2024-01-01 11:10:47   ymd124783   part_time   \n",
       "4       [個人]西語電視台徵求攝影師跟拍(學生可)  2024-01-01 12:47:43  addisababa   part_time   \n",
       "5                     [台北/個人]  2024-01-01 13:09:40  richman888   part_time   \n",
       "6         [個人/桃園]幫忙撕除壁貼（僅一面牆）  2024-01-01 16:19:49     Madoona   part_time   \n",
       "7       [台北/一般]北車蛋糕店徵寒假與長期工讀生  2024-01-01 17:37:59      jj5481   part_time   \n",
       "8  [高雄/個人]高樹1/3-1/4安裝工人2名2500  2024-01-01 21:20:36    cocawowa   part_time   \n",
       "9            [台北/一般]中山區魔術酒吧PT  2024-01-01 21:35:53     seoegg2   part_time   \n",
       "\n",
       "                                          artContent  \\\n",
       "0  ，\\n。\\n\\n，\\n。★，。\\n\\n，，。\\n\\n\\n工作日期：113.1.1\\n每日工作...   \n",
       "1  ，\\n。\\n★\\n#14961 [台北市] 維他命座談會\\n車馬費1500元\\nhttps:...   \n",
       "2  ，\\n。\\n，。。\\n★\\n，，。\\n★\\n。\\n。\\n。\\n。\\n。\\n。\\n。\\n工作或...   \n",
       "3  ，\\n。\\n，。。\\n★\\n，，。\\n★\\n。\\n。\\n。\\n。\\n。\\n。\\n。\\n工作或...   \n",
       "4  ，\\n。\\n工作或交件期：台灣大選(1/13)前後與當日，約三日(實際時間可議)\\n預定排班...   \n",
       "5  ，\\n。\\n，。。\\n★\\n，，。\\n★\\n。\\n。\\n。\\n。\\n。\\n。\\n。\\n工作或...   \n",
       "6  ，\\n。\\n，。。\\n★\\n，，。\\n★\\n。\\n。\\n。\\n。\\n。\\n。\\n。\\n工作或...   \n",
       "7  ，\\n。\\n工作或交件期：寒假期間\\n預定排班方式：輪班制\\n每日工作時間：10:00-22...   \n",
       "8  ，\\n。\\n，。。\\n★\\n，，。\\n★\\n。\\n。\\n。\\n。\\n。\\n。\\n。\\n工作或...   \n",
       "9  ，\\n。\\n，，。\\n★\\n工作或交件期：長期-周一至周六\\n預定排班方式：月排班\\n每日工...   \n",
       "\n",
       "                                          artComment             e_ip  \\\n",
       "0                                                 []    114.136.39.28   \n",
       "1  [{\"cmtStatus\": \"噓\", \"cmtPoster\": \"GUANGLEI\", \"...   125.229.192.37   \n",
       "2                                                 []  118.165.136.221   \n",
       "3                                                 []   111.241.75.141   \n",
       "4                                                 []   116.98.255.131   \n",
       "5                                                 []  223.138.206.210   \n",
       "6                                                 []   114.140.80.105   \n",
       "7                                                 []  118.168.160.165   \n",
       "8  [{\"cmtStatus\": \"推\", \"cmtPoster\": \"a00000763\", ...     36.239.1.221   \n",
       "9                                                 []              NaN   \n",
       "\n",
       "          insertedDate dataSource  \n",
       "0  2024-01-01 02:09:00        ptt  \n",
       "1  2024-01-02 02:11:21        ptt  \n",
       "2  2024-01-02 02:11:21        ptt  \n",
       "3  2024-01-02 02:11:21        ptt  \n",
       "4  2024-01-02 02:11:21        ptt  \n",
       "5  2024-01-02 02:11:21        ptt  \n",
       "6  2024-01-02 02:11:21        ptt  \n",
       "7  2024-01-02 02:11:23        ptt  \n",
       "8  2024-01-02 02:11:23        ptt  \n",
       "9  2024-01-02 02:11:23        ptt  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 移除警告標語\n",
    "warns = [\"本人同意並願意遵守現行法律、本站使用者條款、本站各級規定、本板所有規範\",\n",
    "         \"本人願意為本文內容負責，並保証本文內容皆詳盡屬實，若違反相關規範，願受處分\",\n",
    "         \"誤刪者應至本板使用規範第37條或z-53-3複製範本\",\n",
    "         \"提醒：上方二行文字不得刪除或變更\",\n",
    "         \"違者文章逕行刪除\",\n",
    "         \"本行提醒得Ctrl+Y刪除之\",\n",
    "         \"誤刪者應至本板使用規範第37條或z-53-3複製範本\",\n",
    "         \"為必填項目，缺項應保留空項目名稱，灰色文字得刪除之\",\n",
    "         \"各項均不得為「面議」\",\n",
    "         \"本文僅授權發表於PTT實業坊\",\n",
    "         \"未經同意不得轉載至其它網站\",\n",
    "         \"本人保留一切訴訟權\",\n",
    "         \"否則得視情況提出告訴\",\n",
    "         \"承攬制等不適用排班、休息之工作者僅填第一項「交件期」，其餘項留空白\",\n",
    "         \"一次性工作且未滿四小時者，得將全部資訊填於第一項，其餘項留空白\",\n",
    "         \"不定期工作，第一項「工作期」應填「長期」及可開始工作日\",\n",
    "         \"第二項「排班方式」應填每週或每月何日出勤(休息)，或現場排班等，一次性工作留空\",\n",
    "         \"第三、四項「工作時間」「休息時間」得合併至第三項，第四項留空白\",\n",
    "         \"第四項「休息時間」、第五項「休息計薪供餐」依實際情形填寫之(第五項擇一)\",\n",
    "         \"任一項僅寫「面議」或同義文字者，一律水桶一年並退文\"]\n",
    "\n",
    "\n",
    "for warn in warns:\n",
    "    data[\"artContent\"] = data[\"artContent\"].str.replace(warn, \"\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Art content na number : 22\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>artUrl</th>\n",
       "      <th>artCatagory</th>\n",
       "      <th>artDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>工作日期每日工作時間每日休息時間無休息有無計薪供餐無平常日工資一次應為時薪或日薪不得為月薪計...</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704039923....</td>\n",
       "      <td>part_time</td>\n",
       "      <td>2024-01-01 00:25:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>台北市維他命座談會車馬費元民國年次女性目前主要使用的維他命產品為善存不包含銀寶善存克補專科大...</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704068173....</td>\n",
       "      <td>part_time</td>\n",
       "      <td>2024-01-01 08:16:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>工作或交件期年早上前將內容檔案傳給你下午前收件預定排班方式每日工作時間每日休息時間工作滿小時...</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704077270....</td>\n",
       "      <td>part_time</td>\n",
       "      <td>2024-01-01 10:47:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>工作或交件期晚上以前預定排班方式自行安排每日工作時間自行安排每日休息時間無休息計薪供餐皆無以...</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704078649....</td>\n",
       "      <td>part_time</td>\n",
       "      <td>2024-01-01 11:10:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>工作或交件期台灣大選前後與當日約三日實際時間可議預定排班方式無每日工作時間八小時以內視記者採...</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704084465....</td>\n",
       "      <td>part_time</td>\n",
       "      <td>2024-01-01 12:47:43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content  \\\n",
       "0  工作日期每日工作時間每日休息時間無休息有無計薪供餐無平常日工資一次應為時薪或日薪不得為月薪計...   \n",
       "1  台北市維他命座談會車馬費元民國年次女性目前主要使用的維他命產品為善存不包含銀寶善存克補專科大...   \n",
       "2  工作或交件期年早上前將內容檔案傳給你下午前收件預定排班方式每日工作時間每日休息時間工作滿小時...   \n",
       "3  工作或交件期晚上以前預定排班方式自行安排每日工作時間自行安排每日休息時間無休息計薪供餐皆無以...   \n",
       "4  工作或交件期台灣大選前後與當日約三日實際時間可議預定排班方式無每日工作時間八小時以內視記者採...   \n",
       "\n",
       "                                              artUrl artCatagory  \\\n",
       "0  https://www.ptt.cc/bbs/part-time/M.1704039923....   part_time   \n",
       "1  https://www.ptt.cc/bbs/part-time/M.1704068173....   part_time   \n",
       "2  https://www.ptt.cc/bbs/part-time/M.1704077270....   part_time   \n",
       "3  https://www.ptt.cc/bbs/part-time/M.1704078649....   part_time   \n",
       "4  https://www.ptt.cc/bbs/part-time/M.1704084465....   part_time   \n",
       "\n",
       "              artDate  \n",
       "0 2024-01-01 00:25:21  \n",
       "1 2024-01-01 08:16:11  \n",
       "2 2024-01-01 10:47:48  \n",
       "3 2024-01-01 11:10:47  \n",
       "4 2024-01-01 12:47:43  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 移除空文章\n",
    "print(f\"Art content na number : {data['artContent'].isna().sum()}\")\n",
    "data.dropna(subset=\"artContent\",inplace=True)\n",
    "data.reset_index(inplace=True,drop=True)\n",
    "\n",
    "# 移除網址\n",
    "data[\"artContent\"] = data[\"artContent\"].str.replace(\"(http|https)://.*\", \"\", regex=True)\n",
    "data[\"artTitle\"] = data[\"artTitle\"].str.replace(\"(http|https)://.*\", \"\", regex=True)\n",
    "\n",
    "# 只保留中文字（去除非中文字，包括英數符號）\n",
    "data[\"artContent\"] = data[\"artContent\"].str.replace(\"[^\\u4e00-\\u9fa5]\", \"\", regex=True)\n",
    "data[\"artTitle\"] = data[\"artTitle\"].str.replace(\"[^\\u4e00-\\u9fa5]\", \"\", regex=True)\n",
    "\n",
    "# 日期轉換與欄位整理\n",
    "data['artDate'] = pd.to_datetime(data['artDate'])\n",
    "data['content'] = data['artContent']\n",
    "\n",
    "# 只保留需要的欄位\n",
    "data = data.loc[:, [\"content\", \"artUrl\", \"artCatagory\", 'artDate']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16310"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from /Users/wengwulin/Desktop/社群媒體分析/讀書會專案/第二次讀書會專案/dict/dict.txt.big ...\n",
      "2025-04-19 13:27:58,807 : DEBUG : Building prefix dict from /Users/wengwulin/Desktop/社群媒體分析/讀書會專案/第二次讀書會專案/dict/dict.txt.big ...\n",
      "Loading model from cache /var/folders/tz/hplj27qd26n9qxr1cd83m32c0000gn/T/jieba.ude891b01a373b1226a34d91c1ca0b7f6.cache\n",
      "2025-04-19 13:27:58,809 : DEBUG : Loading model from cache /var/folders/tz/hplj27qd26n9qxr1cd83m32c0000gn/T/jieba.ude891b01a373b1226a34d91c1ca0b7f6.cache\n",
      "Loading model cost 0.590 seconds.\n",
      "2025-04-19 13:27:59,398 : DEBUG : Loading model cost 0.590 seconds.\n",
      "Prefix dict has been built successfully.\n",
      "2025-04-19 13:27:59,399 : DEBUG : Prefix dict has been built successfully.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>artUrl</th>\n",
       "      <th>artCatagory</th>\n",
       "      <th>artDate</th>\n",
       "      <th>words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>工作日期每日工作時間每日休息時間無休息有無計薪供餐無平常日工資一次應為時薪或日薪不得為月薪計...</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704039923....</td>\n",
       "      <td>part_time</td>\n",
       "      <td>2024-01-01 00:25:21</td>\n",
       "      <td>[工作, 日期, 每日, 工作, 時間, 每日, 休息時間, 休息, 計薪, 供餐, 平常,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>台北市維他命座談會車馬費元民國年次女性目前主要使用的維他命產品為善存不包含銀寶善存克補專科大...</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704068173....</td>\n",
       "      <td>part_time</td>\n",
       "      <td>2024-01-01 08:16:11</td>\n",
       "      <td>[台北市, 維他命, 座談會, 車馬費, 民國, 年次, 女性, 目前, 主要, 使用, 維...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>工作或交件期年早上前將內容檔案傳給你下午前收件預定排班方式每日工作時間每日休息時間工作滿小時...</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704077270....</td>\n",
       "      <td>part_time</td>\n",
       "      <td>2024-01-01 10:47:48</td>\n",
       "      <td>[工作, 交件, 期年, 早上, 前將, 內容, 檔案, 傳給, 下午, 收件, 預定, 排...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>工作或交件期晚上以前預定排班方式自行安排每日工作時間自行安排每日休息時間無休息計薪供餐皆無以...</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704078649....</td>\n",
       "      <td>part_time</td>\n",
       "      <td>2024-01-01 11:10:47</td>\n",
       "      <td>[工作, 交件, 晚上, 以前, 預定, 排班, 方式, 自行安排, 每日, 工作, 時間,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>工作或交件期台灣大選前後與當日約三日實際時間可議預定排班方式無每日工作時間八小時以內視記者採...</td>\n",
       "      <td>https://www.ptt.cc/bbs/part-time/M.1704084465....</td>\n",
       "      <td>part_time</td>\n",
       "      <td>2024-01-01 12:47:43</td>\n",
       "      <td>[工作, 交件, 期台灣, 大選, 當日, 三日, 實際, 時間, 可議, 預定, 排班, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content  \\\n",
       "0  工作日期每日工作時間每日休息時間無休息有無計薪供餐無平常日工資一次應為時薪或日薪不得為月薪計...   \n",
       "1  台北市維他命座談會車馬費元民國年次女性目前主要使用的維他命產品為善存不包含銀寶善存克補專科大...   \n",
       "2  工作或交件期年早上前將內容檔案傳給你下午前收件預定排班方式每日工作時間每日休息時間工作滿小時...   \n",
       "3  工作或交件期晚上以前預定排班方式自行安排每日工作時間自行安排每日休息時間無休息計薪供餐皆無以...   \n",
       "4  工作或交件期台灣大選前後與當日約三日實際時間可議預定排班方式無每日工作時間八小時以內視記者採...   \n",
       "\n",
       "                                              artUrl artCatagory  \\\n",
       "0  https://www.ptt.cc/bbs/part-time/M.1704039923....   part_time   \n",
       "1  https://www.ptt.cc/bbs/part-time/M.1704068173....   part_time   \n",
       "2  https://www.ptt.cc/bbs/part-time/M.1704077270....   part_time   \n",
       "3  https://www.ptt.cc/bbs/part-time/M.1704078649....   part_time   \n",
       "4  https://www.ptt.cc/bbs/part-time/M.1704084465....   part_time   \n",
       "\n",
       "              artDate                                              words  \n",
       "0 2024-01-01 00:25:21  [工作, 日期, 每日, 工作, 時間, 每日, 休息時間, 休息, 計薪, 供餐, 平常,...  \n",
       "1 2024-01-01 08:16:11  [台北市, 維他命, 座談會, 車馬費, 民國, 年次, 女性, 目前, 主要, 使用, 維...  \n",
       "2 2024-01-01 10:47:48  [工作, 交件, 期年, 早上, 前將, 內容, 檔案, 傳給, 下午, 收件, 預定, 排...  \n",
       "3 2024-01-01 11:10:47  [工作, 交件, 晚上, 以前, 預定, 排班, 方式, 自行安排, 每日, 工作, 時間,...  \n",
       "4 2024-01-01 12:47:43  [工作, 交件, 期台灣, 大選, 當日, 三日, 實際, 時間, 可議, 預定, 排班, ...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 設定繁體中文詞庫\n",
    "jieba.set_dictionary(\"./dict/dict.txt.big\")\n",
    "\n",
    "# 新增stopwords\n",
    "# jieba.analyse.set_stop_words('./dict/stop_words.txt') #jieba.analyse.extract_tags才會作用\n",
    "with open(\"./dict/stop_words.txt\", encoding=\"utf-8\") as f:\n",
    "    stopWords = [line.strip() for line in f.readlines()]\n",
    "\n",
    "# 設定斷詞 function\n",
    "def getToken(row):\n",
    "    seg_list = jieba.cut(row, cut_all=False)\n",
    "    seg_list = [\n",
    "        w for w in seg_list if w not in stopWords and len(w) > 1\n",
    "    ]  # 篩選掉停用字與字元數小於等於1的詞彙\n",
    "    return seg_list\n",
    "\n",
    "data[\"words\"] = data[\"content\"].apply(getToken)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Lexicon-based / 人工給定主題的主題模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['part_time', 'Soft_Job', 'Tech_Job'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['artCatagory'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用各個主題常見的詞來作為主題的代表詞"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['工作', '方式', '推定', '內容', '聯絡', '砍除', '空白', '情形', '單位', '應徵', '資訊', '第一項',\n",
       "       '國定假日', '工資', '文字', '未註明', '聯絡人', '發薪日', '小時', '依法', '連結', '水桶', '表示',\n",
       "       '承攬', '分類', '時間', '簡介', '一律', '刪除', '特殊', '物品', '標題', '內信', '形式',\n",
       "       '電子郵件', '回覆', '中文', '行動', '市內電話', '地點', '現領', '每日', '規定', '報酬', '條件',\n",
       "       '以上', '休息', '同義', '第二項', '日時', '工時', '應徵者', '延長', '是否', '勞健', '注意',\n",
       "       '法定', '此行', '基本工資', '徵才', '通知', '項目', '縣市', '相同', '全名', '變更', '標籤',\n",
       "       '開頭', '代取', '表單', '一年', '地址', '帳號', '網站', '自然人', '僅有', '人力資源', '第二',\n",
       "       '一次性', '三項', '職缺', '諸如', '七日', '星期六', '載明', '第五項', '退文', '再有', '公司',\n",
       "       '人數', '保勞退', '排班', '學校', '電話', '面試', '計薪', '提供', '需求', '亦可', '供餐'],\n",
       "      dtype='object', name='words')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 定義主題\n",
    "part_time = data.loc[data['artCatagory'] == 'part_time',:]['words'].explode().value_counts().head(100)\n",
    "part_time.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['公司', '工作', '面試', '問題', '時間', '開發', '工程師', '經驗', '目前', '比較', '技術', '軟體',\n",
       "       '覺得', '團隊', '程式', '知道', '需要', '相關', '產品', '一些', '使用', '薪資', '能力', '系統',\n",
       "       '職缺', '方式', '內容', '真的', '應該', '資料', '台灣', '專案', '現在', '主管', '一下', '學習',\n",
       "       '主要', '是否', '直接', '東西', '資訊', '年薪', '員工', '希望', '機會', '分享', '履歷', '小時',\n",
       "       '提供', '最後', '之後', '熟悉', '設計', '工具', '前端', '語言', '薪水', '以上', '需求', '已經',\n",
       "       '建議', '介紹', '討論', '環境', '服務', '看到', '流程', '感覺', '功能', '經歷', '未來', '興趣',\n",
       "       '英文', '了解', '要求', '部分', '處理', '一定', '面試官', '課程', '進行', '測試', '科技',\n",
       "       '加班費', '工時', '網站', '畢業', '重要', '準備', '最近', '小弟', '來說', '選擇', '簡單', '一點',\n",
       "       '前輩', '管理', '新創', '領域', '架構'],\n",
       "      dtype='object', name='words')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 定義主題\n",
    "soft_job = data.loc[data['artCatagory'] == 'Soft_Job',:]['words'].explode().value_counts().head(100)\n",
    "soft_job.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['公司', '台灣', '美國', '工作', '晶片', '表示', '員工', '半導體', '科技', '台積電', '中國',\n",
       "       '技術', '產業', '報導', '台積', '工程師', '英特爾', '市場', '目前', '全球', '發展', '未來',\n",
       "       '問題', '企業', '投資', '積電', '指出', '時間', '現在', '相關', '今年', '產品', '製程', '需要',\n",
       "       '主管', '提供', '合作', '輝達', '客戶', '先進', '億美元', '製造', '已經', '日本', '記者', '認為',\n",
       "       '生產', '設計', '進行', '領域', '需求', '知道', '人才', '薪資', '面試', '去年', '研發', '模型',\n",
       "       '研究', '資料', '真的', '使用', '應該', '影響', '包括', '成長', '能力', '蘋果', '機會', '比較',\n",
       "       '主要', '系統', '設備', '持續', '中心', '人工智慧', '開發', '億元', '重要', '希望', '政府',\n",
       "       '仁勳', '三星', '預計', '超過', '計畫', '年薪', '透過', '宣布', '台北', '直接', '執行長', '國家',\n",
       "       '董事長', '代工', '應用', '覺得', '大學', '內容', '業務'],\n",
       "      dtype='object', name='words')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 定義主題\n",
    "tech_job = data.loc[data['artCatagory'] == 'Tech_Job',:]['words'].explode().value_counts().head(100)\n",
    "tech_job.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 從 custom_topic_word 的所有值中，整理出不重複的詞彙（vocabularies），並以 NumPy 陣列的形式儲存。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['一下', '一些', '一定', '一年', '一律', '一次性', '一點', '七日', '三星', '三項', '中國',\n",
       "       '中心', '中文', '主管', '主要', '之後', '了解', '亦可', '人力資源', '人工智慧', '人數',\n",
       "       '仁勳', '今年', '介紹', '代取', '代工', '以上', '企業', '休息', '使用', '供餐', '依法',\n",
       "       '保勞退', '僅有', '億元', '億美元', '先進', '內信', '內容', '全名', '全球', '公司', '再有',\n",
       "       '分享', '分類', '刪除', '前端', '前輩', '功能', '加班費', '勞健', '包括', '去年', '台北',\n",
       "       '台灣', '台積電', '合作', '同義', '員工', '問題', '單位', '回覆', '國定假日', '國家',\n",
       "       '團隊', '地址', '地點', '執行長', '基本工資', '報酬', '大學', '學校', '學習', '客戶',\n",
       "       '宣布', '專案', '小時', '履歷', '工作', '工具', '工時', '工程師', '工資', '已經',\n",
       "       '市內電話', '市場', '希望', '帳號', '年薪', '延長', '建議', '形式', '影響', '徵才', '情形',\n",
       "       '感覺', '應徵', '應徵者', '應用', '應該', '成長', '承攬', '技術', '投資', '持續', '指出',\n",
       "       '排班', '推定', '提供', '政府', '文字', '新創', '方式', '日時', '日本', '星期六', '是否',\n",
       "       '時間', '晶片', '最後', '最近', '未來', '未註明', '東西', '架構', '條件', '標籤', '標題',\n",
       "       '模型', '機會', '此行', '每日', '比較', '水桶', '法定', '注意', '流程', '測試', '準備',\n",
       "       '熟悉', '物品', '特殊', '現在', '現領', '環境', '生產', '產品', '畢業', '發展', '發薪日',\n",
       "       '目前', '直接', '相同', '相關', '真的', '知道', '砍除', '研發', '研究', '科技', '程式',\n",
       "       '積電', '空白', '第一項', '第二', '第二項', '第五項', '管理', '簡介', '簡單', '系統',\n",
       "       '經歷', '經驗', '網站', '縣市', '聯絡', '聯絡人', '職缺', '能力', '自然人', '興趣', '英文',\n",
       "       '英特爾', '董事長', '薪水', '薪資', '蘋果', '處理', '行動', '表單', '表示', '製程', '製造',\n",
       "       '要求', '規定', '覺得', '計畫', '計薪', '討論', '記者', '設備', '設計', '認為', '語言',\n",
       "       '課程', '諸如', '變更', '資料', '資訊', '超過', '軟體', '載明', '輝達', '退文', '透過',\n",
       "       '通知', '連結', '進行', '選擇', '部分', '重要', '開發', '開頭', '電子郵件', '電話', '需求',\n",
       "       '需要', '面試', '面試官', '項目', '預計', '領域'], dtype='<U4')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "custom_topic_word = {\n",
    "    \"打工\": [\n",
    "        \"工作\", \"方式\", \"推定\", \"內容\", \"聯絡\", \"砍除\", \"空白\", \"情形\", \"單位\", \"應徵\", \"資訊\", \"第一項\",\n",
    "        \"國定假日\", \"工資\", \"文字\", \"未註明\", \"聯絡人\", \"發薪日\", \"小時\", \"依法\", \"連結\", \"水桶\", \"表示\",\n",
    "        \"承攬\", \"分類\", \"時間\", \"簡介\", \"一律\", \"刪除\", \"特殊\", \"物品\", \"標題\", \"內信\", \"形式\",\n",
    "        \"電子郵件\", \"回覆\", \"中文\", \"行動\", \"市內電話\", \"地點\", \"現領\", \"每日\", \"規定\", \"報酬\", \"條件\",\n",
    "        \"以上\", \"休息\", \"同義\", \"第二項\", \"日時\", \"工時\", \"應徵者\", \"延長\", \"是否\", \"勞健\", \"注意\",\n",
    "        \"法定\", \"此行\", \"基本工資\", \"徵才\", \"通知\", \"項目\", \"縣市\", \"相同\", \"全名\", \"變更\", \"標籤\",\n",
    "        \"開頭\", \"代取\", \"表單\", \"一年\", \"地址\", \"帳號\", \"網站\", \"自然人\", \"僅有\", \"人力資源\", \"第二\",\n",
    "        \"一次性\", \"三項\", \"職缺\", \"諸如\", \"七日\", \"星期六\", \"載明\", \"第五項\", \"退文\", \"再有\", \"公司\",\n",
    "        \"人數\", \"保勞退\", \"排班\", \"學校\", \"電話\", \"面試\", \"計薪\", \"提供\", \"需求\", \"亦可\", \"供餐\"\n",
    "    ],\n",
    "    \"軟體工作\": [\n",
    "        \"問題\", \"開發\", \"工程師\", \"經驗\", \"目前\", \"比較\", \"技術\", \"軟體\", \"覺得\", \"團隊\", \"程式\", \"知道\",\n",
    "        \"需要\", \"相關\", \"產品\", \"一些\", \"使用\", \"薪資\", \"能力\", \"系統\", \"方式\", \"真的\", \"應該\", \"資料\",\n",
    "        \"台灣\", \"專案\", \"現在\", \"主管\", \"一下\", \"學習\", \"主要\", \"直接\", \"東西\", \"年薪\", \"員工\", \"希望\",\n",
    "        \"機會\", \"分享\", \"履歷\", \"最後\", \"之後\", \"熟悉\", \"設計\", \"工具\", \"前端\", \"語言\", \"薪水\", \"已經\",\n",
    "        \"建議\", \"介紹\", \"討論\", \"環境\", \"流程\", \"感覺\", \"功能\", \"經歷\", \"未來\", \"興趣\", \"英文\", \"了解\",\n",
    "        \"要求\", \"部分\", \"處理\", \"一定\", \"面試官\", \"課程\", \"進行\", \"測試\", \"科技\", \"加班費\", \"畢業\",\n",
    "        \"重要\", \"準備\", \"最近\",  \"選擇\", \"簡單\", \"一點\", \"前輩\", \"管理\", \"新創\", \"領域\", \"架構\"\n",
    "    ],\n",
    "    \"科技業\": [\n",
    "        \"晶片\", \"台積電\", \"中國\", \"英特爾\", \"市場\", \"全球\", \"發展\", \"企業\", \"投資\", \"積電\", \"指出\", \"今年\",\n",
    "        \"製程\", \"合作\", \"輝達\", \"客戶\", \"先進\", \"億美元\", \"製造\", \"日本\", \"記者\", \"認為\", \"生產\", \"人工智慧\",\n",
    "        \"去年\", \"研發\", \"模型\", \"研究\", \"影響\", \"包括\", \"成長\", \"蘋果\", \"設備\", \"持續\", \"中心\", \"億元\", \"政府\",\n",
    "        \"仁勳\", \"三星\", \"預計\", \"超過\", \"計畫\", \"透過\", \"宣布\", \"台北\", \"執行長\", \"國家\", \"董事長\", \"代工\",\n",
    "        \"應用\", \"大學\"\n",
    "    ],\n",
    "}\n",
    "\n",
    "vocabularies = np.unique(reduce(lambda x, y: x + y, custom_topic_word.values()))\n",
    "vocabularies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 利用 CountVectorizer 的 vocabulary 參數來篩選、製作 DTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       一下  一些  一定  一年  一律  一次性  一點  七日  三星  三項  ...  開頭  電子郵件  電話  需求  需要  面試  \\\n",
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       "...    ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ...  ..   ...  ..  ..  ..  ..   \n",
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       "\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corpus = data['words'].map(\" \".join)\n",
    "vectorizer = CountVectorizer(vocabulary=vocabularies)\n",
    "data_matrix = vectorizer.fit_transform(data_corpus)\n",
    "\n",
    "data_matrix = data_matrix.toarray()\n",
    "feature_names = vectorizer.get_feature_names_out()\n",
    "\n",
    "DTM_df = pd.DataFrame(columns = feature_names, data = data_matrix)\n",
    "DTM_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 針對每個「自定義主題」，統計每篇文章在該主題下出現的詞彙總次數，整理成一個DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       topic_打工  topic_軟體工作  topic_科技業\n",
       "0            51           1          0\n",
       "1            25          16          3\n",
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       "4            69          12          5\n",
       "...         ...         ...        ...\n",
       "16305         5          12         15\n",
       "16306         2           4          2\n",
       "16307         1           1         11\n",
       "16308         2          19         23\n",
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     },
     "metadata": {},
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   ],
   "source": [
    "df_count = pd.DataFrame({})\n",
    "\n",
    "# k 是主題名稱，v 是這個主題下的詞彙list\n",
    "for k, v in custom_topic_word.items():\n",
    "    idx = np.isin(\n",
    "        feature_names,\n",
    "        v\n",
    "    )\n",
    "    df_count[f'topic_{k}'] = data_matrix[:, idx].sum(axis=1)\n",
    "    \n",
    "df_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 每篇文章在各個主題的出現次數轉換成主題分佈機率（比例）\n",
    "也就是 每篇文章屬於各個主題的相對權重（theta 向量）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "   topic_打工  topic_軟體工作  topic_科技業\n",
       "0  0.980769    0.019231   0.000000\n",
       "1  0.568182    0.363636   0.068182\n",
       "2  0.955631    0.044369   0.000000\n",
       "3  0.936620    0.063380   0.000000\n",
       "4  0.802326    0.139535   0.058140"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thetas = df_count.div(\n",
    "    df_count.sum(axis=1),\n",
    "    axis=0\n",
    ")\n",
    "thetas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. LDA 主題模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 整理 Dictionary 和 corpus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 將斷詞後的`doc['words']`轉換成list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['工作',\n",
       " '日期',\n",
       " '每日',\n",
       " '工作',\n",
       " '時間',\n",
       " '每日',\n",
       " '休息時間',\n",
       " '休息',\n",
       " '計薪',\n",
       " '供餐',\n",
       " '平常',\n",
       " '工資',\n",
       " '一次',\n",
       " '應為',\n",
       " '時薪',\n",
       " '日薪',\n",
       " '月薪',\n",
       " '計件',\n",
       " '制者',\n",
       " '註明',\n",
       " '常人',\n",
       " '平均',\n",
       " '每件',\n",
       " '需工',\n",
       " '國定假日',\n",
       " '工資',\n",
       " '無應',\n",
       " '約定',\n",
       " '之倍',\n",
       " '以上',\n",
       " '補休',\n",
       " '留空',\n",
       " '視為',\n",
       " '依法',\n",
       " '規定',\n",
       " '延長',\n",
       " '工時',\n",
       " '工資',\n",
       " '小時',\n",
       " '至少',\n",
       " '上開',\n",
       " '約定',\n",
       " '之倍',\n",
       " '小時',\n",
       " '留空',\n",
       " '視為',\n",
       " '依法',\n",
       " '規定',\n",
       " '勞健',\n",
       " '保勞退',\n",
       " '依法',\n",
       " '規定',\n",
       " '承攬',\n",
       " '制之',\n",
       " '工作',\n",
       " '應填',\n",
       " '依法',\n",
       " '規定',\n",
       " '工資',\n",
       " '發放',\n",
       " '工作',\n",
       " '現領',\n",
       " '工作',\n",
       " '內容',\n",
       " '工作',\n",
       " '地點',\n",
       " '填寫',\n",
       " '完整',\n",
       " '地址',\n",
       " '戶政',\n",
       " '最小',\n",
       " '單位',\n",
       " '相對',\n",
       " '位置',\n",
       " '外包',\n",
       " '承攬',\n",
       " '制得',\n",
       " '留空',\n",
       " '工作',\n",
       " '地點',\n",
       " '私人',\n",
       " '住宅',\n",
       " '得僅',\n",
       " '縣市',\n",
       " '鄉鎮',\n",
       " '市區',\n",
       " '勞務',\n",
       " '內容',\n",
       " '確實',\n",
       " '填寫',\n",
       " '含糊',\n",
       " '工作',\n",
       " '地點',\n",
       " '桃園',\n",
       " '新生路',\n",
       " '永安',\n",
       " '路口',\n",
       " '電梯',\n",
       " '大樓',\n",
       " '勞務',\n",
       " '內容',\n",
       " '兩位',\n",
       " '貨車',\n",
       " '標準',\n",
       " '雙人',\n",
       " '床上',\n",
       " '墊下',\n",
       " '電梯',\n",
       " '大樓',\n",
       " '六樓',\n",
       " '主臥室',\n",
       " '主臥',\n",
       " '標準',\n",
       " '雙人',\n",
       " '床上',\n",
       " '墊下',\n",
       " '墊移',\n",
       " '隔壁',\n",
       " '原有',\n",
       " '兩張',\n",
       " '標準',\n",
       " '雙人',\n",
       " '電梯',\n",
       " '搬到',\n",
       " '樓門口',\n",
       " '聯絡人',\n",
       " '李先生',\n",
       " '聯絡',\n",
       " '方式',\n",
       " '回覆',\n",
       " '應徵者',\n",
       " '僅回',\n",
       " '錄取']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "docs = data['words'].to_list()\n",
    "docs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建立並過濾詞彙表（dictionary），只保留特定條件的詞彙"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-04-19 16:02:48,857 : INFO : adding document #0 to Dictionary<0 unique tokens: []>\n",
      "2025-04-19 16:02:49,742 : INFO : adding document #10000 to Dictionary<58700 unique tokens: ['一次', '上開', '主臥', '主臥室', '之倍']...>\n",
      "2025-04-19 16:02:50,107 : INFO : built Dictionary<101854 unique tokens: ['一次', '上開', '主臥', '主臥室', '之倍']...> from 16310 documents (total 3597554 corpus positions)\n",
      "2025-04-19 16:02:50,107 : INFO : Dictionary lifecycle event {'msg': \"built Dictionary<101854 unique tokens: ['一次', '上開', '主臥', '主臥室', '之倍']...> from 16310 documents (total 3597554 corpus positions)\", 'datetime': '2025-04-19T16:02:50.107747', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n",
      "2025-04-19 16:02:50,143 : INFO : discarding 78593 tokens: [('主臥', 2), ('主臥室', 3), ('墊移', 1), ('新生路', 4), ('存克補', 1), ('為善存', 1), ('銀寶善', 1), ('具短', 1), ('每頁', 1), ('進投', 1)]...\n",
      "2025-04-19 16:02:50,144 : INFO : keeping 23261 tokens which were in no less than 5 and no more than 16146 (=99.0%) documents\n",
      "2025-04-19 16:02:50,168 : INFO : resulting dictionary: Dictionary<23261 unique tokens: ['一次', '上開', '之倍', '以上', '休息']...>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary<23261 unique tokens: ['一次', '上開', '之倍', '以上', '休息']...>\n"
     ]
    }
   ],
   "source": [
    "dictionary = Dictionary(docs)\n",
    "\n",
    "dictionary.filter_extremes(no_below=5, no_above=0.99)\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "一次: 0\n",
      "上開: 1\n",
      "之倍: 2\n",
      "以上: 3\n",
      "休息: 4\n",
      "休息時間: 5\n",
      "位置: 6\n",
      "住宅: 7\n",
      "供餐: 8\n",
      "依法: 9\n",
      "保勞退: 10\n",
      "僅回: 11\n"
     ]
    }
   ],
   "source": [
    "for idx, (k, v) in enumerate(dictionary.token2id.items()):\n",
    "    print(f\"{k}: {v}\")\n",
    "    if idx > 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('台北市 優格 口味 測試 調查 車馬費 元歲 女性 一週 至少 次優 原味 風味 優格 舉辦 時間 四五 調查 時間 小時 配合 填寫 一週 優格 實用 '\n",
      " '紀錄 照片 簡單 文字說明 舉辦 地點 台北市 南京東路 光復 北路 交叉口 台北 巨蛋 參與 報酬 車馬費 報名 活動 網頁 市調 活動 資訊網 '\n",
      " '單位名稱 永光 資訊 多媒體 有限公司 單位地址 台北市 山區 久康 街號 活動 地點 自行 前往 負責人 思允 台北市 優格 口味 測試 調查 車馬費 '\n",
      " '聯絡人 姓氏 陳小姐 電話 聯絡 是否 回信 報名者 報名 自行 留意 網頁 公告 通知 方式 電話 通知 注意事項 公開 招募 列出 必要條件 詳細 '\n",
      " '參加者 條件 配額 將以 報名 資料 進行 篩選 初步 符合 進行 電話 過濾 訪談 分鐘 合格者 邀請 參加 承上 報名 資料 符合條件 配額 額滿將 '\n",
      " '另行通知 避免 受訪者 臨時 缺席 影響 活動 行將 邀請 超額 人數 實際 到場 人數 多於 人數 入場 將會 提供 部分 車馬費 報名 資料 電話 '\n",
      " '過濾 訪談 內容 僅供 篩選 使用 正式 訪問 內容 用於 研究 分析 所有 活動 保證 錄取 能夠 接受者 報名 簡介 常見問題 聯絡 參加 心得 '\n",
      " '知名 部落 介紹')\n"
     ]
    }
   ],
   "source": [
    "pprint(\" \".join(data['words'].iloc[600]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 將docs轉換成BOW形式\n",
    "- 把每篇文件的 token list 轉換成一組 (token_id, count) 的 list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建立 Bag-of-words 作為文章的特徵表示\n",
    "# 用 gensim ldamodel input 需要將文章轉換成 bag of words \n",
    "corpus = [dictionary.doc2bow(doc) for doc in docs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 訓練 LDA topic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-04-19 00:05:49,364 : INFO : using symmetric alpha at 0.1\n",
      "2025-04-19 00:05:49,365 : INFO : using symmetric eta at 0.1\n",
      "2025-04-19 00:05:49,370 : INFO : using serial LDA version on this node\n",
      "2025-04-19 00:05:49,392 : INFO : running online (single-pass) LDA training, 10 topics, 1 passes over the supplied corpus of 16310 documents, updating model once every 2000 documents, evaluating perplexity every 16310 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2025-04-19 00:05:49,392 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n",
      "2025-04-19 00:05:49,393 : INFO : PROGRESS: pass 0, at document #2000/16310\n",
      "2025-04-19 00:05:50,062 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:05:50,070 : INFO : topic #1 (0.100): 0.030*\"工作\" + 0.015*\"方式\" + 0.012*\"推定\" + 0.012*\"砍除\" + 0.012*\"內容\" + 0.011*\"文字\" + 0.011*\"國定假日\" + 0.011*\"應徵\" + 0.010*\"資訊\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:05:50,070 : INFO : topic #4 (0.100): 0.025*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.012*\"內容\" + 0.011*\"空白\" + 0.011*\"資訊\" + 0.011*\"單位\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:05:50,071 : INFO : topic #8 (0.100): 0.034*\"工作\" + 0.016*\"方式\" + 0.013*\"聯絡\" + 0.011*\"推定\" + 0.010*\"情形\" + 0.009*\"內容\" + 0.009*\"空白\" + 0.009*\"聯絡人\" + 0.009*\"小時\" + 0.009*\"單位\"\n",
      "2025-04-19 00:05:50,071 : INFO : topic #0 (0.100): 0.037*\"工作\" + 0.018*\"推定\" + 0.014*\"內容\" + 0.011*\"砍除\" + 0.011*\"第一項\" + 0.010*\"方式\" + 0.010*\"空白\" + 0.010*\"水桶\" + 0.010*\"未註明\" + 0.010*\"單位\"\n",
      "2025-04-19 00:05:50,072 : INFO : topic #6 (0.100): 0.032*\"工作\" + 0.015*\"聯絡\" + 0.015*\"內容\" + 0.015*\"方式\" + 0.011*\"應徵\" + 0.011*\"國定假日\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.009*\"推定\" + 0.009*\"工資\"\n",
      "2025-04-19 00:05:50,072 : INFO : topic diff=9.386298, rho=1.000000\n",
      "2025-04-19 00:05:50,074 : INFO : PROGRESS: pass 0, at document #4000/16310\n",
      "2025-04-19 00:05:50,709 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:05:50,713 : INFO : topic #6 (0.100): 0.033*\"工作\" + 0.016*\"方式\" + 0.014*\"內容\" + 0.014*\"聯絡\" + 0.011*\"應徵\" + 0.010*\"工資\" + 0.010*\"國定假日\" + 0.009*\"聯絡人\" + 0.008*\"情形\" + 0.008*\"資訊\"\n",
      "2025-04-19 00:05:50,714 : INFO : topic #0 (0.100): 0.039*\"工作\" + 0.018*\"推定\" + 0.014*\"內容\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.011*\"水桶\" + 0.011*\"方式\" + 0.010*\"應徵\" + 0.010*\"承攬\"\n",
      "2025-04-19 00:05:50,715 : INFO : topic #8 (0.100): 0.031*\"工作\" + 0.018*\"方式\" + 0.011*\"聯絡\" + 0.011*\"時間\" + 0.011*\"小時\" + 0.010*\"內容\" + 0.010*\"推定\" + 0.010*\"報名\" + 0.009*\"電話\" + 0.008*\"單位\"\n",
      "2025-04-19 00:05:50,716 : INFO : topic #1 (0.100): 0.028*\"工作\" + 0.015*\"方式\" + 0.012*\"內容\" + 0.012*\"文字\" + 0.011*\"砍除\" + 0.010*\"推定\" + 0.010*\"應徵\" + 0.010*\"聯絡\" + 0.009*\"資訊\" + 0.009*\"國定假日\"\n",
      "2025-04-19 00:05:50,716 : INFO : topic #7 (0.100): 0.037*\"工作\" + 0.013*\"情形\" + 0.012*\"單位\" + 0.012*\"推定\" + 0.012*\"空白\" + 0.012*\"資訊\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.011*\"第一項\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:05:50,717 : INFO : topic diff=0.658289, rho=0.707107\n",
      "2025-04-19 00:05:50,718 : INFO : PROGRESS: pass 0, at document #6000/16310\n",
      "2025-04-19 00:05:51,299 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:05:51,303 : INFO : topic #3 (0.100): 0.025*\"工作\" + 0.014*\"推定\" + 0.014*\"國定假日\" + 0.014*\"方式\" + 0.013*\"聯絡\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:05:51,304 : INFO : topic #8 (0.100): 0.024*\"工作\" + 0.015*\"方式\" + 0.013*\"報名\" + 0.012*\"時間\" + 0.011*\"電話\" + 0.011*\"活動\" + 0.010*\"聯絡\" + 0.010*\"內容\" + 0.010*\"小時\" + 0.009*\"台北市\"\n",
      "2025-04-19 00:05:51,304 : INFO : topic #2 (0.100): 0.028*\"工作\" + 0.012*\"第一項\" + 0.012*\"方式\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"推定\" + 0.011*\"砍除\" + 0.010*\"資訊\" + 0.010*\"依法\" + 0.009*\"應徵\"\n",
      "2025-04-19 00:05:51,305 : INFO : topic #5 (0.100): 0.035*\"工作\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"推定\" + 0.011*\"空白\" + 0.011*\"文字\" + 0.010*\"應徵\" + 0.009*\"國定假日\" + 0.009*\"內容\"\n",
      "2025-04-19 00:05:51,305 : INFO : topic #7 (0.100): 0.038*\"工作\" + 0.014*\"情形\" + 0.012*\"空白\" + 0.012*\"單位\" + 0.012*\"推定\" + 0.012*\"資訊\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.011*\"第一項\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:05:51,306 : INFO : topic diff=0.525696, rho=0.577350\n",
      "2025-04-19 00:05:51,307 : INFO : PROGRESS: pass 0, at document #8000/16310\n",
      "2025-04-19 00:05:51,686 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:05:51,690 : INFO : topic #2 (0.100): 0.028*\"工作\" + 0.012*\"第一項\" + 0.012*\"方式\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"推定\" + 0.011*\"砍除\" + 0.010*\"資訊\" + 0.010*\"依法\" + 0.009*\"應徵\"\n",
      "2025-04-19 00:05:51,691 : INFO : topic #3 (0.100): 0.025*\"工作\" + 0.014*\"推定\" + 0.014*\"國定假日\" + 0.014*\"方式\" + 0.013*\"聯絡\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:05:51,692 : INFO : topic #7 (0.100): 0.038*\"工作\" + 0.014*\"情形\" + 0.012*\"空白\" + 0.012*\"單位\" + 0.012*\"推定\" + 0.012*\"資訊\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.011*\"第一項\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:05:51,692 : INFO : topic #6 (0.100): 0.031*\"工作\" + 0.015*\"方式\" + 0.012*\"內容\" + 0.011*\"聯絡\" + 0.008*\"時間\" + 0.008*\"工資\" + 0.008*\"應徵\" + 0.007*\"依法\" + 0.007*\"國定假日\" + 0.007*\"每日\"\n",
      "2025-04-19 00:05:51,693 : INFO : topic #9 (0.100): 0.025*\"工作\" + 0.018*\"公司\" + 0.011*\"面試\" + 0.010*\"工程師\" + 0.009*\"問題\" + 0.009*\"時間\" + 0.008*\"經驗\" + 0.007*\"團隊\" + 0.006*\"方式\" + 0.006*\"技術\"\n",
      "2025-04-19 00:05:51,693 : INFO : topic diff=0.645233, rho=0.500000\n",
      "2025-04-19 00:05:51,694 : INFO : PROGRESS: pass 0, at document #10000/16310\n",
      "2025-04-19 00:05:51,977 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:05:51,981 : INFO : topic #6 (0.100): 0.029*\"工作\" + 0.015*\"業界\" + 0.013*\"方式\" + 0.011*\"內容\" + 0.010*\"聯絡\" + 0.008*\"數學\" + 0.008*\"時間\" + 0.008*\"機制\" + 0.007*\"工資\" + 0.007*\"應徵\"\n",
      "2025-04-19 00:05:51,981 : INFO : topic #3 (0.100): 0.025*\"工作\" + 0.014*\"推定\" + 0.014*\"國定假日\" + 0.014*\"方式\" + 0.013*\"聯絡\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:05:51,982 : INFO : topic #1 (0.100): 0.020*\"工作\" + 0.020*\"公司\" + 0.010*\"資深\" + 0.009*\"開發\" + 0.009*\"內容\" + 0.009*\"方式\" + 0.009*\"資訊\" + 0.007*\"文字\" + 0.006*\"聯絡\" + 0.006*\"分類\"\n",
      "2025-04-19 00:05:51,983 : INFO : topic #2 (0.100): 0.028*\"工作\" + 0.012*\"第一項\" + 0.012*\"方式\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"推定\" + 0.011*\"砍除\" + 0.010*\"資訊\" + 0.009*\"依法\" + 0.009*\"應徵\"\n",
      "2025-04-19 00:05:51,984 : INFO : topic #7 (0.100): 0.038*\"工作\" + 0.014*\"情形\" + 0.012*\"空白\" + 0.012*\"單位\" + 0.012*\"推定\" + 0.012*\"資訊\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.011*\"第一項\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:05:51,984 : INFO : topic diff=0.528236, rho=0.447214\n",
      "2025-04-19 00:05:51,985 : INFO : PROGRESS: pass 0, at document #12000/16310\n",
      "2025-04-19 00:05:52,347 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:05:52,351 : INFO : topic #9 (0.100): 0.018*\"公司\" + 0.017*\"工作\" + 0.009*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.006*\"時間\" + 0.006*\"開發\" + 0.006*\"技術\" + 0.006*\"經驗\" + 0.005*\"比較\"\n",
      "2025-04-19 00:05:52,352 : INFO : topic #3 (0.100): 0.025*\"工作\" + 0.014*\"推定\" + 0.014*\"國定假日\" + 0.014*\"方式\" + 0.013*\"聯絡\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:05:52,353 : INFO : topic #4 (0.100): 0.025*\"製程\" + 0.023*\"工作\" + 0.023*\"研發\" + 0.012*\"砍除\" + 0.012*\"表示\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"空白\" + 0.011*\"方式\" + 0.011*\"資工\"\n",
      "2025-04-19 00:05:52,354 : INFO : topic #0 (0.100): 0.040*\"工作\" + 0.018*\"推定\" + 0.014*\"內容\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.011*\"水桶\" + 0.010*\"方式\" + 0.010*\"應徵\" + 0.010*\"承攬\"\n",
      "2025-04-19 00:05:52,354 : INFO : topic #6 (0.100): 0.025*\"工作\" + 0.024*\"業界\" + 0.022*\"晶片\" + 0.011*\"方式\" + 0.009*\"內容\" + 0.009*\"機制\" + 0.008*\"數學\" + 0.008*\"聯絡\" + 0.008*\"中國\" + 0.007*\"時間\"\n",
      "2025-04-19 00:05:52,355 : INFO : topic diff=0.464044, rho=0.408248\n",
      "2025-04-19 00:05:52,355 : INFO : PROGRESS: pass 0, at document #14000/16310\n",
      "2025-04-19 00:05:52,671 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:05:52,683 : INFO : topic #1 (0.100): 0.019*\"公司\" + 0.018*\"工作\" + 0.011*\"資深\" + 0.009*\"資安\" + 0.008*\"缺點\" + 0.008*\"開發\" + 0.008*\"內容\" + 0.008*\"資訊\" + 0.008*\"方式\" + 0.007*\"厲害\"\n",
      "2025-04-19 00:05:52,684 : INFO : topic #7 (0.100): 0.037*\"工作\" + 0.013*\"情形\" + 0.012*\"單位\" + 0.012*\"空白\" + 0.012*\"推定\" + 0.012*\"資訊\" + 0.012*\"方式\" + 0.011*\"砍除\" + 0.011*\"第一項\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:05:52,684 : INFO : topic #2 (0.100): 0.025*\"工作\" + 0.011*\"第一項\" + 0.011*\"方式\" + 0.010*\"內容\" + 0.010*\"聯絡\" + 0.010*\"推定\" + 0.010*\"砍除\" + 0.009*\"依法\" + 0.009*\"資訊\" + 0.008*\"應徵\"\n",
      "2025-04-19 00:05:52,685 : INFO : topic #5 (0.100): 0.033*\"工作\" + 0.011*\"方式\" + 0.010*\"情形\" + 0.010*\"第一項\" + 0.010*\"推定\" + 0.010*\"空白\" + 0.010*\"文字\" + 0.010*\"應徵\" + 0.009*\"英國\" + 0.009*\"國定假日\"\n",
      "2025-04-19 00:05:52,686 : INFO : topic #8 (0.100): 0.011*\"半導體\" + 0.011*\"工作\" + 0.008*\"公司\" + 0.007*\"時間\" + 0.006*\"進行\" + 0.006*\"目前\" + 0.006*\"研究\" + 0.005*\"方式\" + 0.005*\"資料\" + 0.005*\"使用\"\n",
      "2025-04-19 00:05:52,687 : INFO : topic diff=0.410106, rho=0.377964\n",
      "2025-04-19 00:05:52,688 : INFO : PROGRESS: pass 0, at document #16000/16310\n",
      "2025-04-19 00:05:52,928 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:05:52,933 : INFO : topic #7 (0.100): 0.037*\"工作\" + 0.013*\"情形\" + 0.012*\"單位\" + 0.012*\"空白\" + 0.012*\"資訊\" + 0.012*\"推定\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.011*\"第一項\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:05:52,933 : INFO : topic #1 (0.100): 0.019*\"公司\" + 0.017*\"工作\" + 0.011*\"資深\" + 0.011*\"承諾\" + 0.009*\"缺點\" + 0.009*\"資安\" + 0.009*\"厲害\" + 0.008*\"通勤\" + 0.008*\"內容\" + 0.008*\"資訊\"\n",
      "2025-04-19 00:05:52,937 : INFO : topic #3 (0.100): 0.024*\"工作\" + 0.014*\"推定\" + 0.014*\"國定假日\" + 0.013*\"方式\" + 0.013*\"聯絡\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.011*\"內容\" + 0.010*\"情形\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:05:52,938 : INFO : topic #9 (0.100): 0.016*\"公司\" + 0.011*\"工作\" + 0.008*\"台灣\" + 0.007*\"美國\" + 0.006*\"技術\" + 0.006*\"工程師\" + 0.005*\"員工\" + 0.005*\"問題\" + 0.005*\"科技\" + 0.005*\"面試\"\n",
      "2025-04-19 00:05:52,939 : INFO : topic #2 (0.100): 0.024*\"工作\" + 0.014*\"尾牙\" + 0.010*\"第一項\" + 0.010*\"方式\" + 0.009*\"內容\" + 0.009*\"工資\" + 0.009*\"聯絡\" + 0.009*\"依法\" + 0.009*\"推定\" + 0.009*\"砍除\"\n",
      "2025-04-19 00:05:52,939 : INFO : topic diff=0.362326, rho=0.353553\n",
      "2025-04-19 00:05:53,024 : INFO : -10.214 per-word bound, 1187.4 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:05:53,024 : INFO : PROGRESS: pass 0, at document #16310/16310\n",
      "2025-04-19 00:05:53,063 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:05:53,068 : INFO : topic #1 (0.100): 0.019*\"公司\" + 0.017*\"承諾\" + 0.015*\"工作\" + 0.011*\"資深\" + 0.010*\"通勤\" + 0.010*\"厲害\" + 0.009*\"通用\" + 0.009*\"缺點\" + 0.008*\"責任\" + 0.008*\"資安\"\n",
      "2025-04-19 00:05:53,068 : INFO : topic #8 (0.100): 0.015*\"半導體\" + 0.008*\"工作\" + 0.007*\"公司\" + 0.006*\"時間\" + 0.006*\"進行\" + 0.006*\"研究\" + 0.006*\"蘋果\" + 0.006*\"模型\" + 0.005*\"影響\" + 0.005*\"目前\"\n",
      "2025-04-19 00:05:53,069 : INFO : topic #7 (0.100): 0.036*\"工作\" + 0.013*\"情形\" + 0.013*\"單位\" + 0.012*\"空白\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.011*\"第一項\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:05:53,069 : INFO : topic #4 (0.100): 0.114*\"研發\" + 0.114*\"製程\" + 0.032*\"職場\" + 0.017*\"半導體\" + 0.016*\"表示\" + 0.016*\"工作\" + 0.013*\"資工\" + 0.010*\"半導體業\" + 0.010*\"聯電\" + 0.010*\"光電\"\n",
      "2025-04-19 00:05:53,070 : INFO : topic #2 (0.100): 0.022*\"工作\" + 0.013*\"尾牙\" + 0.009*\"第一項\" + 0.009*\"方式\" + 0.009*\"內容\" + 0.009*\"工資\" + 0.008*\"聯絡\" + 0.008*\"依法\" + 0.008*\"推定\" + 0.008*\"砍除\"\n",
      "2025-04-19 00:05:53,070 : INFO : topic diff=0.344905, rho=0.333333\n",
      "2025-04-19 00:05:53,071 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel<num_terms=23261, num_topics=10, decay=0.5, chunksize=2000> in 3.68s', 'datetime': '2025-04-19T00:05:53.071124', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n"
     ]
    }
   ],
   "source": [
    "ldamodel = LdaModel(\n",
    "    corpus=corpus, \n",
    "    id2word=dictionary, # 字典\n",
    "    num_topics=10, # 生成幾個主題數\n",
    "    random_state=2024, # 亂數\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 透過LDA模型指標尋找最佳主題數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-04-19 00:07:46,705 : INFO : using symmetric alpha at 0.5\n",
      "2025-04-19 00:07:46,706 : INFO : using symmetric eta at 0.5\n",
      "2025-04-19 00:07:46,709 : INFO : using serial LDA version on this node\n",
      "2025-04-19 00:07:46,713 : INFO : running online (multi-pass) LDA training, 2 topics, 5 passes over the supplied corpus of 16310 documents, updating model once every 2000 documents, evaluating perplexity every 16310 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2025-04-19 00:07:46,714 : INFO : PROGRESS: pass 0, at document #2000/16310\n",
      "2025-04-19 00:07:47,417 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:47,419 : INFO : topic #0 (0.500): 0.031*\"工作\" + 0.013*\"應徵\" + 0.013*\"方式\" + 0.012*\"推定\" + 0.011*\"空白\" + 0.011*\"內容\" + 0.010*\"砍除\" + 0.010*\"單位\" + 0.009*\"工資\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:07:47,419 : INFO : topic #1 (0.500): 0.032*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:07:47,420 : INFO : topic diff=4.666146, rho=1.000000\n",
      "2025-04-19 00:07:47,421 : INFO : PROGRESS: pass 0, at document #4000/16310\n",
      "2025-04-19 00:07:48,069 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:48,071 : INFO : topic #0 (0.500): 0.029*\"工作\" + 0.014*\"方式\" + 0.010*\"內容\" + 0.010*\"應徵\" + 0.010*\"推定\" + 0.009*\"聯絡\" + 0.009*\"工資\" + 0.009*\"單位\" + 0.008*\"時間\" + 0.008*\"地點\"\n",
      "2025-04-19 00:07:48,072 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.013*\"方式\" + 0.013*\"推定\" + 0.011*\"砍除\" + 0.011*\"情形\" + 0.011*\"空白\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"第一項\" + 0.010*\"單位\"\n",
      "2025-04-19 00:07:48,072 : INFO : topic diff=0.508650, rho=0.707107\n",
      "2025-04-19 00:07:48,073 : INFO : PROGRESS: pass 0, at document #6000/16310\n",
      "2025-04-19 00:07:48,546 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:48,548 : INFO : topic #0 (0.500): 0.024*\"工作\" + 0.013*\"方式\" + 0.009*\"時間\" + 0.009*\"內容\" + 0.008*\"聯絡\" + 0.007*\"報名\" + 0.007*\"電話\" + 0.007*\"小時\" + 0.007*\"地點\" + 0.007*\"推定\"\n",
      "2025-04-19 00:07:48,548 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.011*\"空白\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:07:48,548 : INFO : topic diff=0.722963, rho=0.577350\n",
      "2025-04-19 00:07:48,549 : INFO : PROGRESS: pass 0, at document #8000/16310\n",
      "2025-04-19 00:07:48,692 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:48,693 : INFO : topic #0 (0.500): 0.019*\"工作\" + 0.013*\"公司\" + 0.008*\"時間\" + 0.007*\"面試\" + 0.007*\"方式\" + 0.005*\"內容\" + 0.005*\"經驗\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"開發\"\n",
      "2025-04-19 00:07:48,693 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.011*\"空白\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:07:48,694 : INFO : topic diff=0.908972, rho=0.500000\n",
      "2025-04-19 00:07:48,694 : INFO : PROGRESS: pass 0, at document #10000/16310\n",
      "2025-04-19 00:07:48,828 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:48,830 : INFO : topic #0 (0.500): 0.017*\"工作\" + 0.014*\"公司\" + 0.008*\"面試\" + 0.007*\"時間\" + 0.006*\"問題\" + 0.005*\"經驗\" + 0.005*\"開發\" + 0.005*\"工程師\" + 0.005*\"方式\" + 0.004*\"內容\"\n",
      "2025-04-19 00:07:48,830 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.011*\"空白\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:07:48,831 : INFO : topic diff=0.496164, rho=0.447214\n",
      "2025-04-19 00:07:48,831 : INFO : PROGRESS: pass 0, at document #12000/16310\n",
      "2025-04-19 00:07:48,976 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:48,978 : INFO : topic #0 (0.500): 0.013*\"工作\" + 0.013*\"公司\" + 0.006*\"面試\" + 0.005*\"時間\" + 0.005*\"問題\" + 0.004*\"工程師\" + 0.004*\"開發\" + 0.004*\"經驗\" + 0.004*\"目前\" + 0.004*\"技術\"\n",
      "2025-04-19 00:07:48,978 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:07:48,979 : INFO : topic diff=0.566727, rho=0.408248\n",
      "2025-04-19 00:07:48,979 : INFO : PROGRESS: pass 0, at document #14000/16310\n",
      "2025-04-19 00:07:49,122 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:49,123 : INFO : topic #0 (0.500): 0.012*\"公司\" + 0.010*\"工作\" + 0.005*\"台灣\" + 0.004*\"面試\" + 0.004*\"時間\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.003*\"目前\" + 0.003*\"員工\"\n",
      "2025-04-19 00:07:49,124 : INFO : topic #1 (0.500): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"第一項\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:07:49,124 : INFO : topic diff=0.438542, rho=0.377964\n",
      "2025-04-19 00:07:49,125 : INFO : PROGRESS: pass 0, at document #16000/16310\n",
      "2025-04-19 00:07:49,268 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:49,269 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.008*\"工作\" + 0.006*\"台灣\" + 0.004*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"問題\" + 0.004*\"員工\" + 0.004*\"時間\" + 0.004*\"工程師\"\n",
      "2025-04-19 00:07:49,270 : INFO : topic #1 (0.500): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"方式\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.011*\"情形\" + 0.010*\"聯絡\" + 0.010*\"第一項\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:07:49,270 : INFO : topic diff=0.326256, rho=0.353553\n",
      "2025-04-19 00:07:49,329 : INFO : -8.501 per-word bound, 362.2 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:07:49,329 : INFO : PROGRESS: pass 0, at document #16310/16310\n",
      "2025-04-19 00:07:49,354 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:49,356 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.007*\"工作\" + 0.006*\"美國\" + 0.006*\"台灣\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.003*\"台積電\"\n",
      "2025-04-19 00:07:49,356 : INFO : topic #1 (0.500): 0.031*\"工作\" + 0.012*\"推定\" + 0.012*\"方式\" + 0.011*\"砍除\" + 0.011*\"情形\" + 0.011*\"空白\" + 0.010*\"聯絡\" + 0.010*\"單位\" + 0.010*\"內容\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:07:49,356 : INFO : topic diff=0.359043, rho=0.333333\n",
      "2025-04-19 00:07:49,356 : INFO : PROGRESS: pass 1, at document #2000/16310\n",
      "2025-04-19 00:07:49,642 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:49,644 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"時間\" + 0.004*\"科技\" + 0.003*\"表示\"\n",
      "2025-04-19 00:07:49,644 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.010*\"情形\" + 0.010*\"單位\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:49,645 : INFO : topic diff=0.916464, rho=0.313805\n",
      "2025-04-19 00:07:49,645 : INFO : PROGRESS: pass 1, at document #4000/16310\n",
      "2025-04-19 00:07:49,936 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:49,938 : INFO : topic #0 (0.500): 0.010*\"公司\" + 0.007*\"工作\" + 0.005*\"台灣\" + 0.005*\"美國\" + 0.004*\"時間\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.003*\"員工\" + 0.003*\"科技\" + 0.003*\"資料\"\n",
      "2025-04-19 00:07:49,938 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.010*\"情形\" + 0.010*\"單位\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:49,938 : INFO : topic diff=0.329926, rho=0.313805\n",
      "2025-04-19 00:07:49,939 : INFO : PROGRESS: pass 1, at document #6000/16310\n",
      "2025-04-19 00:07:50,184 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:50,186 : INFO : topic #0 (0.500): 0.010*\"公司\" + 0.007*\"工作\" + 0.005*\"報名\" + 0.005*\"時間\" + 0.005*\"活動\" + 0.005*\"台灣\" + 0.004*\"資料\" + 0.004*\"美國\" + 0.003*\"技術\" + 0.003*\"使用\"\n",
      "2025-04-19 00:07:50,187 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.010*\"情形\" + 0.010*\"單位\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:50,187 : INFO : topic diff=0.267877, rho=0.313805\n",
      "2025-04-19 00:07:50,187 : INFO : PROGRESS: pass 1, at document #8000/16310\n",
      "2025-04-19 00:07:50,411 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:50,413 : INFO : topic #0 (0.500): 0.013*\"公司\" + 0.011*\"工作\" + 0.006*\"面試\" + 0.006*\"時間\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"經驗\" + 0.004*\"技術\" + 0.004*\"開發\" + 0.004*\"目前\"\n",
      "2025-04-19 00:07:50,414 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.010*\"情形\" + 0.010*\"單位\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:50,414 : INFO : topic diff=0.389331, rho=0.313805\n",
      "2025-04-19 00:07:50,414 : INFO : PROGRESS: pass 1, at document #10000/16310\n",
      "2025-04-19 00:07:50,619 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:50,621 : INFO : topic #0 (0.500): 0.014*\"公司\" + 0.012*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.006*\"時間\" + 0.005*\"工程師\" + 0.005*\"經驗\" + 0.005*\"開發\" + 0.004*\"目前\" + 0.004*\"技術\"\n",
      "2025-04-19 00:07:50,622 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:50,622 : INFO : topic diff=0.292958, rho=0.313805\n",
      "2025-04-19 00:07:50,622 : INFO : PROGRESS: pass 1, at document #12000/16310\n",
      "2025-04-19 00:07:50,785 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:50,786 : INFO : topic #0 (0.500): 0.013*\"公司\" + 0.010*\"工作\" + 0.006*\"面試\" + 0.005*\"問題\" + 0.005*\"時間\" + 0.004*\"工程師\" + 0.004*\"開發\" + 0.004*\"台灣\" + 0.004*\"技術\" + 0.004*\"目前\"\n",
      "2025-04-19 00:07:50,787 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:50,787 : INFO : topic diff=0.313159, rho=0.313805\n",
      "2025-04-19 00:07:50,787 : INFO : PROGRESS: pass 1, at document #14000/16310\n",
      "2025-04-19 00:07:50,944 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:50,946 : INFO : topic #0 (0.500): 0.012*\"公司\" + 0.009*\"工作\" + 0.005*\"台灣\" + 0.004*\"面試\" + 0.004*\"問題\" + 0.004*\"時間\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.003*\"目前\" + 0.003*\"員工\"\n",
      "2025-04-19 00:07:50,946 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"砍除\" + 0.010*\"單位\" + 0.010*\"空白\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:50,947 : INFO : topic diff=0.308267, rho=0.313805\n",
      "2025-04-19 00:07:50,947 : INFO : PROGRESS: pass 1, at document #16000/16310\n",
      "2025-04-19 00:07:51,096 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:51,098 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"問題\" + 0.004*\"員工\" + 0.004*\"工程師\" + 0.004*\"面試\"\n",
      "2025-04-19 00:07:51,098 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\" + 0.010*\"砍除\" + 0.010*\"情形\" + 0.010*\"空白\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:51,098 : INFO : topic diff=0.264518, rho=0.313805\n",
      "2025-04-19 00:07:51,158 : INFO : -8.453 per-word bound, 350.3 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:07:51,158 : INFO : PROGRESS: pass 1, at document #16310/16310\n",
      "2025-04-19 00:07:51,183 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:51,185 : INFO : topic #0 (0.500): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"美國\" + 0.006*\"台灣\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.003*\"問題\"\n",
      "2025-04-19 00:07:51,185 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\" + 0.010*\"砍除\" + 0.010*\"應徵\" + 0.010*\"情形\" + 0.010*\"空白\"\n",
      "2025-04-19 00:07:51,185 : INFO : topic diff=0.323771, rho=0.313805\n",
      "2025-04-19 00:07:51,186 : INFO : PROGRESS: pass 2, at document #2000/16310\n",
      "2025-04-19 00:07:51,442 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:51,444 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.003*\"問題\" + 0.003*\"時間\"\n",
      "2025-04-19 00:07:51,445 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:51,445 : INFO : topic diff=0.673476, rho=0.299409\n",
      "2025-04-19 00:07:51,445 : INFO : PROGRESS: pass 2, at document #4000/16310\n",
      "2025-04-19 00:07:51,719 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:51,721 : INFO : topic #0 (0.500): 0.010*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"時間\" + 0.004*\"晶片\" + 0.003*\"員工\" + 0.003*\"科技\" + 0.003*\"資料\"\n",
      "2025-04-19 00:07:51,721 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:51,722 : INFO : topic diff=0.302914, rho=0.299409\n",
      "2025-04-19 00:07:51,722 : INFO : PROGRESS: pass 2, at document #6000/16310\n",
      "2025-04-19 00:07:51,954 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:51,955 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.007*\"工作\" + 0.005*\"報名\" + 0.005*\"台灣\" + 0.005*\"活動\" + 0.005*\"時間\" + 0.004*\"資料\" + 0.004*\"美國\" + 0.004*\"技術\" + 0.003*\"問題\"\n",
      "2025-04-19 00:07:51,956 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:51,956 : INFO : topic diff=0.247939, rho=0.299409\n",
      "2025-04-19 00:07:51,956 : INFO : PROGRESS: pass 2, at document #8000/16310\n",
      "2025-04-19 00:07:52,154 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:52,155 : INFO : topic #0 (0.500): 0.014*\"公司\" + 0.010*\"工作\" + 0.006*\"面試\" + 0.005*\"時間\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"經驗\" + 0.004*\"技術\" + 0.004*\"開發\" + 0.004*\"目前\"\n",
      "2025-04-19 00:07:52,156 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:52,156 : INFO : topic diff=0.365239, rho=0.299409\n",
      "2025-04-19 00:07:52,156 : INFO : PROGRESS: pass 2, at document #10000/16310\n",
      "2025-04-19 00:07:52,328 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:52,329 : INFO : topic #0 (0.500): 0.014*\"公司\" + 0.011*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.006*\"時間\" + 0.005*\"工程師\" + 0.005*\"經驗\" + 0.005*\"開發\" + 0.004*\"目前\" + 0.004*\"技術\"\n",
      "2025-04-19 00:07:52,330 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"砍除\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:52,330 : INFO : topic diff=0.278015, rho=0.299409\n",
      "2025-04-19 00:07:52,331 : INFO : PROGRESS: pass 2, at document #12000/16310\n",
      "2025-04-19 00:07:52,486 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:52,488 : INFO : topic #0 (0.500): 0.013*\"公司\" + 0.010*\"工作\" + 0.006*\"面試\" + 0.005*\"問題\" + 0.005*\"時間\" + 0.004*\"工程師\" + 0.004*\"開發\" + 0.004*\"台灣\" + 0.004*\"技術\" + 0.004*\"目前\"\n",
      "2025-04-19 00:07:52,488 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"砍除\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.010*\"空白\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:52,488 : INFO : topic diff=0.293189, rho=0.299409\n",
      "2025-04-19 00:07:52,489 : INFO : PROGRESS: pass 2, at document #14000/16310\n",
      "2025-04-19 00:07:52,641 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:52,642 : INFO : topic #0 (0.500): 0.012*\"公司\" + 0.008*\"工作\" + 0.005*\"台灣\" + 0.004*\"面試\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"時間\" + 0.003*\"目前\" + 0.003*\"員工\"\n",
      "2025-04-19 00:07:52,643 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\" + 0.010*\"砍除\" + 0.010*\"應徵\" + 0.010*\"空白\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:52,643 : INFO : topic diff=0.291490, rho=0.299409\n",
      "2025-04-19 00:07:52,643 : INFO : PROGRESS: pass 2, at document #16000/16310\n",
      "2025-04-19 00:07:52,790 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:52,792 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.004*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"晶片\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"面試\"\n",
      "2025-04-19 00:07:52,792 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"空白\"\n",
      "2025-04-19 00:07:52,792 : INFO : topic diff=0.250620, rho=0.299409\n",
      "2025-04-19 00:07:52,851 : INFO : -8.449 per-word bound, 349.4 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:07:52,851 : INFO : PROGRESS: pass 2, at document #16310/16310\n",
      "2025-04-19 00:07:52,876 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:52,877 : INFO : topic #0 (0.500): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.003*\"問題\" + 0.003*\"表示\"\n",
      "2025-04-19 00:07:52,878 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\" + 0.010*\"應徵\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"空白\"\n",
      "2025-04-19 00:07:52,878 : INFO : topic diff=0.306627, rho=0.299409\n",
      "2025-04-19 00:07:52,878 : INFO : PROGRESS: pass 3, at document #2000/16310\n",
      "2025-04-19 00:07:53,126 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:53,127 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.003*\"問題\" + 0.003*\"時間\"\n",
      "2025-04-19 00:07:53,128 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:53,128 : INFO : topic diff=0.623463, rho=0.286829\n",
      "2025-04-19 00:07:53,128 : INFO : PROGRESS: pass 3, at document #4000/16310\n",
      "2025-04-19 00:07:53,374 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:53,376 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"時間\" + 0.004*\"晶片\" + 0.003*\"員工\" + 0.003*\"問題\" + 0.003*\"科技\"\n",
      "2025-04-19 00:07:53,377 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:53,377 : INFO : topic diff=0.292407, rho=0.286829\n",
      "2025-04-19 00:07:53,377 : INFO : PROGRESS: pass 3, at document #6000/16310\n",
      "2025-04-19 00:07:53,604 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:53,605 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.007*\"工作\" + 0.005*\"報名\" + 0.005*\"台灣\" + 0.004*\"時間\" + 0.004*\"活動\" + 0.004*\"美國\" + 0.004*\"資料\" + 0.004*\"技術\" + 0.003*\"問題\"\n",
      "2025-04-19 00:07:53,606 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:53,606 : INFO : topic diff=0.236177, rho=0.286829\n",
      "2025-04-19 00:07:53,606 : INFO : PROGRESS: pass 3, at document #8000/16310\n",
      "2025-04-19 00:07:53,797 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:53,798 : INFO : topic #0 (0.500): 0.013*\"公司\" + 0.010*\"工作\" + 0.006*\"面試\" + 0.005*\"時間\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"經驗\" + 0.004*\"技術\" + 0.004*\"台灣\" + 0.004*\"目前\"\n",
      "2025-04-19 00:07:53,799 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"應徵\" + 0.010*\"單位\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:53,799 : INFO : topic diff=0.345315, rho=0.286829\n",
      "2025-04-19 00:07:53,799 : INFO : PROGRESS: pass 3, at document #10000/16310\n",
      "2025-04-19 00:07:53,968 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:53,969 : INFO : topic #0 (0.500): 0.014*\"公司\" + 0.011*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.005*\"時間\" + 0.005*\"工程師\" + 0.005*\"經驗\" + 0.005*\"開發\" + 0.004*\"目前\" + 0.004*\"技術\"\n",
      "2025-04-19 00:07:53,970 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"砍除\" + 0.010*\"應徵\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:53,970 : INFO : topic diff=0.264775, rho=0.286829\n",
      "2025-04-19 00:07:53,971 : INFO : PROGRESS: pass 3, at document #12000/16310\n",
      "2025-04-19 00:07:54,125 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:54,126 : INFO : topic #0 (0.500): 0.013*\"公司\" + 0.010*\"工作\" + 0.006*\"面試\" + 0.005*\"問題\" + 0.005*\"時間\" + 0.004*\"工程師\" + 0.004*\"開發\" + 0.004*\"台灣\" + 0.004*\"技術\" + 0.004*\"目前\"\n",
      "2025-04-19 00:07:54,127 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"砍除\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.010*\"空白\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:54,127 : INFO : topic diff=0.277286, rho=0.286829\n",
      "2025-04-19 00:07:54,127 : INFO : PROGRESS: pass 3, at document #14000/16310\n",
      "2025-04-19 00:07:54,307 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:54,308 : INFO : topic #0 (0.500): 0.012*\"公司\" + 0.008*\"工作\" + 0.005*\"台灣\" + 0.005*\"面試\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"時間\" + 0.003*\"目前\" + 0.003*\"開發\"\n",
      "2025-04-19 00:07:54,309 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\" + 0.010*\"砍除\" + 0.010*\"應徵\" + 0.010*\"空白\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:54,309 : INFO : topic diff=0.277794, rho=0.286829\n",
      "2025-04-19 00:07:54,309 : INFO : PROGRESS: pass 3, at document #16000/16310\n",
      "2025-04-19 00:07:54,457 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:54,458 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.004*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"晶片\" + 0.004*\"工程師\" + 0.004*\"面試\" + 0.004*\"員工\"\n",
      "2025-04-19 00:07:54,459 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"空白\"\n",
      "2025-04-19 00:07:54,459 : INFO : topic diff=0.239211, rho=0.286829\n",
      "2025-04-19 00:07:54,518 : INFO : -8.448 per-word bound, 349.2 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:07:54,518 : INFO : PROGRESS: pass 3, at document #16310/16310\n",
      "2025-04-19 00:07:54,543 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:54,544 : INFO : topic #0 (0.500): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.003*\"表示\"\n",
      "2025-04-19 00:07:54,544 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\" + 0.010*\"應徵\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"空白\"\n",
      "2025-04-19 00:07:54,545 : INFO : topic diff=0.292528, rho=0.286829\n",
      "2025-04-19 00:07:54,545 : INFO : PROGRESS: pass 4, at document #2000/16310\n",
      "2025-04-19 00:07:54,792 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:54,793 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.003*\"問題\" + 0.003*\"時間\"\n",
      "2025-04-19 00:07:54,793 : INFO : topic #1 (0.500): 0.033*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:54,794 : INFO : topic diff=0.586245, rho=0.275711\n",
      "2025-04-19 00:07:54,794 : INFO : PROGRESS: pass 4, at document #4000/16310\n",
      "2025-04-19 00:07:55,037 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:55,039 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"時間\" + 0.004*\"晶片\" + 0.003*\"員工\" + 0.003*\"問題\" + 0.003*\"科技\"\n",
      "2025-04-19 00:07:55,039 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:55,040 : INFO : topic diff=0.284091, rho=0.275711\n",
      "2025-04-19 00:07:55,040 : INFO : PROGRESS: pass 4, at document #6000/16310\n",
      "2025-04-19 00:07:55,264 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:55,265 : INFO : topic #0 (0.500): 0.011*\"公司\" + 0.007*\"工作\" + 0.005*\"台灣\" + 0.005*\"報名\" + 0.004*\"時間\" + 0.004*\"活動\" + 0.004*\"美國\" + 0.004*\"資料\" + 0.004*\"技術\" + 0.003*\"問題\"\n",
      "2025-04-19 00:07:55,266 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:07:55,266 : INFO : topic diff=0.226828, rho=0.275711\n",
      "2025-04-19 00:07:55,267 : INFO : PROGRESS: pass 4, at document #8000/16310\n",
      "2025-04-19 00:07:55,455 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:55,457 : INFO : topic #0 (0.500): 0.013*\"公司\" + 0.010*\"工作\" + 0.006*\"面試\" + 0.005*\"時間\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"技術\" + 0.004*\"經驗\" + 0.004*\"台灣\" + 0.004*\"目前\"\n",
      "2025-04-19 00:07:55,458 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"應徵\" + 0.010*\"單位\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:55,458 : INFO : topic diff=0.328348, rho=0.275711\n",
      "2025-04-19 00:07:55,458 : INFO : PROGRESS: pass 4, at document #10000/16310\n",
      "2025-04-19 00:07:55,625 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:55,627 : INFO : topic #0 (0.500): 0.014*\"公司\" + 0.011*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.005*\"時間\" + 0.005*\"工程師\" + 0.005*\"經驗\" + 0.005*\"開發\" + 0.004*\"目前\" + 0.004*\"技術\"\n",
      "2025-04-19 00:07:55,627 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"砍除\" + 0.010*\"應徵\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:55,628 : INFO : topic diff=0.253407, rho=0.275711\n",
      "2025-04-19 00:07:55,628 : INFO : PROGRESS: pass 4, at document #12000/16310\n",
      "2025-04-19 00:07:55,781 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:55,782 : INFO : topic #0 (0.500): 0.013*\"公司\" + 0.010*\"工作\" + 0.006*\"面試\" + 0.005*\"問題\" + 0.005*\"時間\" + 0.004*\"工程師\" + 0.004*\"開發\" + 0.004*\"台灣\" + 0.004*\"技術\" + 0.004*\"目前\"\n",
      "2025-04-19 00:07:55,783 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.010*\"砍除\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.010*\"空白\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:55,783 : INFO : topic diff=0.263821, rho=0.275711\n",
      "2025-04-19 00:07:55,784 : INFO : PROGRESS: pass 4, at document #14000/16310\n",
      "2025-04-19 00:07:55,935 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:55,936 : INFO : topic #0 (0.500): 0.012*\"公司\" + 0.008*\"工作\" + 0.005*\"台灣\" + 0.005*\"面試\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"時間\" + 0.004*\"技術\" + 0.003*\"目前\" + 0.003*\"開發\"\n",
      "2025-04-19 00:07:55,937 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.010*\"砍除\" + 0.010*\"空白\" + 0.010*\"情形\"\n",
      "2025-04-19 00:07:55,937 : INFO : topic diff=0.266055, rho=0.275711\n",
      "2025-04-19 00:07:55,937 : INFO : PROGRESS: pass 4, at document #16000/16310\n",
      "2025-04-19 00:07:56,083 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:56,085 : INFO : topic #0 (0.500): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.004*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"面試\" + 0.004*\"工程師\" + 0.004*\"晶片\" + 0.004*\"員工\"\n",
      "2025-04-19 00:07:56,085 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.015*\"方式\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.010*\"砍除\" + 0.010*\"情形\" + 0.010*\"空白\"\n",
      "2025-04-19 00:07:56,085 : INFO : topic diff=0.229457, rho=0.275711\n",
      "2025-04-19 00:07:56,143 : INFO : -8.447 per-word bound, 349.0 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:07:56,144 : INFO : PROGRESS: pass 4, at document #16310/16310\n",
      "2025-04-19 00:07:56,169 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:07:56,170 : INFO : topic #0 (0.500): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"問題\" + 0.004*\"科技\" + 0.003*\"表示\"\n",
      "2025-04-19 00:07:56,170 : INFO : topic #1 (0.500): 0.034*\"工作\" + 0.014*\"方式\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\" + 0.010*\"應徵\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"空白\"\n",
      "2025-04-19 00:07:56,171 : INFO : topic diff=0.280494, rho=0.275711\n",
      "2025-04-19 00:07:56,171 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel<num_terms=23261, num_topics=2, decay=0.5, chunksize=2000> in 9.46s', 'datetime': '2025-04-19T00:07:56.171364', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n",
      "2025-04-19 00:08:00,142 : INFO : -7.158 per-word bound, 142.9 perplexity estimate based on a held-out corpus of 16310 documents with 3460358 words\n",
      "2025-04-19 00:08:00,144 : INFO : using ParallelWordOccurrenceAccumulator<processes=7, batch_size=64> to estimate probabilities from sliding windows\n",
      "2025-04-19 00:08:03,941 : INFO : 1 batches submitted to accumulate stats from 64 documents (22660 virtual)\n",
      "2025-04-19 00:08:03,944 : INFO : 2 batches submitted to accumulate stats from 128 documents (45646 virtual)\n",
      "2025-04-19 00:08:03,947 : INFO : 3 batches submitted to accumulate stats from 192 documents (67171 virtual)\n",
      "2025-04-19 00:08:03,950 : INFO : 4 batches submitted to accumulate stats from 256 documents (88330 virtual)\n",
      "2025-04-19 00:08:03,954 : INFO : 5 batches submitted to accumulate stats from 320 documents (109687 virtual)\n",
      "2025-04-19 00:08:03,958 : INFO : 6 batches submitted to accumulate stats from 384 documents (131042 virtual)\n",
      "2025-04-19 00:08:03,962 : INFO : 7 batches submitted to accumulate stats from 448 documents (153774 virtual)\n",
      "2025-04-19 00:08:03,968 : INFO : 8 batches submitted to accumulate stats from 512 documents (176164 virtual)\n",
      "2025-04-19 00:08:03,974 : INFO : 9 batches submitted to accumulate stats from 576 documents (197020 virtual)\n",
      "2025-04-19 00:08:03,985 : INFO : 10 batches submitted to accumulate stats from 640 documents (218505 virtual)\n",
      "2025-04-19 00:08:03,990 : INFO : 11 batches submitted to accumulate stats from 704 documents (240803 virtual)\n",
      "2025-04-19 00:08:03,997 : INFO : 12 batches submitted to accumulate stats from 768 documents (265360 virtual)\n",
      "2025-04-19 00:08:04,003 : INFO : 13 batches submitted to accumulate stats from 832 documents (286615 virtual)\n",
      "2025-04-19 00:08:04,009 : INFO : 14 batches submitted to accumulate stats from 896 documents (310833 virtual)\n",
      "2025-04-19 00:08:04,076 : INFO : 15 batches submitted to accumulate stats from 960 documents (331313 virtual)\n",
      "2025-04-19 00:08:04,080 : INFO : 16 batches submitted to accumulate stats from 1024 documents (350940 virtual)\n",
      "2025-04-19 00:08:04,084 : INFO : 17 batches submitted to accumulate stats from 1088 documents (368371 virtual)\n",
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      "2025-04-19 00:08:04,313 : INFO : 29 batches submitted to accumulate stats from 1856 documents (648902 virtual)\n",
      "2025-04-19 00:08:04,318 : INFO : 30 batches submitted to accumulate stats from 1920 documents (671126 virtual)\n",
      "2025-04-19 00:08:04,335 : INFO : 31 batches submitted to accumulate stats from 1984 documents (693717 virtual)\n",
      "2025-04-19 00:08:04,360 : INFO : 32 batches submitted to accumulate stats from 2048 documents (714139 virtual)\n",
      "2025-04-19 00:08:04,364 : INFO : 33 batches submitted to accumulate stats from 2112 documents (736202 virtual)\n",
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      "2025-04-19 00:08:04,442 : INFO : 35 batches submitted to accumulate stats from 2240 documents (779677 virtual)\n",
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      "2025-04-19 00:08:06,758 : INFO : 7 accumulators retrieved from output queue\n",
      "2025-04-19 00:08:06,766 : INFO : accumulated word occurrence stats for 3451622 virtual documents\n",
      "2025-04-19 00:08:06,798 : INFO : using symmetric alpha at 0.3333333333333333\n",
      "2025-04-19 00:08:06,799 : INFO : using symmetric eta at 0.3333333333333333\n",
      "2025-04-19 00:08:06,800 : INFO : using serial LDA version on this node\n",
      "2025-04-19 00:08:06,803 : INFO : running online (multi-pass) LDA training, 3 topics, 5 passes over the supplied corpus of 16310 documents, updating model once every 2000 documents, evaluating perplexity every 16310 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2025-04-19 00:08:06,804 : INFO : PROGRESS: pass 0, at document #2000/16310\n",
      "2025-04-19 00:08:07,575 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:07,576 : INFO : topic #0 (0.333): 0.027*\"工作\" + 0.014*\"方式\" + 0.012*\"推定\" + 0.012*\"應徵\" + 0.012*\"空白\" + 0.011*\"單位\" + 0.010*\"砍除\" + 0.010*\"內容\" + 0.009*\"聯絡\" + 0.009*\"資訊\"\n",
      "2025-04-19 00:08:07,577 : INFO : topic #1 (0.333): 0.029*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.010*\"單位\" + 0.010*\"國定假日\" + 0.010*\"第一項\" + 0.010*\"砍除\" + 0.010*\"空白\" + 0.010*\"情形\"\n",
      "2025-04-19 00:08:07,577 : INFO : topic #2 (0.333): 0.038*\"工作\" + 0.012*\"推定\" + 0.012*\"內容\" + 0.011*\"工資\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"情形\" + 0.010*\"聯絡\" + 0.010*\"砍除\" + 0.009*\"小時\"\n",
      "2025-04-19 00:08:07,578 : INFO : topic diff=5.168176, rho=1.000000\n",
      "2025-04-19 00:08:07,579 : INFO : PROGRESS: pass 0, at document #4000/16310\n",
      "2025-04-19 00:08:08,311 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:08,312 : INFO : topic #0 (0.333): 0.022*\"工作\" + 0.013*\"方式\" + 0.010*\"內容\" + 0.009*\"應徵\" + 0.009*\"聯絡\" + 0.008*\"推定\" + 0.008*\"空白\" + 0.008*\"報名\" + 0.008*\"地點\" + 0.008*\"資訊\"\n",
      "2025-04-19 00:08:08,313 : INFO : topic #1 (0.333): 0.029*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:08,313 : INFO : topic #2 (0.333): 0.041*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.011*\"應徵\" + 0.011*\"小時\" + 0.010*\"單位\" + 0.010*\"情形\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:08:08,314 : INFO : topic diff=0.541377, rho=0.707107\n",
      "2025-04-19 00:08:08,315 : INFO : PROGRESS: pass 0, at document #6000/16310\n",
      "2025-04-19 00:08:08,936 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:08,938 : INFO : topic #0 (0.333): 0.015*\"工作\" + 0.011*\"報名\" + 0.010*\"方式\" + 0.009*\"活動\" + 0.009*\"電話\" + 0.008*\"時間\" + 0.008*\"內容\" + 0.008*\"台北市\" + 0.008*\"聯絡\" + 0.007*\"公司\"\n",
      "2025-04-19 00:08:08,938 : INFO : topic #1 (0.333): 0.030*\"工作\" + 0.013*\"方式\" + 0.013*\"推定\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:08,939 : INFO : topic #2 (0.333): 0.042*\"工作\" + 0.014*\"方式\" + 0.014*\"推定\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.012*\"小時\" + 0.011*\"應徵\" + 0.010*\"單位\" + 0.010*\"依法\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:08:08,939 : INFO : topic diff=0.780898, rho=0.577350\n",
      "2025-04-19 00:08:08,940 : INFO : PROGRESS: pass 0, at document #8000/16310\n",
      "2025-04-19 00:08:09,204 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:09,206 : INFO : topic #0 (0.333): 0.016*\"工作\" + 0.014*\"公司\" + 0.008*\"面試\" + 0.007*\"時間\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"方式\" + 0.005*\"經驗\" + 0.005*\"開發\" + 0.005*\"內容\"\n",
      "2025-04-19 00:08:09,206 : INFO : topic #1 (0.333): 0.030*\"工作\" + 0.013*\"方式\" + 0.013*\"推定\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:09,207 : INFO : topic #2 (0.333): 0.042*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.011*\"小時\" + 0.010*\"應徵\" + 0.010*\"單位\" + 0.010*\"依法\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:08:09,207 : INFO : topic diff=0.832769, rho=0.500000\n",
      "2025-04-19 00:08:09,208 : INFO : PROGRESS: pass 0, at document #10000/16310\n",
      "2025-04-19 00:08:09,381 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:09,383 : INFO : topic #0 (0.333): 0.015*\"工作\" + 0.015*\"公司\" + 0.008*\"面試\" + 0.007*\"時間\" + 0.006*\"問題\" + 0.005*\"開發\" + 0.005*\"工程師\" + 0.005*\"經驗\" + 0.005*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 00:08:09,384 : INFO : topic #1 (0.333): 0.030*\"工作\" + 0.013*\"方式\" + 0.013*\"推定\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:09,384 : INFO : topic #2 (0.333): 0.041*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.011*\"小時\" + 0.010*\"應徵\" + 0.010*\"單位\" + 0.010*\"依法\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:08:09,385 : INFO : topic diff=0.475472, rho=0.447214\n",
      "2025-04-19 00:08:09,385 : INFO : PROGRESS: pass 0, at document #12000/16310\n",
      "2025-04-19 00:08:09,570 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:09,572 : INFO : topic #0 (0.333): 0.013*\"公司\" + 0.012*\"工作\" + 0.006*\"面試\" + 0.005*\"問題\" + 0.005*\"時間\" + 0.005*\"工程師\" + 0.004*\"開發\" + 0.004*\"經驗\" + 0.004*\"目前\" + 0.004*\"技術\"\n",
      "2025-04-19 00:08:09,573 : INFO : topic #1 (0.333): 0.030*\"工作\" + 0.013*\"方式\" + 0.013*\"推定\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:08:09,573 : INFO : topic #2 (0.333): 0.041*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.011*\"小時\" + 0.010*\"應徵\" + 0.010*\"單位\" + 0.010*\"依法\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:08:09,574 : INFO : topic diff=0.520220, rho=0.408248\n",
      "2025-04-19 00:08:09,575 : INFO : PROGRESS: pass 0, at document #14000/16310\n",
      "2025-04-19 00:08:09,749 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:09,751 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.009*\"工作\" + 0.005*\"台灣\" + 0.004*\"面試\" + 0.004*\"問題\" + 0.004*\"時間\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.003*\"目前\" + 0.003*\"員工\"\n",
      "2025-04-19 00:08:09,752 : INFO : topic #1 (0.333): 0.030*\"工作\" + 0.013*\"方式\" + 0.013*\"推定\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.010*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:08:09,753 : INFO : topic #2 (0.333): 0.040*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"工資\" + 0.011*\"內容\" + 0.011*\"小時\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.009*\"依法\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:08:09,753 : INFO : topic diff=0.420072, rho=0.377964\n",
      "2025-04-19 00:08:09,754 : INFO : PROGRESS: pass 0, at document #16000/16310\n",
      "2025-04-19 00:08:09,930 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:09,932 : INFO : topic #0 (0.333): 0.011*\"公司\" + 0.008*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:09,932 : INFO : topic #1 (0.333): 0.029*\"工作\" + 0.013*\"方式\" + 0.012*\"推定\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.010*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:08:09,933 : INFO : topic #2 (0.333): 0.039*\"工作\" + 0.013*\"方式\" + 0.012*\"推定\" + 0.011*\"工資\" + 0.011*\"小時\" + 0.011*\"內容\" + 0.010*\"應徵\" + 0.010*\"單位\" + 0.009*\"依法\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:08:09,933 : INFO : topic diff=0.319432, rho=0.353553\n",
      "2025-04-19 00:08:09,998 : INFO : -8.517 per-word bound, 366.3 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:08:09,998 : INFO : PROGRESS: pass 0, at document #16310/16310\n",
      "2025-04-19 00:08:10,054 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:10,056 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"美國\" + 0.006*\"台灣\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"台積電\"\n",
      "2025-04-19 00:08:10,057 : INFO : topic #1 (0.333): 0.029*\"工作\" + 0.012*\"方式\" + 0.012*\"推定\" + 0.011*\"空白\" + 0.011*\"砍除\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.010*\"聯絡\" + 0.010*\"資訊\" + 0.010*\"國定假日\"\n",
      "2025-04-19 00:08:10,057 : INFO : topic #2 (0.333): 0.039*\"工作\" + 0.013*\"小時\" + 0.013*\"方式\" + 0.011*\"推定\" + 0.011*\"工資\" + 0.011*\"內容\" + 0.010*\"單位\" + 0.009*\"應徵\" + 0.008*\"依法\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:08:10,058 : INFO : topic diff=0.324231, rho=0.333333\n",
      "2025-04-19 00:08:10,058 : INFO : PROGRESS: pass 1, at document #2000/16310\n",
      "2025-04-19 00:08:10,711 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:10,713 : INFO : topic #0 (0.333): 0.011*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.003*\"台積電\"\n",
      "2025-04-19 00:08:10,713 : INFO : topic #1 (0.333): 0.031*\"工作\" + 0.013*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:10,714 : INFO : topic #2 (0.333): 0.039*\"工作\" + 0.020*\"方式\" + 0.013*\"小時\" + 0.012*\"時間\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.012*\"推定\" + 0.010*\"依法\" + 0.010*\"單位\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:08:10,714 : INFO : topic diff=1.147336, rho=0.313805\n",
      "2025-04-19 00:08:10,714 : INFO : PROGRESS: pass 1, at document #4000/16310\n",
      "2025-04-19 00:08:11,297 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:11,299 : INFO : topic #0 (0.333): 0.011*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.003*\"科技\" + 0.003*\"表示\" + 0.003*\"問題\"\n",
      "2025-04-19 00:08:11,299 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:11,299 : INFO : topic #2 (0.333): 0.037*\"工作\" + 0.020*\"方式\" + 0.013*\"小時\" + 0.013*\"時間\" + 0.012*\"內容\" + 0.012*\"推定\" + 0.012*\"工資\" + 0.010*\"聯絡\" + 0.010*\"單位\" + 0.010*\"依法\"\n",
      "2025-04-19 00:08:11,300 : INFO : topic diff=0.404685, rho=0.313805\n",
      "2025-04-19 00:08:11,300 : INFO : PROGRESS: pass 1, at document #6000/16310\n",
      "2025-04-19 00:08:11,758 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:11,760 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.007*\"工作\" + 0.005*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.003*\"時間\" + 0.003*\"目前\" + 0.003*\"員工\" + 0.003*\"工程師\"\n",
      "2025-04-19 00:08:11,760 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:11,761 : INFO : topic #2 (0.333): 0.034*\"工作\" + 0.020*\"方式\" + 0.014*\"時間\" + 0.013*\"小時\" + 0.012*\"內容\" + 0.010*\"聯絡\" + 0.010*\"推定\" + 0.010*\"工資\" + 0.010*\"電話\" + 0.010*\"依法\"\n",
      "2025-04-19 00:08:11,761 : INFO : topic diff=0.287964, rho=0.313805\n",
      "2025-04-19 00:08:11,762 : INFO : PROGRESS: pass 1, at document #8000/16310\n",
      "2025-04-19 00:08:12,023 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:12,025 : INFO : topic #0 (0.333): 0.015*\"公司\" + 0.010*\"工作\" + 0.006*\"面試\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"時間\" + 0.004*\"技術\" + 0.004*\"開發\" + 0.004*\"經驗\" + 0.004*\"台灣\"\n",
      "2025-04-19 00:08:12,025 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:12,026 : INFO : topic #2 (0.333): 0.037*\"工作\" + 0.021*\"方式\" + 0.015*\"時間\" + 0.014*\"小時\" + 0.012*\"內容\" + 0.011*\"聯絡\" + 0.010*\"每日\" + 0.010*\"工資\" + 0.010*\"推定\" + 0.010*\"電話\"\n",
      "2025-04-19 00:08:12,026 : INFO : topic diff=0.356930, rho=0.313805\n",
      "2025-04-19 00:08:12,027 : INFO : PROGRESS: pass 1, at document #10000/16310\n",
      "2025-04-19 00:08:12,248 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:12,250 : INFO : topic #0 (0.333): 0.015*\"公司\" + 0.011*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.005*\"時間\" + 0.005*\"經驗\" + 0.005*\"目前\" + 0.004*\"技術\"\n",
      "2025-04-19 00:08:12,251 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:12,251 : INFO : topic #2 (0.333): 0.037*\"工作\" + 0.021*\"方式\" + 0.015*\"時間\" + 0.015*\"小時\" + 0.012*\"內容\" + 0.011*\"聯絡\" + 0.010*\"每日\" + 0.009*\"電話\" + 0.009*\"工資\" + 0.009*\"推定\"\n",
      "2025-04-19 00:08:12,252 : INFO : topic diff=0.283800, rho=0.313805\n",
      "2025-04-19 00:08:12,252 : INFO : PROGRESS: pass 1, at document #12000/16310\n",
      "2025-04-19 00:08:12,456 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:12,458 : INFO : topic #0 (0.333): 0.014*\"公司\" + 0.010*\"工作\" + 0.006*\"面試\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"開發\" + 0.004*\"時間\" + 0.004*\"台灣\" + 0.004*\"技術\" + 0.004*\"目前\"\n",
      "2025-04-19 00:08:12,459 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:12,459 : INFO : topic #2 (0.333): 0.037*\"工作\" + 0.021*\"方式\" + 0.015*\"時間\" + 0.015*\"小時\" + 0.012*\"內容\" + 0.011*\"聯絡\" + 0.010*\"每日\" + 0.009*\"地點\" + 0.009*\"電話\" + 0.009*\"工資\"\n",
      "2025-04-19 00:08:12,460 : INFO : topic diff=0.299853, rho=0.313805\n",
      "2025-04-19 00:08:12,460 : INFO : PROGRESS: pass 1, at document #14000/16310\n",
      "2025-04-19 00:08:12,664 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:12,666 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.008*\"工作\" + 0.005*\"台灣\" + 0.004*\"面試\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"目前\" + 0.003*\"時間\" + 0.003*\"員工\"\n",
      "2025-04-19 00:08:12,666 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:12,667 : INFO : topic #2 (0.333): 0.037*\"工作\" + 0.021*\"方式\" + 0.015*\"時間\" + 0.014*\"小時\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.010*\"每日\" + 0.009*\"地點\" + 0.009*\"電話\" + 0.009*\"通知\"\n",
      "2025-04-19 00:08:12,667 : INFO : topic diff=0.297309, rho=0.313805\n",
      "2025-04-19 00:08:12,668 : INFO : PROGRESS: pass 1, at document #16000/16310\n",
      "2025-04-19 00:08:12,862 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:12,863 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"問題\" + 0.004*\"員工\" + 0.004*\"工程師\" + 0.004*\"面試\"\n",
      "2025-04-19 00:08:12,864 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.010*\"內容\"\n",
      "2025-04-19 00:08:12,864 : INFO : topic #2 (0.333): 0.036*\"工作\" + 0.020*\"方式\" + 0.015*\"時間\" + 0.014*\"小時\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.010*\"地點\" + 0.009*\"每日\" + 0.009*\"報名\" + 0.008*\"工資\"\n",
      "2025-04-19 00:08:12,865 : INFO : topic diff=0.262534, rho=0.313805\n",
      "2025-04-19 00:08:12,932 : INFO : -8.456 per-word bound, 351.3 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:08:12,932 : INFO : PROGRESS: pass 1, at document #16310/16310\n",
      "2025-04-19 00:08:12,965 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:12,966 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 00:08:12,967 : INFO : topic #1 (0.333): 0.031*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.010*\"內容\"\n",
      "2025-04-19 00:08:12,967 : INFO : topic #2 (0.333): 0.038*\"工作\" + 0.018*\"方式\" + 0.016*\"小時\" + 0.015*\"時間\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.010*\"地點\" + 0.009*\"報名\" + 0.009*\"每日\" + 0.009*\"工時\"\n",
      "2025-04-19 00:08:12,968 : INFO : topic diff=0.298291, rho=0.313805\n",
      "2025-04-19 00:08:12,968 : INFO : PROGRESS: pass 2, at document #2000/16310\n",
      "2025-04-19 00:08:13,472 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:13,474 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"工作\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 00:08:13,474 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:13,474 : INFO : topic #2 (0.333): 0.033*\"工作\" + 0.019*\"方式\" + 0.015*\"時間\" + 0.013*\"小時\" + 0.011*\"內容\" + 0.010*\"聯絡\" + 0.010*\"電話\" + 0.009*\"通知\" + 0.009*\"地點\" + 0.009*\"報名\"\n",
      "2025-04-19 00:08:13,475 : INFO : topic diff=0.838892, rho=0.299409\n",
      "2025-04-19 00:08:13,475 : INFO : PROGRESS: pass 2, at document #4000/16310\n",
      "2025-04-19 00:08:13,962 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:13,965 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"工作\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:13,965 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.014*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:13,966 : INFO : topic #2 (0.333): 0.031*\"工作\" + 0.019*\"方式\" + 0.015*\"時間\" + 0.013*\"小時\" + 0.011*\"內容\" + 0.011*\"電話\" + 0.010*\"聯絡\" + 0.010*\"通知\" + 0.010*\"報名\" + 0.009*\"地點\"\n",
      "2025-04-19 00:08:13,966 : INFO : topic diff=0.350293, rho=0.299409\n",
      "2025-04-19 00:08:13,966 : INFO : PROGRESS: pass 2, at document #6000/16310\n",
      "2025-04-19 00:08:14,402 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:14,404 : INFO : topic #0 (0.333): 0.013*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.003*\"晶片\" + 0.003*\"面試\"\n",
      "2025-04-19 00:08:14,404 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:14,405 : INFO : topic #2 (0.333): 0.030*\"工作\" + 0.019*\"方式\" + 0.014*\"時間\" + 0.012*\"小時\" + 0.011*\"電話\" + 0.011*\"內容\" + 0.011*\"報名\" + 0.010*\"聯絡\" + 0.010*\"活動\" + 0.010*\"通知\"\n",
      "2025-04-19 00:08:14,405 : INFO : topic diff=0.259849, rho=0.299409\n",
      "2025-04-19 00:08:14,406 : INFO : PROGRESS: pass 2, at document #8000/16310\n",
      "2025-04-19 00:08:14,681 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:14,683 : INFO : topic #0 (0.333): 0.015*\"公司\" + 0.010*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.004*\"開發\" + 0.004*\"台灣\" + 0.004*\"經驗\" + 0.004*\"目前\"\n",
      "2025-04-19 00:08:14,683 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:14,684 : INFO : topic #2 (0.333): 0.033*\"工作\" + 0.020*\"方式\" + 0.016*\"時間\" + 0.013*\"小時\" + 0.011*\"內容\" + 0.011*\"電話\" + 0.010*\"聯絡\" + 0.010*\"報名\" + 0.009*\"活動\" + 0.009*\"台北市\"\n",
      "2025-04-19 00:08:14,684 : INFO : topic diff=0.333700, rho=0.299409\n",
      "2025-04-19 00:08:14,685 : INFO : PROGRESS: pass 2, at document #10000/16310\n",
      "2025-04-19 00:08:14,919 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:14,921 : INFO : topic #0 (0.333): 0.015*\"公司\" + 0.011*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.005*\"經驗\" + 0.004*\"目前\" + 0.004*\"時間\"\n",
      "2025-04-19 00:08:14,922 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:14,922 : INFO : topic #2 (0.333): 0.033*\"工作\" + 0.020*\"方式\" + 0.016*\"時間\" + 0.014*\"小時\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.010*\"電話\" + 0.010*\"報名\" + 0.009*\"活動\" + 0.009*\"台北市\"\n",
      "2025-04-19 00:08:14,922 : INFO : topic diff=0.267383, rho=0.299409\n",
      "2025-04-19 00:08:14,923 : INFO : PROGRESS: pass 2, at document #12000/16310\n",
      "2025-04-19 00:08:15,146 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:15,148 : INFO : topic #0 (0.333): 0.014*\"公司\" + 0.009*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.004*\"開發\" + 0.004*\"技術\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"時間\"\n",
      "2025-04-19 00:08:15,148 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:15,149 : INFO : topic #2 (0.333): 0.033*\"工作\" + 0.020*\"方式\" + 0.017*\"時間\" + 0.014*\"小時\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.010*\"報名\" + 0.010*\"活動\" + 0.010*\"電話\" + 0.009*\"地點\"\n",
      "2025-04-19 00:08:15,149 : INFO : topic diff=0.280246, rho=0.299409\n",
      "2025-04-19 00:08:15,149 : INFO : PROGRESS: pass 2, at document #14000/16310\n",
      "2025-04-19 00:08:15,363 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:15,365 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.008*\"工作\" + 0.006*\"台灣\" + 0.005*\"面試\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"目前\" + 0.003*\"員工\" + 0.003*\"開發\"\n",
      "2025-04-19 00:08:15,366 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:15,366 : INFO : topic #2 (0.333): 0.032*\"工作\" + 0.019*\"方式\" + 0.016*\"時間\" + 0.013*\"小時\" + 0.011*\"內容\" + 0.011*\"報名\" + 0.010*\"聯絡\" + 0.010*\"活動\" + 0.009*\"電話\" + 0.009*\"地點\"\n",
      "2025-04-19 00:08:15,367 : INFO : topic diff=0.280783, rho=0.299409\n",
      "2025-04-19 00:08:15,367 : INFO : PROGRESS: pass 2, at document #16000/16310\n",
      "2025-04-19 00:08:15,571 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:15,573 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"面試\"\n",
      "2025-04-19 00:08:15,574 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:15,574 : INFO : topic #2 (0.333): 0.032*\"工作\" + 0.018*\"方式\" + 0.016*\"時間\" + 0.013*\"小時\" + 0.011*\"報名\" + 0.010*\"內容\" + 0.010*\"活動\" + 0.010*\"地點\" + 0.010*\"聯絡\" + 0.009*\"電話\"\n",
      "2025-04-19 00:08:15,574 : INFO : topic diff=0.247817, rho=0.299409\n",
      "2025-04-19 00:08:15,643 : INFO : -8.445 per-word bound, 348.4 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:08:15,644 : INFO : PROGRESS: pass 2, at document #16310/16310\n",
      "2025-04-19 00:08:15,678 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:15,680 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 00:08:15,681 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:15,682 : INFO : topic #2 (0.333): 0.033*\"工作\" + 0.017*\"方式\" + 0.016*\"時間\" + 0.015*\"小時\" + 0.011*\"報名\" + 0.010*\"內容\" + 0.010*\"活動\" + 0.010*\"地點\" + 0.010*\"聯絡\" + 0.008*\"台北市\"\n",
      "2025-04-19 00:08:15,682 : INFO : topic diff=0.281434, rho=0.299409\n",
      "2025-04-19 00:08:15,682 : INFO : PROGRESS: pass 3, at document #2000/16310\n",
      "2025-04-19 00:08:16,139 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:16,141 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 00:08:16,141 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:16,142 : INFO : topic #2 (0.333): 0.030*\"工作\" + 0.019*\"方式\" + 0.016*\"時間\" + 0.012*\"小時\" + 0.011*\"電話\" + 0.011*\"內容\" + 0.011*\"報名\" + 0.010*\"活動\" + 0.010*\"聯絡\" + 0.010*\"地點\"\n",
      "2025-04-19 00:08:16,142 : INFO : topic diff=0.733749, rho=0.286829\n",
      "2025-04-19 00:08:16,143 : INFO : PROGRESS: pass 3, at document #4000/16310\n",
      "2025-04-19 00:08:16,589 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:16,591 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:16,591 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:16,592 : INFO : topic #2 (0.333): 0.029*\"工作\" + 0.018*\"方式\" + 0.015*\"時間\" + 0.012*\"小時\" + 0.012*\"電話\" + 0.011*\"內容\" + 0.011*\"報名\" + 0.010*\"活動\" + 0.010*\"通知\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:08:16,592 : INFO : topic diff=0.327646, rho=0.286829\n",
      "2025-04-19 00:08:16,593 : INFO : PROGRESS: pass 3, at document #6000/16310\n",
      "2025-04-19 00:08:16,984 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:16,986 : INFO : topic #0 (0.333): 0.013*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"晶片\" + 0.003*\"面試\"\n",
      "2025-04-19 00:08:16,986 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:16,987 : INFO : topic #2 (0.333): 0.028*\"工作\" + 0.018*\"方式\" + 0.015*\"時間\" + 0.012*\"電話\" + 0.012*\"報名\" + 0.012*\"小時\" + 0.011*\"活動\" + 0.011*\"內容\" + 0.010*\"通知\" + 0.010*\"台北市\"\n",
      "2025-04-19 00:08:16,987 : INFO : topic diff=0.246719, rho=0.286829\n",
      "2025-04-19 00:08:16,987 : INFO : PROGRESS: pass 3, at document #8000/16310\n",
      "2025-04-19 00:08:17,259 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:17,261 : INFO : topic #0 (0.333): 0.015*\"公司\" + 0.009*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.004*\"開發\" + 0.004*\"台灣\" + 0.004*\"經驗\" + 0.004*\"目前\"\n",
      "2025-04-19 00:08:17,261 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:17,262 : INFO : topic #2 (0.333): 0.030*\"工作\" + 0.019*\"方式\" + 0.016*\"時間\" + 0.013*\"小時\" + 0.011*\"電話\" + 0.011*\"內容\" + 0.011*\"報名\" + 0.010*\"活動\" + 0.010*\"聯絡\" + 0.010*\"台北市\"\n",
      "2025-04-19 00:08:17,262 : INFO : topic diff=0.313431, rho=0.286829\n",
      "2025-04-19 00:08:17,263 : INFO : PROGRESS: pass 3, at document #10000/16310\n",
      "2025-04-19 00:08:17,502 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:17,504 : INFO : topic #0 (0.333): 0.016*\"公司\" + 0.010*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.004*\"經驗\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 00:08:17,504 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:17,505 : INFO : topic #2 (0.333): 0.031*\"工作\" + 0.019*\"方式\" + 0.017*\"時間\" + 0.013*\"小時\" + 0.011*\"內容\" + 0.011*\"報名\" + 0.010*\"電話\" + 0.010*\"聯絡\" + 0.010*\"活動\" + 0.009*\"台北市\"\n",
      "2025-04-19 00:08:17,505 : INFO : topic diff=0.252243, rho=0.286829\n",
      "2025-04-19 00:08:17,506 : INFO : PROGRESS: pass 3, at document #12000/16310\n",
      "2025-04-19 00:08:17,752 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:17,754 : INFO : topic #0 (0.333): 0.014*\"公司\" + 0.009*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.004*\"開發\" + 0.004*\"技術\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 00:08:17,754 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:17,755 : INFO : topic #2 (0.333): 0.031*\"工作\" + 0.019*\"方式\" + 0.017*\"時間\" + 0.013*\"小時\" + 0.011*\"報名\" + 0.011*\"內容\" + 0.011*\"活動\" + 0.010*\"聯絡\" + 0.010*\"電話\" + 0.009*\"地點\"\n",
      "2025-04-19 00:08:17,755 : INFO : topic diff=0.264195, rho=0.286829\n",
      "2025-04-19 00:08:17,756 : INFO : PROGRESS: pass 3, at document #14000/16310\n",
      "2025-04-19 00:08:17,978 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:17,980 : INFO : topic #0 (0.333): 0.013*\"公司\" + 0.008*\"工作\" + 0.006*\"台灣\" + 0.005*\"面試\" + 0.005*\"問題\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"目前\" + 0.003*\"開發\" + 0.003*\"員工\"\n",
      "2025-04-19 00:08:17,980 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:17,981 : INFO : topic #2 (0.333): 0.030*\"工作\" + 0.018*\"方式\" + 0.016*\"時間\" + 0.013*\"小時\" + 0.011*\"報名\" + 0.011*\"活動\" + 0.010*\"內容\" + 0.010*\"聯絡\" + 0.010*\"電話\" + 0.010*\"地點\"\n",
      "2025-04-19 00:08:17,981 : INFO : topic diff=0.266639, rho=0.286829\n",
      "2025-04-19 00:08:17,982 : INFO : PROGRESS: pass 3, at document #16000/16310\n",
      "2025-04-19 00:08:18,187 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:18,189 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"晶片\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"面試\"\n",
      "2025-04-19 00:08:18,189 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:18,190 : INFO : topic #2 (0.333): 0.029*\"工作\" + 0.017*\"方式\" + 0.016*\"時間\" + 0.013*\"小時\" + 0.011*\"報名\" + 0.011*\"活動\" + 0.010*\"內容\" + 0.010*\"地點\" + 0.009*\"聯絡\" + 0.009*\"電話\"\n",
      "2025-04-19 00:08:18,190 : INFO : topic diff=0.235313, rho=0.286829\n",
      "2025-04-19 00:08:18,261 : INFO : -8.439 per-word bound, 347.0 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:08:18,261 : INFO : PROGRESS: pass 3, at document #16310/16310\n",
      "2025-04-19 00:08:18,295 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:18,298 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:18,298 : INFO : topic #1 (0.333): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:08:18,299 : INFO : topic #2 (0.333): 0.031*\"工作\" + 0.016*\"方式\" + 0.016*\"時間\" + 0.014*\"小時\" + 0.011*\"活動\" + 0.011*\"報名\" + 0.010*\"地點\" + 0.010*\"內容\" + 0.009*\"聯絡\" + 0.009*\"電話\"\n",
      "2025-04-19 00:08:18,299 : INFO : topic diff=0.267190, rho=0.286829\n",
      "2025-04-19 00:08:18,299 : INFO : PROGRESS: pass 4, at document #2000/16310\n",
      "2025-04-19 00:08:18,722 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:18,724 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:18,725 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:18,726 : INFO : topic #2 (0.333): 0.028*\"工作\" + 0.018*\"方式\" + 0.016*\"時間\" + 0.012*\"小時\" + 0.011*\"電話\" + 0.011*\"報名\" + 0.011*\"活動\" + 0.010*\"內容\" + 0.010*\"台北市\" + 0.010*\"地點\"\n",
      "2025-04-19 00:08:18,726 : INFO : topic diff=0.669148, rho=0.275711\n",
      "2025-04-19 00:08:18,726 : INFO : PROGRESS: pass 4, at document #4000/16310\n",
      "2025-04-19 00:08:19,155 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:19,157 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"問題\" + 0.004*\"科技\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:19,158 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:19,158 : INFO : topic #2 (0.333): 0.027*\"工作\" + 0.018*\"方式\" + 0.016*\"時間\" + 0.012*\"電話\" + 0.012*\"小時\" + 0.011*\"報名\" + 0.011*\"活動\" + 0.010*\"內容\" + 0.010*\"通知\" + 0.010*\"台北市\"\n",
      "2025-04-19 00:08:19,159 : INFO : topic diff=0.312450, rho=0.275711\n",
      "2025-04-19 00:08:19,159 : INFO : PROGRESS: pass 4, at document #6000/16310\n",
      "2025-04-19 00:08:19,536 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:19,538 : INFO : topic #0 (0.333): 0.013*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.005*\"技術\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"晶片\" + 0.003*\"面試\"\n",
      "2025-04-19 00:08:19,538 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:19,539 : INFO : topic #2 (0.333): 0.026*\"工作\" + 0.018*\"方式\" + 0.015*\"時間\" + 0.013*\"電話\" + 0.012*\"報名\" + 0.011*\"活動\" + 0.011*\"小時\" + 0.011*\"內容\" + 0.010*\"台北市\" + 0.010*\"通知\"\n",
      "2025-04-19 00:08:19,539 : INFO : topic diff=0.237084, rho=0.275711\n",
      "2025-04-19 00:08:19,540 : INFO : PROGRESS: pass 4, at document #8000/16310\n",
      "2025-04-19 00:08:19,810 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:19,812 : INFO : topic #0 (0.333): 0.015*\"公司\" + 0.009*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.004*\"台灣\" + 0.004*\"開發\" + 0.004*\"目前\" + 0.004*\"經驗\"\n",
      "2025-04-19 00:08:19,813 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:19,813 : INFO : topic #2 (0.333): 0.028*\"工作\" + 0.019*\"方式\" + 0.017*\"時間\" + 0.012*\"小時\" + 0.012*\"電話\" + 0.011*\"報名\" + 0.011*\"內容\" + 0.011*\"活動\" + 0.010*\"台北市\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:08:19,814 : INFO : topic diff=0.296448, rho=0.275711\n",
      "2025-04-19 00:08:19,814 : INFO : PROGRESS: pass 4, at document #10000/16310\n",
      "2025-04-19 00:08:20,050 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:20,052 : INFO : topic #0 (0.333): 0.016*\"公司\" + 0.010*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.004*\"目前\" + 0.004*\"經驗\" + 0.004*\"比較\"\n",
      "2025-04-19 00:08:20,053 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:20,054 : INFO : topic #2 (0.333): 0.029*\"工作\" + 0.019*\"方式\" + 0.017*\"時間\" + 0.013*\"小時\" + 0.011*\"內容\" + 0.011*\"報名\" + 0.011*\"電話\" + 0.010*\"活動\" + 0.010*\"聯絡\" + 0.010*\"台北市\"\n",
      "2025-04-19 00:08:20,054 : INFO : topic diff=0.239550, rho=0.275711\n",
      "2025-04-19 00:08:20,054 : INFO : PROGRESS: pass 4, at document #12000/16310\n",
      "2025-04-19 00:08:20,280 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:20,282 : INFO : topic #0 (0.333): 0.014*\"公司\" + 0.009*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.004*\"技術\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 00:08:20,283 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:20,283 : INFO : topic #2 (0.333): 0.029*\"工作\" + 0.018*\"方式\" + 0.017*\"時間\" + 0.013*\"小時\" + 0.011*\"活動\" + 0.011*\"報名\" + 0.010*\"內容\" + 0.010*\"電話\" + 0.010*\"聯絡\" + 0.009*\"地點\"\n",
      "2025-04-19 00:08:20,283 : INFO : topic diff=0.250540, rho=0.275711\n",
      "2025-04-19 00:08:20,284 : INFO : PROGRESS: pass 4, at document #14000/16310\n",
      "2025-04-19 00:08:20,535 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:20,538 : INFO : topic #0 (0.333): 0.013*\"公司\" + 0.008*\"工作\" + 0.006*\"台灣\" + 0.005*\"面試\" + 0.005*\"問題\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"目前\" + 0.003*\"開發\" + 0.003*\"員工\"\n",
      "2025-04-19 00:08:20,539 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:20,541 : INFO : topic #2 (0.333): 0.029*\"工作\" + 0.018*\"方式\" + 0.017*\"時間\" + 0.012*\"小時\" + 0.011*\"活動\" + 0.011*\"報名\" + 0.010*\"內容\" + 0.010*\"電話\" + 0.010*\"地點\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:08:20,541 : INFO : topic diff=0.254796, rho=0.275711\n",
      "2025-04-19 00:08:20,542 : INFO : PROGRESS: pass 4, at document #16000/16310\n",
      "2025-04-19 00:08:20,827 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:20,829 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"面試\"\n",
      "2025-04-19 00:08:20,829 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"空白\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:08:20,830 : INFO : topic #2 (0.333): 0.028*\"工作\" + 0.017*\"方式\" + 0.016*\"時間\" + 0.012*\"小時\" + 0.011*\"活動\" + 0.011*\"報名\" + 0.010*\"內容\" + 0.010*\"地點\" + 0.009*\"電話\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:08:20,830 : INFO : topic diff=0.225021, rho=0.275711\n",
      "2025-04-19 00:08:20,897 : INFO : -8.435 per-word bound, 346.1 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:08:20,898 : INFO : PROGRESS: pass 4, at document #16310/16310\n",
      "2025-04-19 00:08:20,931 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:20,933 : INFO : topic #0 (0.333): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:20,933 : INFO : topic #1 (0.333): 0.033*\"工作\" + 0.014*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"空白\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:08:20,934 : INFO : topic #2 (0.333): 0.029*\"工作\" + 0.016*\"時間\" + 0.016*\"方式\" + 0.013*\"小時\" + 0.011*\"活動\" + 0.010*\"報名\" + 0.010*\"地點\" + 0.010*\"內容\" + 0.009*\"聯絡\" + 0.009*\"電話\"\n",
      "2025-04-19 00:08:20,934 : INFO : topic diff=0.255352, rho=0.275711\n",
      "2025-04-19 00:08:20,934 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel<num_terms=23261, num_topics=3, decay=0.5, chunksize=2000> in 14.13s', 'datetime': '2025-04-19T00:08:20.934816', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n",
      "2025-04-19 00:08:25,754 : INFO : -7.070 per-word bound, 134.4 perplexity estimate based on a held-out corpus of 16310 documents with 3460358 words\n",
      "2025-04-19 00:08:25,756 : INFO : using ParallelWordOccurrenceAccumulator<processes=7, batch_size=64> to estimate probabilities from sliding windows\n",
      "2025-04-19 00:08:29,705 : INFO : 1 batches submitted to accumulate stats from 64 documents (22660 virtual)\n",
      "2025-04-19 00:08:29,707 : INFO : 2 batches submitted to accumulate stats from 128 documents (45646 virtual)\n",
      "2025-04-19 00:08:29,710 : INFO : 3 batches submitted to accumulate stats from 192 documents (67171 virtual)\n",
      "2025-04-19 00:08:29,713 : INFO : 4 batches submitted to accumulate stats from 256 documents (88330 virtual)\n",
      "2025-04-19 00:08:29,715 : INFO : 5 batches submitted to accumulate stats from 320 documents (109687 virtual)\n",
      "2025-04-19 00:08:29,719 : INFO : 6 batches submitted to accumulate stats from 384 documents (131042 virtual)\n",
      "2025-04-19 00:08:29,722 : INFO : 7 batches submitted to accumulate stats from 448 documents (153774 virtual)\n",
      "2025-04-19 00:08:29,727 : INFO : 8 batches submitted to accumulate stats from 512 documents (176164 virtual)\n",
      "2025-04-19 00:08:29,732 : INFO : 9 batches submitted to accumulate stats from 576 documents (197020 virtual)\n",
      "2025-04-19 00:08:29,735 : INFO : 10 batches submitted to accumulate stats from 640 documents (218505 virtual)\n",
      "2025-04-19 00:08:29,737 : INFO : 11 batches submitted to accumulate stats from 704 documents (240803 virtual)\n",
      "2025-04-19 00:08:29,739 : INFO : 12 batches submitted to accumulate stats from 768 documents (265360 virtual)\n",
      "2025-04-19 00:08:29,745 : INFO : 13 batches submitted to accumulate stats from 832 documents (286615 virtual)\n",
      "2025-04-19 00:08:29,751 : INFO : 14 batches submitted to accumulate stats from 896 documents (310833 virtual)\n",
      "2025-04-19 00:08:29,841 : INFO : 15 batches submitted to accumulate stats from 960 documents (331313 virtual)\n",
      "2025-04-19 00:08:29,846 : INFO : 16 batches submitted to accumulate stats from 1024 documents (350940 virtual)\n",
      "2025-04-19 00:08:29,850 : INFO : 17 batches submitted to accumulate stats from 1088 documents (368371 virtual)\n",
      "2025-04-19 00:08:29,857 : INFO : 18 batches submitted to accumulate stats from 1152 documents (390334 virtual)\n",
      "2025-04-19 00:08:29,861 : INFO : 19 batches submitted to accumulate stats from 1216 documents (414153 virtual)\n",
      "2025-04-19 00:08:29,869 : INFO : 20 batches submitted to accumulate stats from 1280 documents (435684 virtual)\n",
      "2025-04-19 00:08:29,877 : INFO : 21 batches submitted to accumulate stats from 1344 documents (459433 virtual)\n",
      "2025-04-19 00:08:29,974 : INFO : 22 batches submitted to accumulate stats from 1408 documents (483210 virtual)\n",
      "2025-04-19 00:08:29,998 : INFO : 23 batches submitted to accumulate stats from 1472 documents (507391 virtual)\n",
      "2025-04-19 00:08:30,011 : INFO : 24 batches submitted to accumulate stats from 1536 documents (527404 virtual)\n",
      "2025-04-19 00:08:30,030 : INFO : 25 batches submitted to accumulate stats from 1600 documents (550178 virtual)\n",
      "2025-04-19 00:08:30,036 : INFO : 26 batches submitted to accumulate stats from 1664 documents (575041 virtual)\n",
      "2025-04-19 00:08:30,045 : INFO : 27 batches submitted to accumulate stats from 1728 documents (598912 virtual)\n",
      "2025-04-19 00:08:30,050 : INFO : 28 batches submitted to accumulate stats from 1792 documents (622487 virtual)\n",
      "2025-04-19 00:08:30,109 : INFO : 29 batches submitted to accumulate stats from 1856 documents (648902 virtual)\n",
      "2025-04-19 00:08:30,124 : INFO : 30 batches submitted to accumulate stats from 1920 documents (671126 virtual)\n",
      "2025-04-19 00:08:30,129 : INFO : 31 batches submitted to accumulate stats from 1984 documents (693717 virtual)\n",
      "2025-04-19 00:08:30,150 : INFO : 32 batches submitted to accumulate stats from 2048 documents (714139 virtual)\n",
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      "2025-04-19 00:08:32,752 : INFO : 245 batches submitted to accumulate stats from 15680 documents (3360131 virtual)\n",
      "2025-04-19 00:08:32,763 : INFO : 246 batches submitted to accumulate stats from 15744 documents (3370635 virtual)\n",
      "2025-04-19 00:08:32,778 : INFO : 247 batches submitted to accumulate stats from 15808 documents (3380994 virtual)\n",
      "2025-04-19 00:08:32,781 : INFO : 248 batches submitted to accumulate stats from 15872 documents (3389920 virtual)\n",
      "2025-04-19 00:08:32,784 : INFO : 249 batches submitted to accumulate stats from 15936 documents (3397487 virtual)\n",
      "2025-04-19 00:08:32,791 : INFO : 250 batches submitted to accumulate stats from 16000 documents (3406129 virtual)\n",
      "2025-04-19 00:08:32,793 : INFO : 251 batches submitted to accumulate stats from 16064 documents (3416805 virtual)\n",
      "2025-04-19 00:08:32,798 : INFO : 252 batches submitted to accumulate stats from 16128 documents (3426189 virtual)\n",
      "2025-04-19 00:08:32,811 : INFO : 253 batches submitted to accumulate stats from 16192 documents (3433824 virtual)\n",
      "2025-04-19 00:08:32,828 : INFO : 254 batches submitted to accumulate stats from 16256 documents (3443379 virtual)\n",
      "2025-04-19 00:08:32,829 : INFO : 255 batches submitted to accumulate stats from 16320 documents (3450914 virtual)\n",
      "2025-04-19 00:08:33,033 : INFO : 7 accumulators retrieved from output queue\n",
      "2025-04-19 00:08:33,042 : INFO : accumulated word occurrence stats for 3451622 virtual documents\n",
      "2025-04-19 00:08:33,091 : INFO : using symmetric alpha at 0.25\n",
      "2025-04-19 00:08:33,091 : INFO : using symmetric eta at 0.25\n",
      "2025-04-19 00:08:33,093 : INFO : using serial LDA version on this node\n",
      "2025-04-19 00:08:33,097 : INFO : running online (multi-pass) LDA training, 4 topics, 5 passes over the supplied corpus of 16310 documents, updating model once every 2000 documents, evaluating perplexity every 16310 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2025-04-19 00:08:33,098 : INFO : PROGRESS: pass 0, at document #2000/16310\n",
      "2025-04-19 00:08:33,719 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:33,722 : INFO : topic #0 (0.250): 0.029*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"應徵\" + 0.012*\"空白\" + 0.011*\"單位\" + 0.011*\"砍除\" + 0.010*\"內容\" + 0.010*\"聯絡\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:08:33,722 : INFO : topic #1 (0.250): 0.030*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"國定假日\" + 0.010*\"第一項\" + 0.010*\"空白\" + 0.010*\"情形\" + 0.010*\"內容\"\n",
      "2025-04-19 00:08:33,723 : INFO : topic #2 (0.250): 0.040*\"工作\" + 0.013*\"內容\" + 0.013*\"推定\" + 0.012*\"工資\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"聯絡\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"小時\"\n",
      "2025-04-19 00:08:33,723 : INFO : topic #3 (0.250): 0.020*\"工作\" + 0.012*\"方式\" + 0.011*\"砍除\" + 0.010*\"聯絡人\" + 0.010*\"推定\" + 0.010*\"應徵\" + 0.010*\"空白\" + 0.009*\"文字\" + 0.008*\"資訊\" + 0.008*\"情形\"\n",
      "2025-04-19 00:08:33,723 : INFO : topic diff=5.686695, rho=1.000000\n",
      "2025-04-19 00:08:33,724 : INFO : PROGRESS: pass 0, at document #4000/16310\n",
      "2025-04-19 00:08:34,301 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:34,304 : INFO : topic #0 (0.250): 0.029*\"工作\" + 0.013*\"方式\" + 0.013*\"應徵\" + 0.013*\"空白\" + 0.012*\"推定\" + 0.011*\"砍除\" + 0.011*\"單位\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:08:34,304 : INFO : topic #1 (0.250): 0.030*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"第一項\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"單位\"\n",
      "2025-04-19 00:08:34,305 : INFO : topic #2 (0.250): 0.042*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"內容\" + 0.012*\"工資\" + 0.011*\"應徵\" + 0.011*\"小時\" + 0.010*\"單位\" + 0.010*\"聯絡\" + 0.010*\"情形\"\n",
      "2025-04-19 00:08:34,305 : INFO : topic #3 (0.250): 0.014*\"報名\" + 0.012*\"工作\" + 0.012*\"活動\" + 0.011*\"電話\" + 0.011*\"方式\" + 0.009*\"時間\" + 0.009*\"聯絡\" + 0.009*\"台北市\" + 0.009*\"內容\" + 0.008*\"聯絡人\"\n",
      "2025-04-19 00:08:34,306 : INFO : topic diff=0.556125, rho=0.707107\n",
      "2025-04-19 00:08:34,306 : INFO : PROGRESS: pass 0, at document #6000/16310\n",
      "2025-04-19 00:08:34,811 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:34,814 : INFO : topic #0 (0.250): 0.029*\"工作\" + 0.012*\"應徵\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"推定\" + 0.011*\"砍除\" + 0.010*\"內容\" + 0.010*\"單位\" + 0.009*\"第一項\" + 0.009*\"資訊\"\n",
      "2025-04-19 00:08:34,814 : INFO : topic #1 (0.250): 0.031*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:34,815 : INFO : topic #2 (0.250): 0.042*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.012*\"內容\" + 0.012*\"工資\" + 0.011*\"小時\" + 0.010*\"依法\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:08:34,815 : INFO : topic #3 (0.250): 0.016*\"報名\" + 0.014*\"活動\" + 0.012*\"電話\" + 0.010*\"台北市\" + 0.010*\"時間\" + 0.009*\"方式\" + 0.008*\"聯絡\" + 0.008*\"內容\" + 0.008*\"資料\" + 0.008*\"人數\"\n",
      "2025-04-19 00:08:34,816 : INFO : topic diff=0.777768, rho=0.577350\n",
      "2025-04-19 00:08:34,816 : INFO : PROGRESS: pass 0, at document #8000/16310\n",
      "2025-04-19 00:08:35,178 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:35,180 : INFO : topic #0 (0.250): 0.029*\"工作\" + 0.012*\"應徵\" + 0.012*\"方式\" + 0.011*\"空白\" + 0.011*\"推定\" + 0.010*\"砍除\" + 0.010*\"內容\" + 0.009*\"單位\" + 0.009*\"資訊\" + 0.009*\"第一項\"\n",
      "2025-04-19 00:08:35,181 : INFO : topic #1 (0.250): 0.031*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:35,182 : INFO : topic #2 (0.250): 0.045*\"工作\" + 0.013*\"方式\" + 0.010*\"小時\" + 0.010*\"內容\" + 0.010*\"推定\" + 0.009*\"工資\" + 0.009*\"面試\" + 0.008*\"應徵\" + 0.008*\"時間\" + 0.008*\"單位\"\n",
      "2025-04-19 00:08:35,185 : INFO : topic #3 (0.250): 0.016*\"公司\" + 0.009*\"工作\" + 0.007*\"時間\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"面試\" + 0.005*\"開發\" + 0.005*\"經驗\" + 0.005*\"目前\" + 0.005*\"團隊\"\n",
      "2025-04-19 00:08:35,185 : INFO : topic diff=0.948413, rho=0.500000\n",
      "2025-04-19 00:08:35,187 : INFO : PROGRESS: pass 0, at document #10000/16310\n",
      "2025-04-19 00:08:35,449 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:35,454 : INFO : topic #0 (0.250): 0.029*\"工作\" + 0.012*\"應徵\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"推定\" + 0.010*\"砍除\" + 0.009*\"內容\" + 0.009*\"單位\" + 0.009*\"資訊\" + 0.009*\"第一項\"\n",
      "2025-04-19 00:08:35,457 : INFO : topic #1 (0.250): 0.031*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:35,462 : INFO : topic #2 (0.250): 0.047*\"工作\" + 0.013*\"方式\" + 0.011*\"小時\" + 0.010*\"內容\" + 0.010*\"推定\" + 0.009*\"工資\" + 0.008*\"面試\" + 0.008*\"時間\" + 0.008*\"應徵\" + 0.008*\"單位\"\n",
      "2025-04-19 00:08:35,470 : INFO : topic #3 (0.250): 0.016*\"公司\" + 0.011*\"工作\" + 0.008*\"面試\" + 0.007*\"問題\" + 0.007*\"時間\" + 0.006*\"工程師\" + 0.006*\"開發\" + 0.005*\"經驗\" + 0.005*\"目前\" + 0.004*\"技術\"\n",
      "2025-04-19 00:08:35,473 : INFO : topic diff=0.493554, rho=0.447214\n",
      "2025-04-19 00:08:35,474 : INFO : PROGRESS: pass 0, at document #12000/16310\n",
      "2025-04-19 00:08:35,687 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:35,689 : INFO : topic #0 (0.250): 0.028*\"工作\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"推定\" + 0.010*\"砍除\" + 0.009*\"內容\" + 0.009*\"單位\" + 0.009*\"資訊\" + 0.009*\"第一項\"\n",
      "2025-04-19 00:08:35,689 : INFO : topic #1 (0.250): 0.031*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:08:35,690 : INFO : topic #2 (0.250): 0.047*\"工作\" + 0.013*\"方式\" + 0.011*\"小時\" + 0.010*\"內容\" + 0.009*\"推定\" + 0.008*\"時間\" + 0.008*\"工資\" + 0.008*\"面試\" + 0.008*\"應徵\" + 0.008*\"單位\"\n",
      "2025-04-19 00:08:35,690 : INFO : topic #3 (0.250): 0.014*\"公司\" + 0.009*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"時間\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.004*\"目前\" + 0.004*\"技術\" + 0.004*\"台灣\"\n",
      "2025-04-19 00:08:35,691 : INFO : topic diff=0.504608, rho=0.408248\n",
      "2025-04-19 00:08:35,691 : INFO : PROGRESS: pass 0, at document #14000/16310\n",
      "2025-04-19 00:08:35,877 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:35,879 : INFO : topic #0 (0.250): 0.027*\"工作\" + 0.011*\"方式\" + 0.011*\"應徵\" + 0.010*\"空白\" + 0.010*\"推定\" + 0.009*\"砍除\" + 0.009*\"單位\" + 0.009*\"內容\" + 0.009*\"資訊\" + 0.008*\"第一項\"\n",
      "2025-04-19 00:08:35,880 : INFO : topic #1 (0.250): 0.031*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:08:35,880 : INFO : topic #2 (0.250): 0.046*\"工作\" + 0.012*\"方式\" + 0.011*\"小時\" + 0.010*\"內容\" + 0.009*\"推定\" + 0.008*\"時間\" + 0.008*\"工資\" + 0.008*\"單位\" + 0.008*\"面試\" + 0.008*\"應徵\"\n",
      "2025-04-19 00:08:35,881 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.008*\"工作\" + 0.006*\"台灣\" + 0.004*\"問題\" + 0.004*\"面試\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"時間\" + 0.004*\"目前\" + 0.003*\"員工\"\n",
      "2025-04-19 00:08:35,881 : INFO : topic diff=0.415438, rho=0.377964\n",
      "2025-04-19 00:08:35,882 : INFO : PROGRESS: pass 0, at document #16000/16310\n",
      "2025-04-19 00:08:36,067 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:36,069 : INFO : topic #0 (0.250): 0.025*\"工作\" + 0.010*\"方式\" + 0.010*\"應徵\" + 0.010*\"空白\" + 0.009*\"推定\" + 0.009*\"砍除\" + 0.009*\"單位\" + 0.008*\"資訊\" + 0.008*\"內容\" + 0.008*\"第一項\"\n",
      "2025-04-19 00:08:36,070 : INFO : topic #1 (0.250): 0.030*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:08:36,070 : INFO : topic #2 (0.250): 0.046*\"工作\" + 0.012*\"方式\" + 0.011*\"小時\" + 0.010*\"內容\" + 0.008*\"時間\" + 0.008*\"工資\" + 0.008*\"推定\" + 0.008*\"面試\" + 0.008*\"單位\" + 0.008*\"應徵\"\n",
      "2025-04-19 00:08:36,071 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"晶片\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"員工\" + 0.004*\"工程師\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:36,071 : INFO : topic diff=0.317846, rho=0.353553\n",
      "2025-04-19 00:08:36,139 : INFO : -8.535 per-word bound, 371.0 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:08:36,140 : INFO : PROGRESS: pass 0, at document #16310/16310\n",
      "2025-04-19 00:08:36,172 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:36,175 : INFO : topic #0 (0.250): 0.024*\"關稅\" + 0.022*\"工作\" + 0.009*\"方式\" + 0.009*\"應徵\" + 0.009*\"空白\" + 0.008*\"單位\" + 0.008*\"推定\" + 0.008*\"砍除\" + 0.008*\"分類\" + 0.007*\"資訊\"\n",
      "2025-04-19 00:08:36,175 : INFO : topic #1 (0.250): 0.030*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.010*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:08:36,176 : INFO : topic #2 (0.250): 0.047*\"工作\" + 0.013*\"小時\" + 0.011*\"方式\" + 0.009*\"內容\" + 0.008*\"工時\" + 0.008*\"時間\" + 0.008*\"單位\" + 0.007*\"面試\" + 0.007*\"工資\" + 0.007*\"應徵\"\n",
      "2025-04-19 00:08:36,176 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"美國\" + 0.006*\"台灣\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"台積電\"\n",
      "2025-04-19 00:08:36,176 : INFO : topic diff=0.304742, rho=0.333333\n",
      "2025-04-19 00:08:36,177 : INFO : PROGRESS: pass 1, at document #2000/16310\n",
      "2025-04-19 00:08:36,669 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:36,671 : INFO : topic #0 (0.250): 0.019*\"工作\" + 0.011*\"方式\" + 0.010*\"台北市\" + 0.009*\"關稅\" + 0.009*\"內容\" + 0.009*\"聯絡\" + 0.008*\"應徵\" + 0.008*\"分類\" + 0.008*\"資訊\" + 0.007*\"電話\"\n",
      "2025-04-19 00:08:36,671 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:36,672 : INFO : topic #2 (0.250): 0.045*\"工作\" + 0.019*\"方式\" + 0.014*\"小時\" + 0.013*\"時間\" + 0.011*\"內容\" + 0.011*\"工資\" + 0.010*\"依法\" + 0.010*\"每日\" + 0.010*\"推定\" + 0.009*\"單位\"\n",
      "2025-04-19 00:08:36,672 : INFO : topic #3 (0.250): 0.011*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.003*\"台積電\"\n",
      "2025-04-19 00:08:36,673 : INFO : topic diff=0.992621, rho=0.313805\n",
      "2025-04-19 00:08:36,673 : INFO : PROGRESS: pass 1, at document #4000/16310\n",
      "2025-04-19 00:08:37,135 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:37,138 : INFO : topic #0 (0.250): 0.016*\"電話\" + 0.014*\"工作\" + 0.014*\"台北市\" + 0.014*\"報名\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"方式\" + 0.010*\"通知\" + 0.010*\"地點\" + 0.008*\"人數\"\n",
      "2025-04-19 00:08:37,138 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:37,139 : INFO : topic #2 (0.250): 0.045*\"工作\" + 0.021*\"方式\" + 0.015*\"小時\" + 0.014*\"時間\" + 0.012*\"工資\" + 0.012*\"推定\" + 0.011*\"內容\" + 0.011*\"依法\" + 0.011*\"每日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:37,141 : INFO : topic #3 (0.250): 0.011*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"工作\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"時間\" + 0.003*\"科技\" + 0.003*\"表示\"\n",
      "2025-04-19 00:08:37,145 : INFO : topic diff=0.498947, rho=0.313805\n",
      "2025-04-19 00:08:37,152 : INFO : PROGRESS: pass 1, at document #6000/16310\n",
      "2025-04-19 00:08:37,536 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:37,538 : INFO : topic #0 (0.250): 0.023*\"報名\" + 0.020*\"電話\" + 0.017*\"台北市\" + 0.016*\"活動\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.012*\"通知\" + 0.011*\"人數\" + 0.011*\"地點\" + 0.010*\"車馬費\"\n",
      "2025-04-19 00:08:37,539 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:37,539 : INFO : topic #2 (0.250): 0.046*\"工作\" + 0.023*\"方式\" + 0.015*\"小時\" + 0.015*\"時間\" + 0.012*\"工資\" + 0.012*\"依法\" + 0.012*\"每日\" + 0.012*\"內容\" + 0.012*\"推定\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:37,540 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"工作\" + 0.005*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"資料\" + 0.004*\"時間\" + 0.004*\"問題\" + 0.004*\"目前\" + 0.003*\"產品\"\n",
      "2025-04-19 00:08:37,540 : INFO : topic diff=0.338915, rho=0.313805\n",
      "2025-04-19 00:08:37,541 : INFO : PROGRESS: pass 1, at document #8000/16310\n",
      "2025-04-19 00:08:37,777 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:37,780 : INFO : topic #0 (0.250): 0.023*\"報名\" + 0.020*\"電話\" + 0.017*\"台北市\" + 0.015*\"活動\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.011*\"通知\" + 0.011*\"人數\" + 0.011*\"地點\" + 0.010*\"方式\"\n",
      "2025-04-19 00:08:37,780 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:37,781 : INFO : topic #2 (0.250): 0.049*\"工作\" + 0.023*\"方式\" + 0.017*\"小時\" + 0.016*\"時間\" + 0.012*\"每日\" + 0.012*\"內容\" + 0.011*\"工資\" + 0.010*\"依法\" + 0.010*\"推定\" + 0.010*\"休息\"\n",
      "2025-04-19 00:08:37,781 : INFO : topic #3 (0.250): 0.015*\"公司\" + 0.009*\"工作\" + 0.006*\"面試\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.004*\"時間\" + 0.004*\"開發\" + 0.004*\"目前\" + 0.004*\"台灣\"\n",
      "2025-04-19 00:08:37,782 : INFO : topic diff=0.340004, rho=0.313805\n",
      "2025-04-19 00:08:37,782 : INFO : PROGRESS: pass 1, at document #10000/16310\n",
      "2025-04-19 00:08:37,986 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:37,988 : INFO : topic #0 (0.250): 0.024*\"報名\" + 0.019*\"電話\" + 0.016*\"台北市\" + 0.016*\"活動\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.011*\"通知\" + 0.011*\"地點\" + 0.010*\"人數\" + 0.010*\"方式\"\n",
      "2025-04-19 00:08:37,989 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:37,989 : INFO : topic #2 (0.250): 0.051*\"工作\" + 0.023*\"方式\" + 0.017*\"小時\" + 0.017*\"時間\" + 0.012*\"每日\" + 0.012*\"內容\" + 0.010*\"聯絡\" + 0.010*\"工資\" + 0.010*\"休息\" + 0.009*\"工時\"\n",
      "2025-04-19 00:08:37,990 : INFO : topic #3 (0.250): 0.015*\"公司\" + 0.010*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.005*\"時間\" + 0.005*\"目前\" + 0.004*\"技術\" + 0.004*\"經驗\"\n",
      "2025-04-19 00:08:37,990 : INFO : topic diff=0.280427, rho=0.313805\n",
      "2025-04-19 00:08:37,990 : INFO : PROGRESS: pass 1, at document #12000/16310\n",
      "2025-04-19 00:08:38,177 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:38,179 : INFO : topic #0 (0.250): 0.025*\"報名\" + 0.018*\"活動\" + 0.018*\"電話\" + 0.015*\"台北市\" + 0.012*\"聯絡\" + 0.011*\"通知\" + 0.011*\"內容\" + 0.011*\"地點\" + 0.010*\"人數\" + 0.010*\"方式\"\n",
      "2025-04-19 00:08:38,179 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:38,180 : INFO : topic #2 (0.250): 0.051*\"工作\" + 0.023*\"方式\" + 0.017*\"小時\" + 0.017*\"時間\" + 0.012*\"每日\" + 0.011*\"內容\" + 0.010*\"工時\" + 0.010*\"聯絡\" + 0.009*\"休息\" + 0.009*\"工資\"\n",
      "2025-04-19 00:08:38,180 : INFO : topic #3 (0.250): 0.014*\"公司\" + 0.009*\"工作\" + 0.006*\"面試\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"開發\" + 0.004*\"技術\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"時間\"\n",
      "2025-04-19 00:08:38,181 : INFO : topic diff=0.293272, rho=0.313805\n",
      "2025-04-19 00:08:38,181 : INFO : PROGRESS: pass 1, at document #14000/16310\n",
      "2025-04-19 00:08:38,359 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:38,361 : INFO : topic #0 (0.250): 0.025*\"報名\" + 0.017*\"活動\" + 0.017*\"電話\" + 0.014*\"台北市\" + 0.012*\"聯絡\" + 0.011*\"通知\" + 0.010*\"地點\" + 0.010*\"人數\" + 0.010*\"內容\" + 0.010*\"方式\"\n",
      "2025-04-19 00:08:38,362 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:38,362 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.022*\"方式\" + 0.017*\"小時\" + 0.017*\"時間\" + 0.011*\"每日\" + 0.011*\"內容\" + 0.010*\"工時\" + 0.009*\"聯絡\" + 0.009*\"工資\" + 0.009*\"休息\"\n",
      "2025-04-19 00:08:38,363 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.004*\"問題\" + 0.004*\"面試\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"目前\" + 0.003*\"員工\" + 0.003*\"美國\"\n",
      "2025-04-19 00:08:38,363 : INFO : topic diff=0.288521, rho=0.313805\n",
      "2025-04-19 00:08:38,364 : INFO : PROGRESS: pass 1, at document #16000/16310\n",
      "2025-04-19 00:08:38,539 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:38,541 : INFO : topic #0 (0.250): 0.026*\"報名\" + 0.017*\"活動\" + 0.015*\"電話\" + 0.013*\"台北市\" + 0.011*\"聯絡\" + 0.011*\"問卷\" + 0.010*\"通知\" + 0.010*\"地點\" + 0.010*\"人數\" + 0.009*\"方式\"\n",
      "2025-04-19 00:08:38,541 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:38,542 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.021*\"方式\" + 0.017*\"小時\" + 0.016*\"時間\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"每日\" + 0.009*\"工資\" + 0.009*\"聯絡\" + 0.009*\"單位\"\n",
      "2025-04-19 00:08:38,542 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.003*\"表示\"\n",
      "2025-04-19 00:08:38,543 : INFO : topic diff=0.255317, rho=0.313805\n",
      "2025-04-19 00:08:38,606 : INFO : -8.459 per-word bound, 351.9 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:08:38,606 : INFO : PROGRESS: pass 1, at document #16310/16310\n",
      "2025-04-19 00:08:38,658 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:38,660 : INFO : topic #0 (0.250): 0.025*\"報名\" + 0.021*\"問卷\" + 0.017*\"活動\" + 0.013*\"電話\" + 0.012*\"研究\" + 0.012*\"台北市\" + 0.010*\"聯絡\" + 0.010*\"地點\" + 0.010*\"填寫\" + 0.009*\"工作\"\n",
      "2025-04-19 00:08:38,661 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:38,661 : INFO : topic #2 (0.250): 0.051*\"工作\" + 0.020*\"小時\" + 0.019*\"方式\" + 0.016*\"時間\" + 0.014*\"工時\" + 0.011*\"內容\" + 0.010*\"每日\" + 0.009*\"聯絡\" + 0.009*\"單位\" + 0.008*\"地點\"\n",
      "2025-04-19 00:08:38,662 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"美國\" + 0.006*\"台灣\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"台積電\"\n",
      "2025-04-19 00:08:38,662 : INFO : topic diff=0.275234, rho=0.313805\n",
      "2025-04-19 00:08:38,663 : INFO : PROGRESS: pass 2, at document #2000/16310\n",
      "2025-04-19 00:08:39,062 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:39,064 : INFO : topic #0 (0.250): 0.029*\"報名\" + 0.023*\"活動\" + 0.020*\"電話\" + 0.016*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"車馬費\" + 0.012*\"人數\" + 0.012*\"通知\" + 0.011*\"聯絡\" + 0.011*\"地點\"\n",
      "2025-04-19 00:08:39,064 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:39,065 : INFO : topic #2 (0.250): 0.047*\"工作\" + 0.023*\"方式\" + 0.016*\"小時\" + 0.016*\"時間\" + 0.012*\"工資\" + 0.011*\"每日\" + 0.011*\"內容\" + 0.011*\"依法\" + 0.010*\"休息\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:39,065 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 00:08:39,066 : INFO : topic diff=0.906902, rho=0.299409\n",
      "2025-04-19 00:08:39,066 : INFO : PROGRESS: pass 2, at document #4000/16310\n",
      "2025-04-19 00:08:39,457 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:39,460 : INFO : topic #0 (0.250): 0.028*\"報名\" + 0.023*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.012*\"車馬費\" + 0.012*\"人數\" + 0.012*\"舉辦\" + 0.012*\"通知\" + 0.011*\"聯絡\" + 0.011*\"地點\"\n",
      "2025-04-19 00:08:39,460 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:39,461 : INFO : topic #2 (0.250): 0.047*\"工作\" + 0.023*\"方式\" + 0.016*\"小時\" + 0.016*\"時間\" + 0.013*\"工資\" + 0.012*\"每日\" + 0.012*\"依法\" + 0.011*\"推定\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 00:08:39,461 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:39,461 : INFO : topic diff=0.390145, rho=0.299409\n",
      "2025-04-19 00:08:39,462 : INFO : PROGRESS: pass 2, at document #6000/16310\n",
      "2025-04-19 00:08:39,807 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:39,809 : INFO : topic #0 (0.250): 0.030*\"報名\" + 0.025*\"活動\" + 0.022*\"電話\" + 0.017*\"台北市\" + 0.013*\"車馬費\" + 0.013*\"人數\" + 0.012*\"舉辦\" + 0.012*\"訪問\" + 0.012*\"通知\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:39,810 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:39,810 : INFO : topic #2 (0.250): 0.047*\"工作\" + 0.024*\"方式\" + 0.016*\"小時\" + 0.016*\"時間\" + 0.012*\"工資\" + 0.012*\"每日\" + 0.012*\"依法\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.011*\"休息\"\n",
      "2025-04-19 00:08:39,811 : INFO : topic #3 (0.250): 0.013*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"晶片\" + 0.003*\"科技\"\n",
      "2025-04-19 00:08:39,811 : INFO : topic diff=0.278405, rho=0.299409\n",
      "2025-04-19 00:08:39,811 : INFO : PROGRESS: pass 2, at document #8000/16310\n",
      "2025-04-19 00:08:40,056 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:40,058 : INFO : topic #0 (0.250): 0.030*\"報名\" + 0.025*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.013*\"車馬費\" + 0.012*\"舉辦\" + 0.012*\"人數\" + 0.011*\"通知\" + 0.011*\"訪問\" + 0.011*\"資料\"\n",
      "2025-04-19 00:08:40,059 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:40,059 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.024*\"方式\" + 0.017*\"時間\" + 0.017*\"小時\" + 0.012*\"每日\" + 0.012*\"內容\" + 0.011*\"工資\" + 0.010*\"依法\" + 0.010*\"休息\" + 0.010*\"推定\"\n",
      "2025-04-19 00:08:40,060 : INFO : topic #3 (0.250): 0.015*\"公司\" + 0.009*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.004*\"開發\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"經驗\"\n",
      "2025-04-19 00:08:40,060 : INFO : topic diff=0.317855, rho=0.299409\n",
      "2025-04-19 00:08:40,060 : INFO : PROGRESS: pass 2, at document #10000/16310\n",
      "2025-04-19 00:08:40,267 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:40,269 : INFO : topic #0 (0.250): 0.031*\"報名\" + 0.025*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.012*\"車馬費\" + 0.012*\"舉辦\" + 0.012*\"人數\" + 0.011*\"通知\" + 0.011*\"資料\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:08:40,270 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:40,270 : INFO : topic #2 (0.250): 0.051*\"工作\" + 0.024*\"方式\" + 0.018*\"時間\" + 0.018*\"小時\" + 0.012*\"每日\" + 0.012*\"內容\" + 0.010*\"工時\" + 0.010*\"聯絡\" + 0.009*\"休息\" + 0.009*\"工資\"\n",
      "2025-04-19 00:08:40,271 : INFO : topic #3 (0.250): 0.016*\"公司\" + 0.010*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.005*\"目前\" + 0.004*\"經驗\" + 0.004*\"比較\"\n",
      "2025-04-19 00:08:40,271 : INFO : topic diff=0.265873, rho=0.299409\n",
      "2025-04-19 00:08:40,271 : INFO : PROGRESS: pass 2, at document #12000/16310\n",
      "2025-04-19 00:08:40,490 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:40,492 : INFO : topic #0 (0.250): 0.031*\"報名\" + 0.027*\"活動\" + 0.019*\"電話\" + 0.014*\"台北市\" + 0.012*\"研究\" + 0.012*\"舉辦\" + 0.012*\"人數\" + 0.011*\"問卷\" + 0.011*\"車馬費\" + 0.011*\"參加\"\n",
      "2025-04-19 00:08:40,493 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:40,494 : INFO : topic #2 (0.250): 0.051*\"工作\" + 0.023*\"方式\" + 0.018*\"時間\" + 0.018*\"小時\" + 0.011*\"每日\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"聯絡\" + 0.009*\"休息\" + 0.009*\"工資\"\n",
      "2025-04-19 00:08:40,494 : INFO : topic #3 (0.250): 0.014*\"公司\" + 0.009*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.004*\"技術\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 00:08:40,494 : INFO : topic diff=0.274682, rho=0.299409\n",
      "2025-04-19 00:08:40,495 : INFO : PROGRESS: pass 2, at document #14000/16310\n",
      "2025-04-19 00:08:40,682 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:40,684 : INFO : topic #0 (0.250): 0.030*\"報名\" + 0.026*\"活動\" + 0.018*\"電話\" + 0.014*\"研究\" + 0.014*\"問卷\" + 0.013*\"台北市\" + 0.012*\"舉辦\" + 0.011*\"人數\" + 0.011*\"參加\" + 0.010*\"通知\"\n",
      "2025-04-19 00:08:40,684 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:40,685 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.023*\"方式\" + 0.018*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"每日\" + 0.011*\"工時\" + 0.009*\"聯絡\" + 0.009*\"休息\" + 0.009*\"單位\"\n",
      "2025-04-19 00:08:40,685 : INFO : topic #3 (0.250): 0.013*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"問題\" + 0.005*\"面試\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"目前\" + 0.003*\"開發\" + 0.003*\"員工\"\n",
      "2025-04-19 00:08:40,686 : INFO : topic diff=0.273865, rho=0.299409\n",
      "2025-04-19 00:08:40,686 : INFO : PROGRESS: pass 2, at document #16000/16310\n",
      "2025-04-19 00:08:40,865 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:40,867 : INFO : topic #0 (0.250): 0.030*\"報名\" + 0.027*\"活動\" + 0.016*\"研究\" + 0.016*\"電話\" + 0.014*\"問卷\" + 0.012*\"台北市\" + 0.011*\"舉辦\" + 0.011*\"人數\" + 0.011*\"參加\" + 0.010*\"通知\"\n",
      "2025-04-19 00:08:40,867 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:40,868 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.021*\"方式\" + 0.017*\"小時\" + 0.017*\"時間\" + 0.012*\"工時\" + 0.011*\"內容\" + 0.010*\"每日\" + 0.009*\"地點\" + 0.009*\"單位\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:08:40,868 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"面試\"\n",
      "2025-04-19 00:08:40,869 : INFO : topic diff=0.244578, rho=0.299409\n",
      "2025-04-19 00:08:40,933 : INFO : -8.443 per-word bound, 348.1 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:08:40,934 : INFO : PROGRESS: pass 2, at document #16310/16310\n",
      "2025-04-19 00:08:40,964 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:40,966 : INFO : topic #0 (0.250): 0.028*\"報名\" + 0.025*\"活動\" + 0.020*\"研究\" + 0.020*\"問卷\" + 0.013*\"電話\" + 0.013*\"台北市\" + 0.011*\"時間\" + 0.011*\"舉辦\" + 0.010*\"人數\" + 0.010*\"參與\"\n",
      "2025-04-19 00:08:40,966 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:40,967 : INFO : topic #2 (0.250): 0.051*\"工作\" + 0.019*\"小時\" + 0.019*\"方式\" + 0.017*\"時間\" + 0.014*\"工時\" + 0.011*\"內容\" + 0.010*\"每日\" + 0.009*\"聯絡\" + 0.009*\"地點\" + 0.008*\"單位\"\n",
      "2025-04-19 00:08:40,967 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 00:08:40,967 : INFO : topic diff=0.262645, rho=0.299409\n",
      "2025-04-19 00:08:40,968 : INFO : PROGRESS: pass 3, at document #2000/16310\n",
      "2025-04-19 00:08:41,339 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:41,341 : INFO : topic #0 (0.250): 0.029*\"報名\" + 0.025*\"活動\" + 0.019*\"電話\" + 0.016*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"參與\" + 0.012*\"車馬費\" + 0.012*\"人數\" + 0.012*\"研究\" + 0.011*\"時間\"\n",
      "2025-04-19 00:08:41,342 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:41,342 : INFO : topic #2 (0.250): 0.048*\"工作\" + 0.023*\"方式\" + 0.017*\"小時\" + 0.016*\"時間\" + 0.011*\"每日\" + 0.011*\"工資\" + 0.011*\"內容\" + 0.010*\"依法\" + 0.010*\"工時\" + 0.010*\"休息\"\n",
      "2025-04-19 00:08:41,343 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 00:08:41,343 : INFO : topic diff=0.779662, rho=0.286829\n",
      "2025-04-19 00:08:41,343 : INFO : PROGRESS: pass 3, at document #4000/16310\n",
      "2025-04-19 00:08:41,716 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:41,718 : INFO : topic #0 (0.250): 0.029*\"報名\" + 0.025*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.012*\"人數\" + 0.011*\"通知\" + 0.011*\"資料\" + 0.011*\"時間\"\n",
      "2025-04-19 00:08:41,718 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:41,721 : INFO : topic #2 (0.250): 0.047*\"工作\" + 0.023*\"方式\" + 0.016*\"小時\" + 0.016*\"時間\" + 0.012*\"工資\" + 0.012*\"每日\" + 0.011*\"依法\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:41,726 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:41,730 : INFO : topic diff=0.353576, rho=0.286829\n",
      "2025-04-19 00:08:41,736 : INFO : PROGRESS: pass 3, at document #6000/16310\n",
      "2025-04-19 00:08:42,074 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:42,076 : INFO : topic #0 (0.250): 0.031*\"報名\" + 0.026*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.013*\"車馬費\" + 0.013*\"舉辦\" + 0.013*\"人數\" + 0.012*\"訪問\" + 0.011*\"資料\" + 0.011*\"通知\"\n",
      "2025-04-19 00:08:42,077 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:42,077 : INFO : topic #2 (0.250): 0.047*\"工作\" + 0.024*\"方式\" + 0.016*\"小時\" + 0.016*\"時間\" + 0.012*\"每日\" + 0.012*\"工資\" + 0.012*\"依法\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.010*\"休息\"\n",
      "2025-04-19 00:08:42,078 : INFO : topic #3 (0.250): 0.013*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.005*\"技術\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"晶片\" + 0.003*\"科技\"\n",
      "2025-04-19 00:08:42,078 : INFO : topic diff=0.258180, rho=0.286829\n",
      "2025-04-19 00:08:42,078 : INFO : PROGRESS: pass 3, at document #8000/16310\n",
      "2025-04-19 00:08:42,317 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:42,319 : INFO : topic #0 (0.250): 0.030*\"報名\" + 0.026*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.012*\"人數\" + 0.012*\"資料\" + 0.011*\"訪問\" + 0.011*\"通知\"\n",
      "2025-04-19 00:08:42,319 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:42,320 : INFO : topic #2 (0.250): 0.049*\"工作\" + 0.024*\"方式\" + 0.017*\"時間\" + 0.017*\"小時\" + 0.012*\"每日\" + 0.011*\"內容\" + 0.010*\"工資\" + 0.010*\"依法\" + 0.010*\"休息\" + 0.009*\"工時\"\n",
      "2025-04-19 00:08:42,321 : INFO : topic #3 (0.250): 0.015*\"公司\" + 0.008*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.005*\"開發\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"經驗\"\n",
      "2025-04-19 00:08:42,321 : INFO : topic diff=0.298122, rho=0.286829\n",
      "2025-04-19 00:08:42,321 : INFO : PROGRESS: pass 3, at document #10000/16310\n",
      "2025-04-19 00:08:42,526 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:42,528 : INFO : topic #0 (0.250): 0.031*\"報名\" + 0.026*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.012*\"舉辦\" + 0.012*\"車馬費\" + 0.012*\"人數\" + 0.012*\"研究\" + 0.012*\"資料\" + 0.011*\"參加\"\n",
      "2025-04-19 00:08:42,529 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:42,529 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.023*\"方式\" + 0.018*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"每日\" + 0.010*\"工時\" + 0.009*\"聯絡\" + 0.009*\"休息\" + 0.009*\"工資\"\n",
      "2025-04-19 00:08:42,529 : INFO : topic #3 (0.250): 0.016*\"公司\" + 0.009*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.005*\"目前\" + 0.004*\"比較\" + 0.004*\"經驗\"\n",
      "2025-04-19 00:08:42,530 : INFO : topic diff=0.250906, rho=0.286829\n",
      "2025-04-19 00:08:42,530 : INFO : PROGRESS: pass 3, at document #12000/16310\n",
      "2025-04-19 00:08:42,725 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:42,727 : INFO : topic #0 (0.250): 0.031*\"報名\" + 0.028*\"活動\" + 0.018*\"電話\" + 0.014*\"台北市\" + 0.013*\"研究\" + 0.013*\"舉辦\" + 0.011*\"問卷\" + 0.011*\"人數\" + 0.011*\"參加\" + 0.011*\"資料\"\n",
      "2025-04-19 00:08:42,728 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:42,728 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.023*\"方式\" + 0.018*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"每日\" + 0.011*\"工時\" + 0.009*\"聯絡\" + 0.009*\"休息\" + 0.008*\"工資\"\n",
      "2025-04-19 00:08:42,729 : INFO : topic #3 (0.250): 0.014*\"公司\" + 0.008*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.004*\"技術\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 00:08:42,729 : INFO : topic diff=0.259103, rho=0.286829\n",
      "2025-04-19 00:08:42,729 : INFO : PROGRESS: pass 3, at document #14000/16310\n",
      "2025-04-19 00:08:42,918 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:42,920 : INFO : topic #0 (0.250): 0.030*\"報名\" + 0.028*\"活動\" + 0.017*\"電話\" + 0.015*\"研究\" + 0.013*\"問卷\" + 0.013*\"台北市\" + 0.012*\"舉辦\" + 0.011*\"人數\" + 0.011*\"參加\" + 0.011*\"資料\"\n",
      "2025-04-19 00:08:42,920 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:42,921 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.022*\"方式\" + 0.018*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"每日\" + 0.009*\"聯絡\" + 0.008*\"地點\" + 0.008*\"休息\"\n",
      "2025-04-19 00:08:42,921 : INFO : topic #3 (0.250): 0.013*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"問題\" + 0.005*\"面試\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"目前\" + 0.003*\"開發\" + 0.003*\"美國\"\n",
      "2025-04-19 00:08:42,922 : INFO : topic diff=0.260128, rho=0.286829\n",
      "2025-04-19 00:08:42,922 : INFO : PROGRESS: pass 3, at document #16000/16310\n",
      "2025-04-19 00:08:43,102 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:43,104 : INFO : topic #0 (0.250): 0.029*\"報名\" + 0.028*\"活動\" + 0.017*\"研究\" + 0.015*\"電話\" + 0.014*\"問卷\" + 0.012*\"舉辦\" + 0.012*\"台北市\" + 0.011*\"參加\" + 0.011*\"人數\" + 0.010*\"參與\"\n",
      "2025-04-19 00:08:43,105 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:43,105 : INFO : topic #2 (0.250): 0.049*\"工作\" + 0.021*\"方式\" + 0.017*\"時間\" + 0.017*\"小時\" + 0.012*\"工時\" + 0.011*\"內容\" + 0.010*\"每日\" + 0.009*\"地點\" + 0.008*\"單位\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:08:43,106 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"晶片\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"面試\"\n",
      "2025-04-19 00:08:43,106 : INFO : topic diff=0.233232, rho=0.286829\n",
      "2025-04-19 00:08:43,193 : INFO : -8.438 per-word bound, 346.7 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:08:43,193 : INFO : PROGRESS: pass 3, at document #16310/16310\n",
      "2025-04-19 00:08:43,223 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:43,225 : INFO : topic #0 (0.250): 0.027*\"報名\" + 0.027*\"活動\" + 0.020*\"研究\" + 0.018*\"問卷\" + 0.014*\"電話\" + 0.012*\"台北市\" + 0.011*\"舉辦\" + 0.011*\"時間\" + 0.010*\"參與\" + 0.010*\"人數\"\n",
      "2025-04-19 00:08:43,225 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:43,226 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.019*\"方式\" + 0.019*\"小時\" + 0.017*\"時間\" + 0.014*\"工時\" + 0.011*\"內容\" + 0.009*\"每日\" + 0.009*\"地點\" + 0.009*\"聯絡\" + 0.008*\"單位\"\n",
      "2025-04-19 00:08:43,226 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:43,227 : INFO : topic diff=0.249669, rho=0.286829\n",
      "2025-04-19 00:08:43,227 : INFO : PROGRESS: pass 4, at document #2000/16310\n",
      "2025-04-19 00:08:43,587 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:43,589 : INFO : topic #0 (0.250): 0.029*\"報名\" + 0.026*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.014*\"舉辦\" + 0.012*\"研究\" + 0.012*\"參與\" + 0.012*\"人數\" + 0.012*\"車馬費\" + 0.011*\"時間\"\n",
      "2025-04-19 00:08:43,590 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:43,590 : INFO : topic #2 (0.250): 0.048*\"工作\" + 0.022*\"方式\" + 0.017*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"每日\" + 0.011*\"工資\" + 0.010*\"工時\" + 0.010*\"依法\" + 0.010*\"休息\"\n",
      "2025-04-19 00:08:43,591 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:43,591 : INFO : topic diff=0.711553, rho=0.275711\n",
      "2025-04-19 00:08:43,591 : INFO : PROGRESS: pass 4, at document #4000/16310\n",
      "2025-04-19 00:08:43,952 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:43,954 : INFO : topic #0 (0.250): 0.029*\"報名\" + 0.026*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.012*\"人數\" + 0.011*\"資料\" + 0.011*\"參與\" + 0.011*\"通知\"\n",
      "2025-04-19 00:08:43,955 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:43,955 : INFO : topic #2 (0.250): 0.047*\"工作\" + 0.023*\"方式\" + 0.016*\"小時\" + 0.016*\"時間\" + 0.012*\"工資\" + 0.012*\"每日\" + 0.011*\"內容\" + 0.011*\"依法\" + 0.010*\"休息\" + 0.010*\"推定\"\n",
      "2025-04-19 00:08:43,956 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:43,956 : INFO : topic diff=0.335299, rho=0.275711\n",
      "2025-04-19 00:08:43,956 : INFO : PROGRESS: pass 4, at document #6000/16310\n",
      "2025-04-19 00:08:44,279 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:44,281 : INFO : topic #0 (0.250): 0.031*\"報名\" + 0.027*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.013*\"車馬費\" + 0.013*\"舉辦\" + 0.013*\"人數\" + 0.012*\"訪問\" + 0.012*\"資料\" + 0.011*\"通知\"\n",
      "2025-04-19 00:08:44,281 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:44,282 : INFO : topic #2 (0.250): 0.047*\"工作\" + 0.023*\"方式\" + 0.016*\"小時\" + 0.016*\"時間\" + 0.012*\"每日\" + 0.012*\"工資\" + 0.011*\"依法\" + 0.011*\"內容\" + 0.010*\"推定\" + 0.010*\"休息\"\n",
      "2025-04-19 00:08:44,282 : INFO : topic #3 (0.250): 0.013*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.005*\"技術\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"晶片\" + 0.003*\"科技\"\n",
      "2025-04-19 00:08:44,282 : INFO : topic diff=0.246474, rho=0.275711\n",
      "2025-04-19 00:08:44,283 : INFO : PROGRESS: pass 4, at document #8000/16310\n",
      "2025-04-19 00:08:44,520 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:44,522 : INFO : topic #0 (0.250): 0.030*\"報名\" + 0.027*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.012*\"人數\" + 0.012*\"資料\" + 0.011*\"訪問\" + 0.011*\"參加\"\n",
      "2025-04-19 00:08:44,522 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:44,523 : INFO : topic #2 (0.250): 0.049*\"工作\" + 0.023*\"方式\" + 0.017*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"每日\" + 0.010*\"工資\" + 0.010*\"依法\" + 0.010*\"休息\" + 0.009*\"工時\"\n",
      "2025-04-19 00:08:44,529 : INFO : topic #3 (0.250): 0.015*\"公司\" + 0.008*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.005*\"台灣\" + 0.004*\"開發\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 00:08:44,532 : INFO : topic diff=0.281795, rho=0.275711\n",
      "2025-04-19 00:08:44,536 : INFO : PROGRESS: pass 4, at document #10000/16310\n",
      "2025-04-19 00:08:44,753 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:44,755 : INFO : topic #0 (0.250): 0.031*\"報名\" + 0.027*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.012*\"舉辦\" + 0.012*\"研究\" + 0.012*\"車馬費\" + 0.012*\"資料\" + 0.012*\"人數\" + 0.011*\"參加\"\n",
      "2025-04-19 00:08:44,755 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:44,756 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.023*\"方式\" + 0.018*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"每日\" + 0.010*\"工時\" + 0.009*\"聯絡\" + 0.009*\"休息\" + 0.009*\"工資\"\n",
      "2025-04-19 00:08:44,756 : INFO : topic #3 (0.250): 0.016*\"公司\" + 0.009*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.005*\"目前\" + 0.004*\"比較\" + 0.004*\"台灣\"\n",
      "2025-04-19 00:08:44,757 : INFO : topic diff=0.238386, rho=0.275711\n",
      "2025-04-19 00:08:44,757 : INFO : PROGRESS: pass 4, at document #12000/16310\n",
      "2025-04-19 00:08:44,951 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:44,953 : INFO : topic #0 (0.250): 0.031*\"報名\" + 0.029*\"活動\" + 0.018*\"電話\" + 0.014*\"研究\" + 0.013*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"參加\" + 0.011*\"人數\" + 0.011*\"資料\" + 0.011*\"問卷\"\n",
      "2025-04-19 00:08:44,954 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:44,954 : INFO : topic #2 (0.250): 0.049*\"工作\" + 0.022*\"方式\" + 0.018*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"每日\" + 0.011*\"工時\" + 0.009*\"聯絡\" + 0.008*\"休息\" + 0.008*\"工資\"\n",
      "2025-04-19 00:08:44,955 : INFO : topic #3 (0.250): 0.014*\"公司\" + 0.008*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.004*\"技術\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 00:08:44,955 : INFO : topic diff=0.246169, rho=0.275711\n",
      "2025-04-19 00:08:44,955 : INFO : PROGRESS: pass 4, at document #14000/16310\n",
      "2025-04-19 00:08:45,148 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:45,149 : INFO : topic #0 (0.250): 0.029*\"報名\" + 0.028*\"活動\" + 0.017*\"電話\" + 0.015*\"研究\" + 0.013*\"問卷\" + 0.012*\"台北市\" + 0.012*\"舉辦\" + 0.011*\"參加\" + 0.011*\"人數\" + 0.011*\"參與\"\n",
      "2025-04-19 00:08:45,150 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:45,151 : INFO : topic #2 (0.250): 0.049*\"工作\" + 0.021*\"方式\" + 0.018*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"每日\" + 0.009*\"聯絡\" + 0.008*\"地點\" + 0.008*\"休息\"\n",
      "2025-04-19 00:08:45,151 : INFO : topic #3 (0.250): 0.013*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"問題\" + 0.005*\"面試\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"目前\" + 0.004*\"開發\" + 0.003*\"美國\"\n",
      "2025-04-19 00:08:45,151 : INFO : topic diff=0.248548, rho=0.275711\n",
      "2025-04-19 00:08:45,152 : INFO : PROGRESS: pass 4, at document #16000/16310\n",
      "2025-04-19 00:08:45,331 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:45,333 : INFO : topic #0 (0.250): 0.029*\"報名\" + 0.029*\"活動\" + 0.017*\"研究\" + 0.016*\"電話\" + 0.013*\"問卷\" + 0.012*\"舉辦\" + 0.012*\"台北市\" + 0.011*\"參加\" + 0.011*\"人數\" + 0.010*\"參與\"\n",
      "2025-04-19 00:08:45,333 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:45,334 : INFO : topic #2 (0.250): 0.049*\"工作\" + 0.020*\"方式\" + 0.017*\"時間\" + 0.017*\"小時\" + 0.012*\"工時\" + 0.011*\"內容\" + 0.009*\"每日\" + 0.009*\"地點\" + 0.008*\"單位\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:08:45,334 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"面試\"\n",
      "2025-04-19 00:08:45,335 : INFO : topic diff=0.223364, rho=0.275711\n",
      "2025-04-19 00:08:45,398 : INFO : -8.434 per-word bound, 345.8 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:08:45,398 : INFO : PROGRESS: pass 4, at document #16310/16310\n",
      "2025-04-19 00:08:45,427 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:45,429 : INFO : topic #0 (0.250): 0.027*\"活動\" + 0.026*\"報名\" + 0.019*\"研究\" + 0.017*\"問卷\" + 0.014*\"電話\" + 0.012*\"台北市\" + 0.011*\"舉辦\" + 0.011*\"時間\" + 0.011*\"參與\" + 0.010*\"人數\"\n",
      "2025-04-19 00:08:45,429 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.013*\"推定\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:08:45,430 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.019*\"方式\" + 0.018*\"小時\" + 0.017*\"時間\" + 0.013*\"工時\" + 0.011*\"內容\" + 0.009*\"每日\" + 0.009*\"地點\" + 0.008*\"聯絡\" + 0.008*\"單位\"\n",
      "2025-04-19 00:08:45,430 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.006*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 00:08:45,430 : INFO : topic diff=0.238909, rho=0.275711\n",
      "2025-04-19 00:08:45,431 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel<num_terms=23261, num_topics=4, decay=0.5, chunksize=2000> in 12.33s', 'datetime': '2025-04-19T00:08:45.431050', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n",
      "2025-04-19 00:08:49,916 : INFO : -7.041 per-word bound, 131.7 perplexity estimate based on a held-out corpus of 16310 documents with 3460358 words\n",
      "2025-04-19 00:08:49,918 : INFO : using ParallelWordOccurrenceAccumulator<processes=7, batch_size=64> to estimate probabilities from sliding windows\n",
      "2025-04-19 00:08:53,359 : INFO : 1 batches submitted to accumulate stats from 64 documents (22660 virtual)\n",
      "2025-04-19 00:08:53,362 : INFO : 2 batches submitted to accumulate stats from 128 documents (45646 virtual)\n",
      "2025-04-19 00:08:53,365 : INFO : 3 batches submitted to accumulate stats from 192 documents (67171 virtual)\n",
      "2025-04-19 00:08:53,367 : INFO : 4 batches submitted to accumulate stats from 256 documents (88330 virtual)\n",
      "2025-04-19 00:08:53,371 : INFO : 5 batches submitted to accumulate stats from 320 documents (109687 virtual)\n",
      "2025-04-19 00:08:53,375 : INFO : 6 batches submitted to accumulate stats from 384 documents (131042 virtual)\n",
      "2025-04-19 00:08:53,378 : INFO : 7 batches submitted to accumulate stats from 448 documents (153774 virtual)\n",
      "2025-04-19 00:08:53,381 : INFO : 8 batches submitted to accumulate stats from 512 documents (176164 virtual)\n",
      "2025-04-19 00:08:53,384 : INFO : 9 batches submitted to accumulate stats from 576 documents (197020 virtual)\n",
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      "2025-04-19 00:08:53,399 : INFO : 12 batches submitted to accumulate stats from 768 documents (265360 virtual)\n",
      "2025-04-19 00:08:53,404 : INFO : 13 batches submitted to accumulate stats from 832 documents (286615 virtual)\n",
      "2025-04-19 00:08:53,411 : INFO : 14 batches submitted to accumulate stats from 896 documents (310833 virtual)\n",
      "2025-04-19 00:08:53,488 : INFO : 15 batches submitted to accumulate stats from 960 documents (331313 virtual)\n",
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      "2025-04-19 00:08:53,505 : INFO : 17 batches submitted to accumulate stats from 1088 documents (368371 virtual)\n",
      "2025-04-19 00:08:53,510 : INFO : 18 batches submitted to accumulate stats from 1152 documents (390334 virtual)\n",
      "2025-04-19 00:08:53,514 : INFO : 19 batches submitted to accumulate stats from 1216 documents (414153 virtual)\n",
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      "2025-04-19 00:08:53,624 : INFO : 23 batches submitted to accumulate stats from 1472 documents (507391 virtual)\n",
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      "2025-04-19 00:08:53,662 : INFO : 26 batches submitted to accumulate stats from 1664 documents (575041 virtual)\n",
      "2025-04-19 00:08:53,667 : INFO : 27 batches submitted to accumulate stats from 1728 documents (598912 virtual)\n",
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      "2025-04-19 00:08:53,741 : INFO : 29 batches submitted to accumulate stats from 1856 documents (648902 virtual)\n",
      "2025-04-19 00:08:53,745 : INFO : 30 batches submitted to accumulate stats from 1920 documents (671126 virtual)\n",
      "2025-04-19 00:08:53,756 : INFO : 31 batches submitted to accumulate stats from 1984 documents (693717 virtual)\n",
      "2025-04-19 00:08:53,802 : INFO : 32 batches submitted to accumulate stats from 2048 documents (714139 virtual)\n",
      "2025-04-19 00:08:53,808 : INFO : 33 batches submitted to accumulate stats from 2112 documents (736202 virtual)\n",
      "2025-04-19 00:08:53,846 : INFO : 34 batches submitted to accumulate stats from 2176 documents (758687 virtual)\n",
      "2025-04-19 00:08:53,894 : INFO : 35 batches submitted to accumulate stats from 2240 documents (779677 virtual)\n",
      "2025-04-19 00:08:53,898 : INFO : 36 batches submitted to accumulate stats from 2304 documents (800483 virtual)\n",
      "2025-04-19 00:08:53,908 : INFO : 37 batches submitted to accumulate stats from 2368 documents (821258 virtual)\n",
      "2025-04-19 00:08:53,927 : INFO : 38 batches submitted to accumulate stats from 2432 documents (844326 virtual)\n",
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      "2025-04-19 00:08:56,429 : INFO : 7 accumulators retrieved from output queue\n",
      "2025-04-19 00:08:56,437 : INFO : accumulated word occurrence stats for 3451622 virtual documents\n",
      "2025-04-19 00:08:56,498 : INFO : using symmetric alpha at 0.2\n",
      "2025-04-19 00:08:56,498 : INFO : using symmetric eta at 0.2\n",
      "2025-04-19 00:08:56,500 : INFO : using serial LDA version on this node\n",
      "2025-04-19 00:08:56,504 : INFO : running online (multi-pass) LDA training, 5 topics, 5 passes over the supplied corpus of 16310 documents, updating model once every 2000 documents, evaluating perplexity every 16310 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2025-04-19 00:08:56,505 : INFO : PROGRESS: pass 0, at document #2000/16310\n",
      "2025-04-19 00:08:57,167 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:57,169 : INFO : topic #0 (0.200): 0.028*\"工作\" + 0.014*\"方式\" + 0.013*\"應徵\" + 0.012*\"推定\" + 0.012*\"空白\" + 0.011*\"單位\" + 0.011*\"砍除\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:08:57,170 : INFO : topic #1 (0.200): 0.029*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.010*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"內容\" + 0.010*\"第一項\" + 0.010*\"情形\"\n",
      "2025-04-19 00:08:57,170 : INFO : topic #2 (0.200): 0.039*\"工作\" + 0.013*\"內容\" + 0.012*\"推定\" + 0.012*\"工資\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"情形\" + 0.010*\"聯絡\" + 0.010*\"砍除\" + 0.010*\"小時\"\n",
      "2025-04-19 00:08:57,171 : INFO : topic #3 (0.200): 0.019*\"工作\" + 0.013*\"方式\" + 0.011*\"砍除\" + 0.010*\"應徵\" + 0.010*\"聯絡人\" + 0.009*\"推定\" + 0.009*\"文字\" + 0.009*\"空白\" + 0.009*\"資訊\" + 0.008*\"情形\"\n",
      "2025-04-19 00:08:57,172 : INFO : topic #4 (0.200): 0.037*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"方式\" + 0.011*\"聯絡\" + 0.010*\"聯絡人\" + 0.010*\"第一項\" + 0.010*\"單位\" + 0.009*\"情形\" + 0.009*\"內容\"\n",
      "2025-04-19 00:08:57,172 : INFO : topic diff=6.258093, rho=1.000000\n",
      "2025-04-19 00:08:57,173 : INFO : PROGRESS: pass 0, at document #4000/16310\n",
      "2025-04-19 00:08:57,783 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:57,786 : INFO : topic #0 (0.200): 0.028*\"工作\" + 0.014*\"應徵\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.011*\"推定\" + 0.011*\"單位\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:08:57,786 : INFO : topic #1 (0.200): 0.029*\"工作\" + 0.015*\"方式\" + 0.012*\"砍除\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"單位\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:57,787 : INFO : topic #2 (0.200): 0.041*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.013*\"內容\" + 0.013*\"工資\" + 0.012*\"小時\" + 0.011*\"應徵\" + 0.011*\"單位\" + 0.010*\"未註明\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:08:57,787 : INFO : topic #3 (0.200): 0.016*\"報名\" + 0.013*\"活動\" + 0.013*\"電話\" + 0.011*\"方式\" + 0.010*\"工作\" + 0.010*\"時間\" + 0.010*\"台北市\" + 0.009*\"聯絡\" + 0.009*\"內容\" + 0.008*\"人數\"\n",
      "2025-04-19 00:08:57,788 : INFO : topic #4 (0.200): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.010*\"砍除\" + 0.010*\"聯絡人\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:57,788 : INFO : topic diff=0.603408, rho=0.707107\n",
      "2025-04-19 00:08:57,789 : INFO : PROGRESS: pass 0, at document #6000/16310\n",
      "2025-04-19 00:08:58,289 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:58,291 : INFO : topic #0 (0.200): 0.028*\"工作\" + 0.013*\"應徵\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"內容\" + 0.010*\"第一項\" + 0.010*\"推定\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:58,292 : INFO : topic #1 (0.200): 0.029*\"工作\" + 0.014*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:58,292 : INFO : topic #2 (0.200): 0.040*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.012*\"小時\" + 0.012*\"內容\" + 0.011*\"工資\" + 0.010*\"單位\" + 0.010*\"依法\" + 0.010*\"時間\" + 0.009*\"應徵\"\n",
      "2025-04-19 00:08:58,293 : INFO : topic #3 (0.200): 0.018*\"報名\" + 0.015*\"活動\" + 0.013*\"電話\" + 0.011*\"台北市\" + 0.010*\"時間\" + 0.008*\"聯絡\" + 0.008*\"資料\" + 0.008*\"內容\" + 0.008*\"方式\" + 0.008*\"人數\"\n",
      "2025-04-19 00:08:58,293 : INFO : topic #4 (0.200): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:58,294 : INFO : topic diff=0.666836, rho=0.577350\n",
      "2025-04-19 00:08:58,294 : INFO : PROGRESS: pass 0, at document #8000/16310\n",
      "2025-04-19 00:08:58,571 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:58,574 : INFO : topic #0 (0.200): 0.028*\"工作\" + 0.012*\"應徵\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"內容\" + 0.010*\"第一項\" + 0.010*\"推定\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:58,574 : INFO : topic #1 (0.200): 0.029*\"工作\" + 0.014*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:58,575 : INFO : topic #2 (0.200): 0.035*\"工作\" + 0.010*\"面試\" + 0.010*\"方式\" + 0.010*\"公司\" + 0.008*\"時間\" + 0.008*\"小時\" + 0.008*\"內容\" + 0.006*\"推定\" + 0.006*\"經驗\" + 0.006*\"工資\"\n",
      "2025-04-19 00:08:58,575 : INFO : topic #3 (0.200): 0.016*\"公司\" + 0.008*\"時間\" + 0.007*\"問題\" + 0.007*\"目前\" + 0.007*\"產品\" + 0.006*\"工程師\" + 0.006*\"資料\" + 0.006*\"使用\" + 0.006*\"報名\" + 0.006*\"活動\"\n",
      "2025-04-19 00:08:58,576 : INFO : topic #4 (0.200): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:58,576 : INFO : topic diff=0.963371, rho=0.500000\n",
      "2025-04-19 00:08:58,577 : INFO : PROGRESS: pass 0, at document #10000/16310\n",
      "2025-04-19 00:08:58,830 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:58,833 : INFO : topic #0 (0.200): 0.028*\"工作\" + 0.012*\"應徵\" + 0.012*\"方式\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"內容\" + 0.010*\"第一項\" + 0.010*\"推定\" + 0.009*\"單位\"\n",
      "2025-04-19 00:08:58,833 : INFO : topic #1 (0.200): 0.029*\"工作\" + 0.014*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:58,834 : INFO : topic #2 (0.200): 0.034*\"工作\" + 0.012*\"面試\" + 0.011*\"公司\" + 0.008*\"方式\" + 0.008*\"時間\" + 0.007*\"內容\" + 0.006*\"小時\" + 0.006*\"經驗\" + 0.006*\"覺得\" + 0.006*\"比較\"\n",
      "2025-04-19 00:08:58,834 : INFO : topic #3 (0.200): 0.017*\"公司\" + 0.008*\"問題\" + 0.008*\"目前\" + 0.007*\"時間\" + 0.007*\"工程師\" + 0.006*\"使用\" + 0.006*\"產品\" + 0.006*\"資料\" + 0.006*\"技術\" + 0.005*\"團隊\"\n",
      "2025-04-19 00:08:58,834 : INFO : topic #4 (0.200): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:58,835 : INFO : topic diff=0.639901, rho=0.447214\n",
      "2025-04-19 00:08:58,839 : INFO : PROGRESS: pass 0, at document #12000/16310\n",
      "2025-04-19 00:08:59,123 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:59,126 : INFO : topic #0 (0.200): 0.027*\"工作\" + 0.012*\"應徵\" + 0.012*\"方式\" + 0.011*\"砍除\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"內容\" + 0.009*\"第一項\" + 0.009*\"推定\" + 0.009*\"單位\"\n",
      "2025-04-19 00:08:59,127 : INFO : topic #1 (0.200): 0.029*\"工作\" + 0.014*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.011*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:59,127 : INFO : topic #2 (0.200): 0.032*\"工作\" + 0.012*\"面試\" + 0.011*\"公司\" + 0.007*\"時間\" + 0.007*\"方式\" + 0.006*\"覺得\" + 0.006*\"比較\" + 0.006*\"經驗\" + 0.006*\"內容\" + 0.005*\"小時\"\n",
      "2025-04-19 00:08:59,128 : INFO : topic #3 (0.200): 0.015*\"公司\" + 0.007*\"問題\" + 0.006*\"目前\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.005*\"台灣\" + 0.005*\"時間\" + 0.005*\"使用\" + 0.004*\"產品\" + 0.004*\"資料\"\n",
      "2025-04-19 00:08:59,128 : INFO : topic #4 (0.200): 0.036*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:59,129 : INFO : topic diff=0.602195, rho=0.408248\n",
      "2025-04-19 00:08:59,129 : INFO : PROGRESS: pass 0, at document #14000/16310\n",
      "2025-04-19 00:08:59,387 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:59,390 : INFO : topic #0 (0.200): 0.026*\"工作\" + 0.012*\"應徵\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.009*\"資訊\" + 0.009*\"內容\" + 0.009*\"單位\" + 0.009*\"第一項\" + 0.009*\"推定\"\n",
      "2025-04-19 00:08:59,390 : INFO : topic #1 (0.200): 0.028*\"工作\" + 0.014*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:08:59,391 : INFO : topic #2 (0.200): 0.031*\"工作\" + 0.011*\"面試\" + 0.011*\"公司\" + 0.006*\"時間\" + 0.006*\"覺得\" + 0.006*\"比較\" + 0.006*\"方式\" + 0.005*\"經驗\" + 0.005*\"真的\" + 0.005*\"內容\"\n",
      "2025-04-19 00:08:59,391 : INFO : topic #3 (0.200): 0.012*\"公司\" + 0.007*\"台灣\" + 0.005*\"問題\" + 0.005*\"技術\" + 0.005*\"目前\" + 0.004*\"工程師\" + 0.004*\"美國\" + 0.004*\"晶片\" + 0.004*\"產業\" + 0.004*\"產品\"\n",
      "2025-04-19 00:08:59,392 : INFO : topic #4 (0.200): 0.036*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.010*\"方式\" + 0.010*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:08:59,392 : INFO : topic diff=0.492179, rho=0.377964\n",
      "2025-04-19 00:08:59,393 : INFO : PROGRESS: pass 0, at document #16000/16310\n",
      "2025-04-19 00:08:59,641 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:59,643 : INFO : topic #0 (0.200): 0.025*\"工作\" + 0.011*\"方式\" + 0.011*\"應徵\" + 0.010*\"砍除\" + 0.010*\"空白\" + 0.009*\"資訊\" + 0.009*\"單位\" + 0.009*\"內容\" + 0.009*\"第一項\" + 0.008*\"推定\"\n",
      "2025-04-19 00:08:59,644 : INFO : topic #1 (0.200): 0.028*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.010*\"文字\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:08:59,644 : INFO : topic #2 (0.200): 0.030*\"工作\" + 0.011*\"公司\" + 0.010*\"面試\" + 0.006*\"時間\" + 0.006*\"比較\" + 0.006*\"覺得\" + 0.006*\"真的\" + 0.005*\"應該\" + 0.005*\"方式\" + 0.005*\"內容\"\n",
      "2025-04-19 00:08:59,645 : INFO : topic #3 (0.200): 0.011*\"公司\" + 0.007*\"台灣\" + 0.006*\"美國\" + 0.005*\"晶片\" + 0.005*\"技術\" + 0.004*\"表示\" + 0.004*\"目前\" + 0.004*\"中國\" + 0.004*\"半導體\" + 0.004*\"問題\"\n",
      "2025-04-19 00:08:59,645 : INFO : topic #4 (0.200): 0.035*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.010*\"情形\" + 0.010*\"方式\" + 0.010*\"砍除\" + 0.010*\"單位\" + 0.010*\"國定假日\"\n",
      "2025-04-19 00:08:59,646 : INFO : topic diff=0.381256, rho=0.353553\n",
      "2025-04-19 00:08:59,720 : INFO : -8.527 per-word bound, 368.9 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:08:59,720 : INFO : PROGRESS: pass 0, at document #16310/16310\n",
      "2025-04-19 00:08:59,760 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:08:59,763 : INFO : topic #0 (0.200): 0.023*\"工作\" + 0.010*\"方式\" + 0.010*\"應徵\" + 0.009*\"單位\" + 0.009*\"砍除\" + 0.009*\"空白\" + 0.008*\"資訊\" + 0.008*\"內容\" + 0.008*\"第一項\" + 0.008*\"推定\"\n",
      "2025-04-19 00:08:59,763 : INFO : topic #1 (0.200): 0.027*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"國定假日\" + 0.010*\"聯絡\" + 0.010*\"文字\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:08:59,764 : INFO : topic #2 (0.200): 0.027*\"工作\" + 0.012*\"公司\" + 0.010*\"面試\" + 0.007*\"真的\" + 0.006*\"覺得\" + 0.006*\"時間\" + 0.006*\"比較\" + 0.006*\"應該\" + 0.005*\"事情\" + 0.005*\"一下\"\n",
      "2025-04-19 00:08:59,764 : INFO : topic #3 (0.200): 0.011*\"公司\" + 0.008*\"美國\" + 0.008*\"台灣\" + 0.006*\"技術\" + 0.005*\"晶片\" + 0.005*\"台積電\" + 0.004*\"表示\" + 0.004*\"中國\" + 0.004*\"台積\" + 0.004*\"科技\"\n",
      "2025-04-19 00:08:59,765 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.011*\"第一項\" + 0.010*\"聯絡\" + 0.010*\"情形\" + 0.010*\"方式\" + 0.010*\"砍除\" + 0.009*\"單位\" + 0.009*\"國定假日\"\n",
      "2025-04-19 00:08:59,765 : INFO : topic diff=0.375175, rho=0.333333\n",
      "2025-04-19 00:08:59,765 : INFO : PROGRESS: pass 1, at document #2000/16310\n",
      "2025-04-19 00:09:00,390 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:00,393 : INFO : topic #0 (0.200): 0.028*\"工作\" + 0.016*\"方式\" + 0.010*\"應徵\" + 0.010*\"內容\" + 0.010*\"工資\" + 0.009*\"聯絡\" + 0.008*\"通知\" + 0.008*\"單位\" + 0.008*\"推定\" + 0.008*\"依法\"\n",
      "2025-04-19 00:09:00,393 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.015*\"方式\" + 0.012*\"推定\" + 0.012*\"砍除\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.010*\"資訊\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:09:00,394 : INFO : topic #2 (0.200): 0.029*\"工作\" + 0.011*\"公司\" + 0.011*\"面試\" + 0.008*\"時間\" + 0.006*\"真的\" + 0.005*\"覺得\" + 0.005*\"比較\" + 0.005*\"方式\" + 0.005*\"小時\" + 0.005*\"應該\"\n",
      "2025-04-19 00:09:00,397 : INFO : topic #3 (0.200): 0.011*\"公司\" + 0.007*\"美國\" + 0.007*\"台灣\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"台積電\" + 0.004*\"表示\" + 0.004*\"中國\" + 0.004*\"科技\" + 0.004*\"台積\"\n",
      "2025-04-19 00:09:00,405 : INFO : topic #4 (0.200): 0.038*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.011*\"方式\" + 0.011*\"聯絡\" + 0.011*\"砍除\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:00,410 : INFO : topic diff=1.157990, rho=0.313805\n",
      "2025-04-19 00:09:00,414 : INFO : PROGRESS: pass 1, at document #4000/16310\n",
      "2025-04-19 00:09:01,024 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:01,027 : INFO : topic #0 (0.200): 0.030*\"工作\" + 0.020*\"方式\" + 0.012*\"內容\" + 0.011*\"通知\" + 0.011*\"聯絡\" + 0.011*\"電話\" + 0.011*\"工資\" + 0.010*\"時間\" + 0.010*\"台北市\" + 0.010*\"依法\"\n",
      "2025-04-19 00:09:01,027 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.015*\"方式\" + 0.012*\"砍除\" + 0.011*\"推定\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"文字\"\n",
      "2025-04-19 00:09:01,028 : INFO : topic #2 (0.200): 0.030*\"工作\" + 0.011*\"面試\" + 0.010*\"公司\" + 0.009*\"時間\" + 0.006*\"小時\" + 0.005*\"方式\" + 0.005*\"經驗\" + 0.005*\"真的\" + 0.005*\"內容\" + 0.005*\"比較\"\n",
      "2025-04-19 00:09:01,028 : INFO : topic #3 (0.200): 0.010*\"公司\" + 0.007*\"美國\" + 0.007*\"台灣\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"報名\" + 0.004*\"活動\" + 0.004*\"台積電\" + 0.004*\"資料\" + 0.004*\"表示\"\n",
      "2025-04-19 00:09:01,029 : INFO : topic #4 (0.200): 0.037*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:01,029 : INFO : topic diff=0.487175, rho=0.313805\n",
      "2025-04-19 00:09:01,030 : INFO : PROGRESS: pass 1, at document #6000/16310\n",
      "2025-04-19 00:09:01,520 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:01,522 : INFO : topic #0 (0.200): 0.032*\"工作\" + 0.023*\"方式\" + 0.013*\"內容\" + 0.013*\"聯絡\" + 0.013*\"通知\" + 0.012*\"電話\" + 0.012*\"時間\" + 0.011*\"依法\" + 0.011*\"台北市\" + 0.011*\"工資\"\n",
      "2025-04-19 00:09:01,523 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:09:01,523 : INFO : topic #2 (0.200): 0.030*\"工作\" + 0.011*\"公司\" + 0.010*\"面試\" + 0.009*\"時間\" + 0.007*\"經驗\" + 0.006*\"小時\" + 0.005*\"方式\" + 0.005*\"內容\" + 0.005*\"比較\" + 0.004*\"真的\"\n",
      "2025-04-19 00:09:01,524 : INFO : topic #3 (0.200): 0.010*\"公司\" + 0.007*\"報名\" + 0.006*\"活動\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"資料\" + 0.004*\"技術\" + 0.004*\"進行\" + 0.004*\"使用\" + 0.004*\"產品\"\n",
      "2025-04-19 00:09:01,524 : INFO : topic #4 (0.200): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:01,525 : INFO : topic diff=0.326379, rho=0.313805\n",
      "2025-04-19 00:09:01,525 : INFO : PROGRESS: pass 1, at document #8000/16310\n",
      "2025-04-19 00:09:01,798 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:01,801 : INFO : topic #0 (0.200): 0.032*\"工作\" + 0.023*\"方式\" + 0.013*\"內容\" + 0.013*\"聯絡\" + 0.012*\"通知\" + 0.012*\"時間\" + 0.012*\"電話\" + 0.011*\"台北市\" + 0.011*\"依法\" + 0.011*\"小時\"\n",
      "2025-04-19 00:09:01,801 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:09:01,802 : INFO : topic #2 (0.200): 0.027*\"工作\" + 0.015*\"公司\" + 0.013*\"面試\" + 0.009*\"時間\" + 0.008*\"經驗\" + 0.006*\"覺得\" + 0.006*\"比較\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.006*\"開發\"\n",
      "2025-04-19 00:09:01,802 : INFO : topic #3 (0.200): 0.012*\"公司\" + 0.006*\"技術\" + 0.006*\"台灣\" + 0.006*\"產品\" + 0.005*\"資料\" + 0.005*\"目前\" + 0.005*\"報名\" + 0.005*\"使用\" + 0.005*\"活動\" + 0.005*\"美國\"\n",
      "2025-04-19 00:09:01,803 : INFO : topic #4 (0.200): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:01,803 : INFO : topic diff=0.435202, rho=0.313805\n",
      "2025-04-19 00:09:01,803 : INFO : PROGRESS: pass 1, at document #10000/16310\n",
      "2025-04-19 00:09:02,057 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:02,059 : INFO : topic #0 (0.200): 0.032*\"工作\" + 0.024*\"方式\" + 0.013*\"內容\" + 0.013*\"聯絡\" + 0.012*\"時間\" + 0.012*\"通知\" + 0.012*\"電話\" + 0.011*\"小時\" + 0.011*\"台北市\" + 0.011*\"依法\"\n",
      "2025-04-19 00:09:02,060 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:09:02,060 : INFO : topic #2 (0.200): 0.024*\"工作\" + 0.016*\"公司\" + 0.013*\"面試\" + 0.008*\"時間\" + 0.008*\"經驗\" + 0.007*\"問題\" + 0.007*\"比較\" + 0.006*\"開發\" + 0.006*\"覺得\" + 0.006*\"工程師\"\n",
      "2025-04-19 00:09:02,061 : INFO : topic #3 (0.200): 0.012*\"公司\" + 0.006*\"產品\" + 0.006*\"技術\" + 0.006*\"台灣\" + 0.006*\"資料\" + 0.005*\"使用\" + 0.005*\"目前\" + 0.005*\"問題\" + 0.004*\"美國\" + 0.004*\"報名\"\n",
      "2025-04-19 00:09:02,061 : INFO : topic #4 (0.200): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:02,061 : INFO : topic diff=0.325595, rho=0.313805\n",
      "2025-04-19 00:09:02,062 : INFO : PROGRESS: pass 1, at document #12000/16310\n",
      "2025-04-19 00:09:02,310 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:02,312 : INFO : topic #0 (0.200): 0.032*\"工作\" + 0.024*\"方式\" + 0.014*\"聯絡\" + 0.013*\"內容\" + 0.013*\"時間\" + 0.012*\"通知\" + 0.012*\"電話\" + 0.011*\"小時\" + 0.011*\"台北市\" + 0.011*\"地點\"\n",
      "2025-04-19 00:09:02,313 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"第一項\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:09:02,313 : INFO : topic #2 (0.200): 0.023*\"工作\" + 0.016*\"公司\" + 0.013*\"面試\" + 0.008*\"時間\" + 0.008*\"經驗\" + 0.007*\"比較\" + 0.007*\"問題\" + 0.006*\"開發\" + 0.006*\"覺得\" + 0.006*\"工程師\"\n",
      "2025-04-19 00:09:02,314 : INFO : topic #3 (0.200): 0.010*\"公司\" + 0.006*\"台灣\" + 0.005*\"技術\" + 0.005*\"產品\" + 0.005*\"使用\" + 0.004*\"目前\" + 0.004*\"資料\" + 0.004*\"美國\" + 0.004*\"問題\" + 0.003*\"產業\"\n",
      "2025-04-19 00:09:02,314 : INFO : topic #4 (0.200): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:02,315 : INFO : topic diff=0.323943, rho=0.313805\n",
      "2025-04-19 00:09:02,315 : INFO : PROGRESS: pass 1, at document #14000/16310\n",
      "2025-04-19 00:09:02,568 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:02,570 : INFO : topic #0 (0.200): 0.032*\"工作\" + 0.023*\"方式\" + 0.013*\"聯絡\" + 0.013*\"內容\" + 0.012*\"時間\" + 0.012*\"通知\" + 0.011*\"電話\" + 0.011*\"小時\" + 0.011*\"地點\" + 0.011*\"台北市\"\n",
      "2025-04-19 00:09:02,571 : INFO : topic #1 (0.200): 0.029*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"第一項\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:02,571 : INFO : topic #2 (0.200): 0.024*\"工作\" + 0.016*\"公司\" + 0.012*\"面試\" + 0.007*\"時間\" + 0.007*\"經驗\" + 0.007*\"比較\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"覺得\" + 0.005*\"開發\"\n",
      "2025-04-19 00:09:02,572 : INFO : topic #3 (0.200): 0.009*\"公司\" + 0.008*\"台灣\" + 0.005*\"技術\" + 0.005*\"美國\" + 0.004*\"晶片\" + 0.004*\"表示\" + 0.004*\"產業\" + 0.004*\"科技\" + 0.004*\"產品\" + 0.004*\"員工\"\n",
      "2025-04-19 00:09:02,573 : INFO : topic #4 (0.200): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:02,573 : INFO : topic diff=0.309660, rho=0.313805\n",
      "2025-04-19 00:09:02,573 : INFO : PROGRESS: pass 1, at document #16000/16310\n",
      "2025-04-19 00:09:02,785 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:02,787 : INFO : topic #0 (0.200): 0.030*\"工作\" + 0.023*\"方式\" + 0.013*\"聯絡\" + 0.012*\"時間\" + 0.012*\"內容\" + 0.012*\"通知\" + 0.012*\"小時\" + 0.011*\"電話\" + 0.011*\"地點\" + 0.011*\"工資\"\n",
      "2025-04-19 00:09:02,788 : INFO : topic #1 (0.200): 0.029*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"第一項\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:02,788 : INFO : topic #2 (0.200): 0.023*\"工作\" + 0.017*\"公司\" + 0.011*\"面試\" + 0.007*\"時間\" + 0.007*\"工程師\" + 0.007*\"比較\" + 0.006*\"經驗\" + 0.006*\"問題\" + 0.006*\"覺得\" + 0.006*\"主管\"\n",
      "2025-04-19 00:09:02,789 : INFO : topic #3 (0.200): 0.009*\"公司\" + 0.008*\"台灣\" + 0.006*\"美國\" + 0.005*\"晶片\" + 0.005*\"技術\" + 0.005*\"表示\" + 0.004*\"中國\" + 0.004*\"半導體\" + 0.004*\"台積電\" + 0.004*\"員工\"\n",
      "2025-04-19 00:09:02,789 : INFO : topic #4 (0.200): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:02,790 : INFO : topic diff=0.285886, rho=0.313805\n",
      "2025-04-19 00:09:02,859 : INFO : -8.354 per-word bound, 327.1 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:09:02,860 : INFO : PROGRESS: pass 1, at document #16310/16310\n",
      "2025-04-19 00:09:02,895 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:02,897 : INFO : topic #0 (0.200): 0.033*\"工作\" + 0.021*\"方式\" + 0.014*\"小時\" + 0.013*\"時間\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.011*\"通知\" + 0.011*\"地點\" + 0.010*\"電話\" + 0.010*\"工資\"\n",
      "2025-04-19 00:09:02,898 : INFO : topic #1 (0.200): 0.029*\"工作\" + 0.014*\"方式\" + 0.011*\"砍除\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.010*\"第一項\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:02,898 : INFO : topic #2 (0.200): 0.021*\"工作\" + 0.017*\"公司\" + 0.010*\"面試\" + 0.007*\"知道\" + 0.007*\"真的\" + 0.006*\"問題\" + 0.006*\"時間\" + 0.006*\"工程師\" + 0.006*\"比較\" + 0.006*\"覺得\"\n",
      "2025-04-19 00:09:02,899 : INFO : topic #3 (0.200): 0.009*\"公司\" + 0.008*\"美國\" + 0.008*\"台灣\" + 0.006*\"晶片\" + 0.006*\"技術\" + 0.005*\"表示\" + 0.005*\"台積電\" + 0.004*\"中國\" + 0.004*\"科技\" + 0.004*\"台積\"\n",
      "2025-04-19 00:09:02,899 : INFO : topic #4 (0.200): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:02,899 : INFO : topic diff=0.320931, rho=0.313805\n",
      "2025-04-19 00:09:02,900 : INFO : PROGRESS: pass 2, at document #2000/16310\n",
      "2025-04-19 00:09:03,477 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:03,479 : INFO : topic #0 (0.200): 0.035*\"工作\" + 0.024*\"方式\" + 0.016*\"時間\" + 0.014*\"小時\" + 0.013*\"內容\" + 0.012*\"通知\" + 0.012*\"聯絡\" + 0.012*\"電話\" + 0.012*\"工資\" + 0.012*\"台北市\"\n",
      "2025-04-19 00:09:03,480 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.013*\"方式\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:03,480 : INFO : topic #2 (0.200): 0.021*\"工作\" + 0.016*\"公司\" + 0.011*\"面試\" + 0.006*\"時間\" + 0.006*\"問題\" + 0.006*\"知道\" + 0.006*\"真的\" + 0.006*\"比較\" + 0.006*\"工程師\" + 0.006*\"經驗\"\n",
      "2025-04-19 00:09:03,481 : INFO : topic #3 (0.200): 0.009*\"公司\" + 0.008*\"美國\" + 0.008*\"台灣\" + 0.006*\"晶片\" + 0.005*\"技術\" + 0.005*\"表示\" + 0.005*\"台積電\" + 0.004*\"中國\" + 0.004*\"科技\" + 0.004*\"台積\"\n",
      "2025-04-19 00:09:03,481 : INFO : topic #4 (0.200): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:03,481 : INFO : topic diff=0.930425, rho=0.299409\n",
      "2025-04-19 00:09:03,482 : INFO : PROGRESS: pass 2, at document #4000/16310\n",
      "2025-04-19 00:09:04,071 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:04,073 : INFO : topic #0 (0.200): 0.034*\"工作\" + 0.023*\"方式\" + 0.016*\"時間\" + 0.014*\"小時\" + 0.013*\"內容\" + 0.012*\"電話\" + 0.012*\"通知\" + 0.012*\"聯絡\" + 0.011*\"工資\" + 0.011*\"地點\"\n",
      "2025-04-19 00:09:04,074 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.010*\"聯絡\" + 0.010*\"資訊\" + 0.010*\"空白\" + 0.010*\"推定\"\n",
      "2025-04-19 00:09:04,074 : INFO : topic #2 (0.200): 0.021*\"工作\" + 0.016*\"公司\" + 0.011*\"面試\" + 0.007*\"時間\" + 0.006*\"問題\" + 0.006*\"經驗\" + 0.006*\"知道\" + 0.006*\"比較\" + 0.005*\"真的\" + 0.005*\"工程師\"\n",
      "2025-04-19 00:09:04,075 : INFO : topic #3 (0.200): 0.009*\"公司\" + 0.008*\"美國\" + 0.008*\"台灣\" + 0.006*\"晶片\" + 0.005*\"技術\" + 0.005*\"表示\" + 0.004*\"台積電\" + 0.004*\"中國\" + 0.004*\"科技\" + 0.004*\"台積\"\n",
      "2025-04-19 00:09:04,075 : INFO : topic #4 (0.200): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:04,076 : INFO : topic diff=0.371374, rho=0.299409\n",
      "2025-04-19 00:09:04,076 : INFO : PROGRESS: pass 2, at document #6000/16310\n",
      "2025-04-19 00:09:04,544 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:04,546 : INFO : topic #0 (0.200): 0.032*\"工作\" + 0.023*\"方式\" + 0.016*\"時間\" + 0.013*\"小時\" + 0.013*\"電話\" + 0.013*\"內容\" + 0.012*\"通知\" + 0.012*\"聯絡\" + 0.012*\"報名\" + 0.011*\"台北市\"\n",
      "2025-04-19 00:09:04,547 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.011*\"文字\" + 0.010*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:04,547 : INFO : topic #2 (0.200): 0.021*\"工作\" + 0.016*\"公司\" + 0.010*\"面試\" + 0.007*\"經驗\" + 0.007*\"時間\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.005*\"比較\" + 0.005*\"知道\" + 0.005*\"需要\"\n",
      "2025-04-19 00:09:04,548 : INFO : topic #3 (0.200): 0.009*\"公司\" + 0.007*\"台灣\" + 0.007*\"美國\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.005*\"產品\" + 0.004*\"表示\" + 0.004*\"科技\" + 0.004*\"台積電\" + 0.004*\"中國\"\n",
      "2025-04-19 00:09:04,548 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"第一項\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:04,548 : INFO : topic diff=0.251031, rho=0.299409\n",
      "2025-04-19 00:09:04,549 : INFO : PROGRESS: pass 2, at document #8000/16310\n",
      "2025-04-19 00:09:04,800 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:04,802 : INFO : topic #0 (0.200): 0.033*\"工作\" + 0.023*\"方式\" + 0.016*\"時間\" + 0.014*\"小時\" + 0.013*\"電話\" + 0.013*\"內容\" + 0.012*\"聯絡\" + 0.012*\"台北市\" + 0.012*\"通知\" + 0.011*\"報名\"\n",
      "2025-04-19 00:09:04,802 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.011*\"文字\" + 0.010*\"資訊\" + 0.010*\"空白\" + 0.010*\"聯絡\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:04,803 : INFO : topic #2 (0.200): 0.020*\"工作\" + 0.018*\"公司\" + 0.011*\"面試\" + 0.007*\"經驗\" + 0.007*\"問題\" + 0.007*\"工程師\" + 0.007*\"時間\" + 0.006*\"開發\" + 0.006*\"比較\" + 0.006*\"覺得\"\n",
      "2025-04-19 00:09:04,804 : INFO : topic #3 (0.200): 0.009*\"公司\" + 0.008*\"台灣\" + 0.006*\"產品\" + 0.006*\"美國\" + 0.006*\"技術\" + 0.004*\"使用\" + 0.004*\"科技\" + 0.004*\"產業\" + 0.004*\"資料\" + 0.004*\"進行\"\n",
      "2025-04-19 00:09:04,804 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\"\n",
      "2025-04-19 00:09:04,804 : INFO : topic diff=0.374744, rho=0.299409\n",
      "2025-04-19 00:09:04,804 : INFO : PROGRESS: pass 2, at document #10000/16310\n",
      "2025-04-19 00:09:05,030 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:05,032 : INFO : topic #0 (0.200): 0.033*\"工作\" + 0.024*\"方式\" + 0.017*\"時間\" + 0.014*\"小時\" + 0.013*\"聯絡\" + 0.013*\"內容\" + 0.012*\"電話\" + 0.012*\"台北市\" + 0.012*\"報名\" + 0.011*\"通知\"\n",
      "2025-04-19 00:09:05,033 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"砍除\" + 0.011*\"文字\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"空白\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:05,033 : INFO : topic #2 (0.200): 0.019*\"工作\" + 0.018*\"公司\" + 0.012*\"面試\" + 0.008*\"問題\" + 0.007*\"經驗\" + 0.007*\"時間\" + 0.007*\"工程師\" + 0.006*\"開發\" + 0.006*\"比較\" + 0.006*\"覺得\"\n",
      "2025-04-19 00:09:05,034 : INFO : topic #3 (0.200): 0.009*\"公司\" + 0.008*\"台灣\" + 0.007*\"產品\" + 0.006*\"技術\" + 0.006*\"美國\" + 0.004*\"使用\" + 0.004*\"資料\" + 0.004*\"產業\" + 0.004*\"研究\" + 0.004*\"進行\"\n",
      "2025-04-19 00:09:05,034 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\"\n",
      "2025-04-19 00:09:05,034 : INFO : topic diff=0.288282, rho=0.299409\n",
      "2025-04-19 00:09:05,035 : INFO : PROGRESS: pass 2, at document #12000/16310\n",
      "2025-04-19 00:09:05,255 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:05,258 : INFO : topic #0 (0.200): 0.033*\"工作\" + 0.024*\"方式\" + 0.017*\"時間\" + 0.015*\"小時\" + 0.013*\"聯絡\" + 0.012*\"內容\" + 0.012*\"報名\" + 0.012*\"電話\" + 0.012*\"台北市\" + 0.011*\"通知\"\n",
      "2025-04-19 00:09:05,258 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"砍除\" + 0.011*\"文字\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"空白\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:05,258 : INFO : topic #2 (0.200): 0.019*\"工作\" + 0.018*\"公司\" + 0.011*\"面試\" + 0.008*\"問題\" + 0.007*\"經驗\" + 0.007*\"工程師\" + 0.007*\"時間\" + 0.006*\"開發\" + 0.006*\"比較\" + 0.006*\"覺得\"\n",
      "2025-04-19 00:09:05,259 : INFO : topic #3 (0.200): 0.008*\"公司\" + 0.008*\"台灣\" + 0.005*\"美國\" + 0.005*\"技術\" + 0.005*\"產品\" + 0.004*\"晶片\" + 0.004*\"產業\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"員工\"\n",
      "2025-04-19 00:09:05,259 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\"\n",
      "2025-04-19 00:09:05,260 : INFO : topic diff=0.299128, rho=0.299409\n",
      "2025-04-19 00:09:05,260 : INFO : PROGRESS: pass 2, at document #14000/16310\n",
      "2025-04-19 00:09:05,478 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:05,481 : INFO : topic #0 (0.200): 0.032*\"工作\" + 0.023*\"方式\" + 0.017*\"時間\" + 0.014*\"小時\" + 0.013*\"聯絡\" + 0.012*\"報名\" + 0.012*\"內容\" + 0.012*\"電話\" + 0.011*\"台北市\" + 0.011*\"通知\"\n",
      "2025-04-19 00:09:05,481 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"砍除\" + 0.011*\"文字\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"空白\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:05,482 : INFO : topic #2 (0.200): 0.019*\"工作\" + 0.018*\"公司\" + 0.010*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.006*\"經驗\" + 0.006*\"時間\" + 0.006*\"比較\" + 0.005*\"開發\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:05,482 : INFO : topic #3 (0.200): 0.008*\"台灣\" + 0.008*\"公司\" + 0.006*\"美國\" + 0.005*\"晶片\" + 0.005*\"技術\" + 0.005*\"表示\" + 0.004*\"科技\" + 0.004*\"產業\" + 0.004*\"員工\" + 0.004*\"半導體\"\n",
      "2025-04-19 00:09:05,483 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\"\n",
      "2025-04-19 00:09:05,483 : INFO : topic diff=0.293956, rho=0.299409\n",
      "2025-04-19 00:09:05,485 : INFO : PROGRESS: pass 2, at document #16000/16310\n",
      "2025-04-19 00:09:05,716 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:05,719 : INFO : topic #0 (0.200): 0.032*\"工作\" + 0.023*\"方式\" + 0.016*\"時間\" + 0.014*\"小時\" + 0.013*\"報名\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.011*\"台北市\" + 0.011*\"電話\" + 0.011*\"通知\"\n",
      "2025-04-19 00:09:05,719 : INFO : topic #1 (0.200): 0.029*\"工作\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"砍除\" + 0.011*\"文字\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"空白\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:05,720 : INFO : topic #2 (0.200): 0.019*\"工作\" + 0.018*\"公司\" + 0.010*\"面試\" + 0.008*\"工程師\" + 0.007*\"問題\" + 0.006*\"時間\" + 0.006*\"比較\" + 0.006*\"經驗\" + 0.005*\"知道\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:05,720 : INFO : topic #3 (0.200): 0.008*\"台灣\" + 0.008*\"公司\" + 0.007*\"美國\" + 0.006*\"晶片\" + 0.005*\"表示\" + 0.005*\"技術\" + 0.005*\"中國\" + 0.005*\"半導體\" + 0.004*\"台積電\" + 0.004*\"員工\"\n",
      "2025-04-19 00:09:05,721 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\"\n",
      "2025-04-19 00:09:05,721 : INFO : topic diff=0.270375, rho=0.299409\n",
      "2025-04-19 00:09:05,791 : INFO : -8.301 per-word bound, 315.5 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:09:05,791 : INFO : PROGRESS: pass 2, at document #16310/16310\n",
      "2025-04-19 00:09:05,826 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:05,829 : INFO : topic #0 (0.200): 0.034*\"工作\" + 0.021*\"方式\" + 0.017*\"時間\" + 0.016*\"小時\" + 0.014*\"報名\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.011*\"地點\" + 0.011*\"台北市\" + 0.011*\"活動\"\n",
      "2025-04-19 00:09:05,829 : INFO : topic #1 (0.200): 0.029*\"工作\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"砍除\" + 0.011*\"文字\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"內容\" + 0.010*\"空白\"\n",
      "2025-04-19 00:09:05,830 : INFO : topic #2 (0.200): 0.018*\"公司\" + 0.017*\"工作\" + 0.009*\"面試\" + 0.007*\"問題\" + 0.007*\"工程師\" + 0.007*\"知道\" + 0.006*\"真的\" + 0.006*\"時間\" + 0.006*\"比較\" + 0.006*\"覺得\"\n",
      "2025-04-19 00:09:05,830 : INFO : topic #3 (0.200): 0.009*\"美國\" + 0.009*\"公司\" + 0.009*\"台灣\" + 0.006*\"晶片\" + 0.006*\"技術\" + 0.005*\"表示\" + 0.005*\"台積電\" + 0.005*\"中國\" + 0.005*\"科技\" + 0.005*\"台積\"\n",
      "2025-04-19 00:09:05,830 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"國定假日\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:05,831 : INFO : topic diff=0.297525, rho=0.299409\n",
      "2025-04-19 00:09:05,831 : INFO : PROGRESS: pass 3, at document #2000/16310\n",
      "2025-04-19 00:09:06,394 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:06,397 : INFO : topic #0 (0.200): 0.032*\"工作\" + 0.022*\"方式\" + 0.017*\"時間\" + 0.014*\"小時\" + 0.012*\"報名\" + 0.012*\"電話\" + 0.012*\"內容\" + 0.011*\"通知\" + 0.011*\"聯絡\" + 0.011*\"台北市\"\n",
      "2025-04-19 00:09:06,397 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.011*\"文字\" + 0.010*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"推定\"\n",
      "2025-04-19 00:09:06,398 : INFO : topic #2 (0.200): 0.017*\"公司\" + 0.017*\"工作\" + 0.009*\"面試\" + 0.007*\"問題\" + 0.007*\"工程師\" + 0.006*\"知道\" + 0.006*\"時間\" + 0.006*\"真的\" + 0.005*\"比較\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:06,398 : INFO : topic #3 (0.200): 0.009*\"美國\" + 0.009*\"台灣\" + 0.009*\"公司\" + 0.006*\"晶片\" + 0.006*\"技術\" + 0.005*\"表示\" + 0.005*\"台積電\" + 0.005*\"中國\" + 0.005*\"科技\" + 0.005*\"台積\"\n",
      "2025-04-19 00:09:06,399 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"方式\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:06,399 : INFO : topic diff=0.816685, rho=0.286829\n",
      "2025-04-19 00:09:06,399 : INFO : PROGRESS: pass 3, at document #4000/16310\n",
      "2025-04-19 00:09:06,966 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:06,968 : INFO : topic #0 (0.200): 0.030*\"工作\" + 0.021*\"方式\" + 0.016*\"時間\" + 0.013*\"小時\" + 0.012*\"電話\" + 0.012*\"報名\" + 0.012*\"內容\" + 0.011*\"通知\" + 0.011*\"聯絡\" + 0.011*\"地點\"\n",
      "2025-04-19 00:09:06,969 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"文字\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:09:06,969 : INFO : topic #2 (0.200): 0.017*\"工作\" + 0.017*\"公司\" + 0.009*\"面試\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"知道\" + 0.006*\"時間\" + 0.005*\"比較\" + 0.005*\"經驗\" + 0.005*\"真的\"\n",
      "2025-04-19 00:09:06,974 : INFO : topic #3 (0.200): 0.009*\"美國\" + 0.009*\"台灣\" + 0.009*\"公司\" + 0.006*\"晶片\" + 0.005*\"技術\" + 0.005*\"表示\" + 0.005*\"台積電\" + 0.005*\"中國\" + 0.005*\"科技\" + 0.005*\"台積\"\n",
      "2025-04-19 00:09:06,978 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:06,981 : INFO : topic diff=0.338138, rho=0.286829\n",
      "2025-04-19 00:09:06,987 : INFO : PROGRESS: pass 3, at document #6000/16310\n",
      "2025-04-19 00:09:07,466 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:07,469 : INFO : topic #0 (0.200): 0.029*\"工作\" + 0.021*\"方式\" + 0.016*\"時間\" + 0.013*\"報名\" + 0.013*\"電話\" + 0.013*\"小時\" + 0.012*\"內容\" + 0.012*\"活動\" + 0.011*\"通知\" + 0.011*\"台北市\"\n",
      "2025-04-19 00:09:07,469 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"文字\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:07,470 : INFO : topic #2 (0.200): 0.017*\"公司\" + 0.017*\"工作\" + 0.009*\"面試\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"經驗\" + 0.006*\"時間\" + 0.006*\"知道\" + 0.005*\"比較\" + 0.005*\"需要\"\n",
      "2025-04-19 00:09:07,470 : INFO : topic #3 (0.200): 0.009*\"公司\" + 0.008*\"台灣\" + 0.008*\"美國\" + 0.006*\"晶片\" + 0.005*\"技術\" + 0.005*\"表示\" + 0.005*\"科技\" + 0.005*\"台積電\" + 0.004*\"中國\" + 0.004*\"產品\"\n",
      "2025-04-19 00:09:07,470 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:07,471 : INFO : topic diff=0.219076, rho=0.286829\n",
      "2025-04-19 00:09:07,471 : INFO : PROGRESS: pass 3, at document #8000/16310\n",
      "2025-04-19 00:09:07,708 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:07,710 : INFO : topic #0 (0.200): 0.029*\"工作\" + 0.022*\"方式\" + 0.016*\"時間\" + 0.013*\"小時\" + 0.013*\"報名\" + 0.012*\"電話\" + 0.012*\"內容\" + 0.011*\"聯絡\" + 0.011*\"活動\" + 0.011*\"台北市\"\n",
      "2025-04-19 00:09:07,711 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"文字\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:09:07,711 : INFO : topic #2 (0.200): 0.018*\"公司\" + 0.018*\"工作\" + 0.010*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.007*\"經驗\" + 0.006*\"時間\" + 0.006*\"開發\" + 0.005*\"比較\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:07,712 : INFO : topic #3 (0.200): 0.009*\"公司\" + 0.009*\"台灣\" + 0.008*\"美國\" + 0.006*\"技術\" + 0.006*\"產品\" + 0.005*\"科技\" + 0.004*\"晶片\" + 0.004*\"產業\" + 0.004*\"市場\" + 0.004*\"員工\"\n",
      "2025-04-19 00:09:07,713 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:07,713 : INFO : topic diff=0.334620, rho=0.286829\n",
      "2025-04-19 00:09:07,713 : INFO : PROGRESS: pass 3, at document #10000/16310\n",
      "2025-04-19 00:09:07,929 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:07,931 : INFO : topic #0 (0.200): 0.030*\"工作\" + 0.022*\"方式\" + 0.017*\"時間\" + 0.014*\"小時\" + 0.013*\"報名\" + 0.012*\"聯絡\" + 0.012*\"電話\" + 0.012*\"內容\" + 0.012*\"活動\" + 0.011*\"台北市\"\n",
      "2025-04-19 00:09:07,932 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"文字\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:09:07,932 : INFO : topic #2 (0.200): 0.018*\"公司\" + 0.017*\"工作\" + 0.011*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.007*\"經驗\" + 0.006*\"時間\" + 0.006*\"開發\" + 0.006*\"比較\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:07,933 : INFO : topic #3 (0.200): 0.009*\"台灣\" + 0.009*\"公司\" + 0.007*\"美國\" + 0.006*\"產品\" + 0.006*\"技術\" + 0.005*\"產業\" + 0.004*\"科技\" + 0.004*\"員工\" + 0.004*\"市場\" + 0.004*\"表示\"\n",
      "2025-04-19 00:09:07,933 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:07,933 : INFO : topic diff=0.266511, rho=0.286829\n",
      "2025-04-19 00:09:07,934 : INFO : PROGRESS: pass 3, at document #12000/16310\n",
      "2025-04-19 00:09:08,169 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:08,171 : INFO : topic #0 (0.200): 0.030*\"工作\" + 0.022*\"方式\" + 0.017*\"時間\" + 0.014*\"小時\" + 0.014*\"報名\" + 0.012*\"活動\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.011*\"電話\" + 0.011*\"台北市\"\n",
      "2025-04-19 00:09:08,172 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:09:08,172 : INFO : topic #2 (0.200): 0.018*\"公司\" + 0.017*\"工作\" + 0.010*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.006*\"經驗\" + 0.006*\"時間\" + 0.006*\"開發\" + 0.006*\"比較\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:08,173 : INFO : topic #3 (0.200): 0.008*\"台灣\" + 0.008*\"公司\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.005*\"產品\" + 0.005*\"產業\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"員工\"\n",
      "2025-04-19 00:09:08,173 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:08,173 : INFO : topic diff=0.284731, rho=0.286829\n",
      "2025-04-19 00:09:08,174 : INFO : PROGRESS: pass 3, at document #14000/16310\n",
      "2025-04-19 00:09:08,389 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:08,391 : INFO : topic #0 (0.200): 0.029*\"工作\" + 0.021*\"方式\" + 0.017*\"時間\" + 0.014*\"報名\" + 0.014*\"小時\" + 0.012*\"活動\" + 0.012*\"聯絡\" + 0.011*\"電話\" + 0.011*\"內容\" + 0.011*\"台北市\"\n",
      "2025-04-19 00:09:08,392 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"文字\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.010*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:08,392 : INFO : topic #2 (0.200): 0.017*\"工作\" + 0.017*\"公司\" + 0.010*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.006*\"經驗\" + 0.006*\"時間\" + 0.006*\"比較\" + 0.005*\"開發\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:08,393 : INFO : topic #3 (0.200): 0.009*\"台灣\" + 0.008*\"公司\" + 0.006*\"美國\" + 0.005*\"晶片\" + 0.005*\"表示\" + 0.005*\"科技\" + 0.005*\"技術\" + 0.005*\"產業\" + 0.005*\"員工\" + 0.004*\"半導體\"\n",
      "2025-04-19 00:09:08,393 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:08,394 : INFO : topic diff=0.281097, rho=0.286829\n",
      "2025-04-19 00:09:08,394 : INFO : PROGRESS: pass 3, at document #16000/16310\n",
      "2025-04-19 00:09:08,602 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:08,604 : INFO : topic #0 (0.200): 0.029*\"工作\" + 0.021*\"方式\" + 0.016*\"時間\" + 0.014*\"報名\" + 0.014*\"小時\" + 0.012*\"活動\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"台北市\" + 0.011*\"電話\"\n",
      "2025-04-19 00:09:08,605 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:08,605 : INFO : topic #2 (0.200): 0.018*\"公司\" + 0.017*\"工作\" + 0.009*\"面試\" + 0.008*\"工程師\" + 0.007*\"問題\" + 0.006*\"比較\" + 0.006*\"時間\" + 0.006*\"經驗\" + 0.005*\"知道\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:08,606 : INFO : topic #3 (0.200): 0.009*\"台灣\" + 0.008*\"公司\" + 0.008*\"美國\" + 0.006*\"晶片\" + 0.006*\"表示\" + 0.005*\"中國\" + 0.005*\"半導體\" + 0.005*\"技術\" + 0.005*\"台積電\" + 0.005*\"員工\"\n",
      "2025-04-19 00:09:08,606 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:08,607 : INFO : topic diff=0.256670, rho=0.286829\n",
      "2025-04-19 00:09:08,675 : INFO : -8.279 per-word bound, 310.7 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:09:08,675 : INFO : PROGRESS: pass 3, at document #16310/16310\n",
      "2025-04-19 00:09:08,733 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:08,736 : INFO : topic #0 (0.200): 0.031*\"工作\" + 0.019*\"方式\" + 0.017*\"時間\" + 0.015*\"小時\" + 0.014*\"報名\" + 0.013*\"活動\" + 0.011*\"台北市\" + 0.011*\"聯絡\" + 0.010*\"地點\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:08,736 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"資訊\" + 0.010*\"分類\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:09:08,737 : INFO : topic #2 (0.200): 0.018*\"公司\" + 0.016*\"工作\" + 0.009*\"面試\" + 0.007*\"問題\" + 0.007*\"工程師\" + 0.007*\"知道\" + 0.006*\"真的\" + 0.005*\"時間\" + 0.005*\"比較\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:08,737 : INFO : topic #3 (0.200): 0.010*\"美國\" + 0.009*\"台灣\" + 0.008*\"公司\" + 0.007*\"晶片\" + 0.006*\"表示\" + 0.006*\"技術\" + 0.005*\"台積電\" + 0.005*\"中國\" + 0.005*\"科技\" + 0.005*\"台積\"\n",
      "2025-04-19 00:09:08,737 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"方式\" + 0.013*\"空白\" + 0.012*\"砍除\" + 0.012*\"單位\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"國定假日\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:08,738 : INFO : topic diff=0.278675, rho=0.286829\n",
      "2025-04-19 00:09:08,738 : INFO : PROGRESS: pass 4, at document #2000/16310\n",
      "2025-04-19 00:09:09,291 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:09,294 : INFO : topic #0 (0.200): 0.029*\"工作\" + 0.020*\"方式\" + 0.017*\"時間\" + 0.013*\"小時\" + 0.013*\"報名\" + 0.012*\"電話\" + 0.012*\"活動\" + 0.011*\"台北市\" + 0.011*\"通知\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:09,294 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"文字\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.010*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"分類\"\n",
      "2025-04-19 00:09:09,295 : INFO : topic #2 (0.200): 0.017*\"公司\" + 0.016*\"工作\" + 0.009*\"面試\" + 0.007*\"問題\" + 0.007*\"工程師\" + 0.006*\"知道\" + 0.005*\"時間\" + 0.005*\"真的\" + 0.005*\"比較\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:09,295 : INFO : topic #3 (0.200): 0.009*\"美國\" + 0.009*\"台灣\" + 0.008*\"公司\" + 0.007*\"晶片\" + 0.006*\"表示\" + 0.005*\"技術\" + 0.005*\"台積電\" + 0.005*\"中國\" + 0.005*\"科技\" + 0.005*\"台積\"\n",
      "2025-04-19 00:09:09,296 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.017*\"推定\" + 0.013*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:09,296 : INFO : topic diff=0.742210, rho=0.275711\n",
      "2025-04-19 00:09:09,296 : INFO : PROGRESS: pass 4, at document #4000/16310\n",
      "2025-04-19 00:09:09,864 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:09,867 : INFO : topic #0 (0.200): 0.028*\"工作\" + 0.020*\"方式\" + 0.016*\"時間\" + 0.013*\"小時\" + 0.013*\"電話\" + 0.012*\"報名\" + 0.011*\"活動\" + 0.011*\"通知\" + 0.011*\"內容\" + 0.011*\"台北市\"\n",
      "2025-04-19 00:09:09,868 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"文字\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:09:09,868 : INFO : topic #2 (0.200): 0.017*\"公司\" + 0.016*\"工作\" + 0.009*\"面試\" + 0.007*\"問題\" + 0.007*\"工程師\" + 0.006*\"知道\" + 0.005*\"時間\" + 0.005*\"比較\" + 0.005*\"真的\" + 0.005*\"經驗\"\n",
      "2025-04-19 00:09:09,868 : INFO : topic #3 (0.200): 0.009*\"美國\" + 0.009*\"台灣\" + 0.008*\"公司\" + 0.007*\"晶片\" + 0.006*\"表示\" + 0.005*\"技術\" + 0.005*\"台積電\" + 0.005*\"中國\" + 0.005*\"科技\" + 0.005*\"台積\"\n",
      "2025-04-19 00:09:09,869 : INFO : topic #4 (0.200): 0.035*\"工作\" + 0.017*\"推定\" + 0.013*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:09,869 : INFO : topic diff=0.322819, rho=0.275711\n",
      "2025-04-19 00:09:09,870 : INFO : PROGRESS: pass 4, at document #6000/16310\n",
      "2025-04-19 00:09:10,337 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:10,348 : INFO : topic #0 (0.200): 0.027*\"工作\" + 0.020*\"方式\" + 0.016*\"時間\" + 0.014*\"報名\" + 0.013*\"電話\" + 0.012*\"活動\" + 0.012*\"小時\" + 0.011*\"通知\" + 0.011*\"內容\" + 0.011*\"台北市\"\n",
      "2025-04-19 00:09:10,351 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"分類\"\n",
      "2025-04-19 00:09:10,355 : INFO : topic #2 (0.200): 0.018*\"公司\" + 0.016*\"工作\" + 0.008*\"面試\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"經驗\" + 0.006*\"知道\" + 0.006*\"時間\" + 0.005*\"比較\" + 0.004*\"需要\"\n",
      "2025-04-19 00:09:10,360 : INFO : topic #3 (0.200): 0.009*\"美國\" + 0.009*\"台灣\" + 0.009*\"公司\" + 0.006*\"晶片\" + 0.005*\"表示\" + 0.005*\"技術\" + 0.005*\"台積電\" + 0.005*\"科技\" + 0.005*\"中國\" + 0.005*\"員工\"\n",
      "2025-04-19 00:09:10,361 : INFO : topic #4 (0.200): 0.035*\"工作\" + 0.017*\"推定\" + 0.014*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:10,361 : INFO : topic diff=0.203179, rho=0.275711\n",
      "2025-04-19 00:09:10,362 : INFO : PROGRESS: pass 4, at document #8000/16310\n",
      "2025-04-19 00:09:10,597 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:10,599 : INFO : topic #0 (0.200): 0.027*\"工作\" + 0.021*\"方式\" + 0.017*\"時間\" + 0.013*\"報名\" + 0.013*\"小時\" + 0.013*\"電話\" + 0.012*\"活動\" + 0.012*\"台北市\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:10,600 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:09:10,600 : INFO : topic #2 (0.200): 0.018*\"公司\" + 0.017*\"工作\" + 0.010*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.007*\"經驗\" + 0.006*\"時間\" + 0.006*\"開發\" + 0.005*\"比較\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:10,601 : INFO : topic #3 (0.200): 0.009*\"台灣\" + 0.009*\"公司\" + 0.009*\"美國\" + 0.006*\"技術\" + 0.005*\"晶片\" + 0.005*\"科技\" + 0.005*\"產品\" + 0.005*\"表示\" + 0.005*\"員工\" + 0.005*\"產業\"\n",
      "2025-04-19 00:09:10,601 : INFO : topic #4 (0.200): 0.035*\"工作\" + 0.017*\"推定\" + 0.014*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:10,601 : INFO : topic diff=0.307448, rho=0.275711\n",
      "2025-04-19 00:09:10,602 : INFO : PROGRESS: pass 4, at document #10000/16310\n",
      "2025-04-19 00:09:10,814 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:10,816 : INFO : topic #0 (0.200): 0.027*\"工作\" + 0.021*\"方式\" + 0.017*\"時間\" + 0.013*\"報名\" + 0.013*\"小時\" + 0.012*\"活動\" + 0.012*\"電話\" + 0.012*\"聯絡\" + 0.012*\"台北市\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:10,817 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"分類\"\n",
      "2025-04-19 00:09:10,817 : INFO : topic #2 (0.200): 0.018*\"公司\" + 0.017*\"工作\" + 0.010*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.007*\"經驗\" + 0.006*\"開發\" + 0.006*\"時間\" + 0.006*\"比較\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:10,818 : INFO : topic #3 (0.200): 0.010*\"台灣\" + 0.009*\"公司\" + 0.008*\"美國\" + 0.006*\"技術\" + 0.005*\"產品\" + 0.005*\"科技\" + 0.005*\"產業\" + 0.004*\"員工\" + 0.004*\"市場\" + 0.004*\"表示\"\n",
      "2025-04-19 00:09:10,818 : INFO : topic #4 (0.200): 0.035*\"工作\" + 0.017*\"推定\" + 0.014*\"方式\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:10,818 : INFO : topic diff=0.249498, rho=0.275711\n",
      "2025-04-19 00:09:10,819 : INFO : PROGRESS: pass 4, at document #12000/16310\n",
      "2025-04-19 00:09:11,029 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:11,032 : INFO : topic #0 (0.200): 0.027*\"工作\" + 0.021*\"方式\" + 0.017*\"時間\" + 0.014*\"報名\" + 0.013*\"小時\" + 0.013*\"活動\" + 0.012*\"聯絡\" + 0.011*\"電話\" + 0.011*\"台北市\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:11,032 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"分類\"\n",
      "2025-04-19 00:09:11,033 : INFO : topic #2 (0.200): 0.017*\"公司\" + 0.016*\"工作\" + 0.010*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.006*\"經驗\" + 0.006*\"開發\" + 0.006*\"時間\" + 0.006*\"比較\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:11,033 : INFO : topic #3 (0.200): 0.009*\"台灣\" + 0.008*\"公司\" + 0.007*\"美國\" + 0.005*\"晶片\" + 0.005*\"技術\" + 0.005*\"科技\" + 0.005*\"表示\" + 0.005*\"產業\" + 0.004*\"員工\" + 0.004*\"半導體\"\n",
      "2025-04-19 00:09:11,034 : INFO : topic #4 (0.200): 0.035*\"工作\" + 0.017*\"推定\" + 0.014*\"方式\" + 0.013*\"空白\" + 0.012*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:11,034 : INFO : topic diff=0.269872, rho=0.275711\n",
      "2025-04-19 00:09:11,034 : INFO : PROGRESS: pass 4, at document #14000/16310\n",
      "2025-04-19 00:09:11,278 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:11,280 : INFO : topic #0 (0.200): 0.027*\"工作\" + 0.021*\"方式\" + 0.017*\"時間\" + 0.014*\"報名\" + 0.013*\"活動\" + 0.013*\"小時\" + 0.011*\"聯絡\" + 0.011*\"電話\" + 0.011*\"台北市\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:11,281 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"分類\"\n",
      "2025-04-19 00:09:11,281 : INFO : topic #2 (0.200): 0.017*\"公司\" + 0.017*\"工作\" + 0.009*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.006*\"經驗\" + 0.006*\"比較\" + 0.006*\"時間\" + 0.005*\"開發\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:11,282 : INFO : topic #3 (0.200): 0.009*\"台灣\" + 0.008*\"公司\" + 0.006*\"美國\" + 0.006*\"晶片\" + 0.005*\"表示\" + 0.005*\"科技\" + 0.005*\"員工\" + 0.005*\"技術\" + 0.005*\"產業\" + 0.005*\"半導體\"\n",
      "2025-04-19 00:09:11,282 : INFO : topic #4 (0.200): 0.035*\"工作\" + 0.017*\"推定\" + 0.013*\"方式\" + 0.013*\"空白\" + 0.012*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:11,283 : INFO : topic diff=0.267742, rho=0.275711\n",
      "2025-04-19 00:09:11,283 : INFO : PROGRESS: pass 4, at document #16000/16310\n",
      "2025-04-19 00:09:11,491 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:11,493 : INFO : topic #0 (0.200): 0.026*\"工作\" + 0.020*\"方式\" + 0.017*\"時間\" + 0.015*\"報名\" + 0.013*\"小時\" + 0.013*\"活動\" + 0.011*\"聯絡\" + 0.011*\"台北市\" + 0.011*\"電話\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:11,493 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"文字\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.010*\"空白\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"分類\"\n",
      "2025-04-19 00:09:11,494 : INFO : topic #2 (0.200): 0.017*\"公司\" + 0.017*\"工作\" + 0.009*\"面試\" + 0.008*\"工程師\" + 0.007*\"問題\" + 0.006*\"比較\" + 0.006*\"時間\" + 0.005*\"經驗\" + 0.005*\"知道\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:11,494 : INFO : topic #3 (0.200): 0.009*\"台灣\" + 0.008*\"公司\" + 0.008*\"美國\" + 0.007*\"晶片\" + 0.006*\"表示\" + 0.005*\"中國\" + 0.005*\"半導體\" + 0.005*\"台積電\" + 0.005*\"員工\" + 0.005*\"技術\"\n",
      "2025-04-19 00:09:11,495 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.017*\"推定\" + 0.013*\"方式\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:11,495 : INFO : topic diff=0.243291, rho=0.275711\n",
      "2025-04-19 00:09:11,564 : INFO : -8.268 per-word bound, 308.3 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:09:11,564 : INFO : PROGRESS: pass 4, at document #16310/16310\n",
      "2025-04-19 00:09:11,598 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:11,600 : INFO : topic #0 (0.200): 0.028*\"工作\" + 0.019*\"方式\" + 0.017*\"時間\" + 0.014*\"報名\" + 0.014*\"小時\" + 0.013*\"活動\" + 0.011*\"台北市\" + 0.010*\"聯絡\" + 0.010*\"地點\" + 0.010*\"電話\"\n",
      "2025-04-19 00:09:11,601 : INFO : topic #1 (0.200): 0.030*\"工作\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.010*\"空白\" + 0.010*\"分類\" + 0.010*\"資訊\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:09:11,601 : INFO : topic #2 (0.200): 0.017*\"公司\" + 0.016*\"工作\" + 0.008*\"面試\" + 0.007*\"問題\" + 0.007*\"工程師\" + 0.006*\"知道\" + 0.005*\"真的\" + 0.005*\"時間\" + 0.005*\"比較\" + 0.005*\"覺得\"\n",
      "2025-04-19 00:09:11,602 : INFO : topic #3 (0.200): 0.010*\"美國\" + 0.009*\"台灣\" + 0.008*\"公司\" + 0.007*\"晶片\" + 0.006*\"表示\" + 0.006*\"台積電\" + 0.006*\"技術\" + 0.005*\"中國\" + 0.005*\"科技\" + 0.005*\"台積\"\n",
      "2025-04-19 00:09:11,602 : INFO : topic #4 (0.200): 0.034*\"工作\" + 0.016*\"推定\" + 0.013*\"方式\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"單位\" + 0.012*\"內容\" + 0.012*\"情形\" + 0.011*\"國定假日\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:11,603 : INFO : topic diff=0.262556, rho=0.275711\n",
      "2025-04-19 00:09:11,603 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel<num_terms=23261, num_topics=5, decay=0.5, chunksize=2000> in 15.10s', 'datetime': '2025-04-19T00:09:11.603411', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n",
      "2025-04-19 00:09:16,814 : INFO : -6.986 per-word bound, 126.7 perplexity estimate based on a held-out corpus of 16310 documents with 3460358 words\n",
      "2025-04-19 00:09:16,816 : INFO : using ParallelWordOccurrenceAccumulator<processes=7, batch_size=64> to estimate probabilities from sliding windows\n",
      "2025-04-19 00:09:20,615 : INFO : 1 batches submitted to accumulate stats from 64 documents (22660 virtual)\n",
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      "2025-04-19 00:09:24,227 : INFO : 250 batches submitted to accumulate stats from 16000 documents (3406129 virtual)\n",
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      "2025-04-19 00:09:24,265 : INFO : 253 batches submitted to accumulate stats from 16192 documents (3433824 virtual)\n",
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      "2025-04-19 00:09:24,288 : INFO : 255 batches submitted to accumulate stats from 16320 documents (3450914 virtual)\n",
      "2025-04-19 00:09:24,448 : INFO : 7 accumulators retrieved from output queue\n",
      "2025-04-19 00:09:24,458 : INFO : accumulated word occurrence stats for 3451622 virtual documents\n",
      "2025-04-19 00:09:24,535 : INFO : using symmetric alpha at 0.16666666666666666\n",
      "2025-04-19 00:09:24,536 : INFO : using symmetric eta at 0.16666666666666666\n",
      "2025-04-19 00:09:24,539 : INFO : using serial LDA version on this node\n",
      "2025-04-19 00:09:24,545 : INFO : running online (multi-pass) LDA training, 6 topics, 5 passes over the supplied corpus of 16310 documents, updating model once every 2000 documents, evaluating perplexity every 16310 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2025-04-19 00:09:24,546 : INFO : PROGRESS: pass 0, at document #2000/16310\n",
      "2025-04-19 00:09:25,211 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:25,214 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.014*\"方式\" + 0.013*\"應徵\" + 0.012*\"推定\" + 0.012*\"單位\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:09:25,215 : INFO : topic #1 (0.167): 0.030*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"砍除\" + 0.010*\"第一項\" + 0.010*\"國定假日\" + 0.010*\"內容\" + 0.010*\"情形\"\n",
      "2025-04-19 00:09:25,215 : INFO : topic #4 (0.167): 0.038*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"方式\" + 0.011*\"聯絡\" + 0.010*\"第一項\" + 0.010*\"單位\" + 0.010*\"聯絡人\" + 0.009*\"情形\" + 0.009*\"內容\"\n",
      "2025-04-19 00:09:25,216 : INFO : topic #2 (0.167): 0.040*\"工作\" + 0.013*\"內容\" + 0.012*\"推定\" + 0.012*\"工資\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"聯絡\" + 0.010*\"小時\"\n",
      "2025-04-19 00:09:25,216 : INFO : topic #5 (0.167): 0.017*\"工作\" + 0.012*\"方式\" + 0.011*\"空白\" + 0.010*\"聯絡\" + 0.009*\"應徵\" + 0.009*\"內容\" + 0.008*\"分類\" + 0.008*\"聯絡人\" + 0.008*\"小時\" + 0.008*\"資訊\"\n",
      "2025-04-19 00:09:25,217 : INFO : topic diff=6.811724, rho=1.000000\n",
      "2025-04-19 00:09:25,218 : INFO : PROGRESS: pass 0, at document #4000/16310\n",
      "2025-04-19 00:09:25,898 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:25,901 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.014*\"應徵\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.012*\"空白\" + 0.011*\"推定\" + 0.011*\"單位\" + 0.011*\"資訊\" + 0.010*\"第一項\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:25,901 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.015*\"方式\" + 0.012*\"砍除\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"單位\" + 0.011*\"連結\"\n",
      "2025-04-19 00:09:25,902 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.010*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"聯絡人\"\n",
      "2025-04-19 00:09:25,902 : INFO : topic #3 (0.167): 0.015*\"工作\" + 0.012*\"方式\" + 0.009*\"聯絡人\" + 0.009*\"時間\" + 0.009*\"砍除\" + 0.008*\"資訊\" + 0.008*\"電話\" + 0.008*\"應徵\" + 0.008*\"聯絡\" + 0.008*\"內容\"\n",
      "2025-04-19 00:09:25,903 : INFO : topic #5 (0.167): 0.015*\"報名\" + 0.014*\"活動\" + 0.013*\"電話\" + 0.012*\"方式\" + 0.011*\"台北市\" + 0.011*\"工作\" + 0.010*\"時間\" + 0.010*\"聯絡\" + 0.010*\"通知\" + 0.009*\"內容\"\n",
      "2025-04-19 00:09:25,903 : INFO : topic diff=0.670462, rho=0.707107\n",
      "2025-04-19 00:09:25,904 : INFO : PROGRESS: pass 0, at document #6000/16310\n",
      "2025-04-19 00:09:26,446 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:26,450 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.013*\"應徵\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:26,450 : INFO : topic #3 (0.167): 0.011*\"工作\" + 0.011*\"方式\" + 0.011*\"公司\" + 0.009*\"時間\" + 0.009*\"電話\" + 0.009*\"聯絡人\" + 0.009*\"報名\" + 0.008*\"實驗\" + 0.008*\"聯絡\" + 0.008*\"資訊\"\n",
      "2025-04-19 00:09:26,451 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:26,451 : INFO : topic #2 (0.167): 0.043*\"工作\" + 0.016*\"方式\" + 0.013*\"推定\" + 0.012*\"小時\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.011*\"單位\" + 0.010*\"未註明\" + 0.010*\"依法\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:09:26,452 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"連結\"\n",
      "2025-04-19 00:09:26,452 : INFO : topic diff=0.661024, rho=0.577350\n",
      "2025-04-19 00:09:26,453 : INFO : PROGRESS: pass 0, at document #8000/16310\n",
      "2025-04-19 00:09:26,794 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:26,797 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.013*\"應徵\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:26,798 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:26,799 : INFO : topic #2 (0.167): 0.043*\"工作\" + 0.012*\"方式\" + 0.010*\"公司\" + 0.010*\"小時\" + 0.009*\"內容\" + 0.009*\"面試\" + 0.008*\"時間\" + 0.008*\"推定\" + 0.008*\"工資\" + 0.007*\"單位\"\n",
      "2025-04-19 00:09:26,799 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"連結\"\n",
      "2025-04-19 00:09:26,800 : INFO : topic #3 (0.167): 0.016*\"公司\" + 0.011*\"工作\" + 0.009*\"方式\" + 0.009*\"時間\" + 0.007*\"電話\" + 0.007*\"資訊\" + 0.007*\"實驗\" + 0.007*\"報名\" + 0.007*\"聯絡人\" + 0.007*\"內容\"\n",
      "2025-04-19 00:09:26,800 : INFO : topic diff=0.916051, rho=0.500000\n",
      "2025-04-19 00:09:26,801 : INFO : PROGRESS: pass 0, at document #10000/16310\n",
      "2025-04-19 00:09:27,090 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:27,093 : INFO : topic #3 (0.167): 0.016*\"公司\" + 0.010*\"工作\" + 0.009*\"方式\" + 0.008*\"時間\" + 0.007*\"資訊\" + 0.007*\"研發\" + 0.007*\"連結\" + 0.007*\"電話\" + 0.007*\"報名\" + 0.007*\"實驗\"\n",
      "2025-04-19 00:09:27,093 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.009*\"工作\" + 0.008*\"面試\" + 0.008*\"問題\" + 0.007*\"時間\" + 0.007*\"工程師\" + 0.006*\"開發\" + 0.006*\"經驗\" + 0.006*\"目前\" + 0.005*\"技術\"\n",
      "2025-04-19 00:09:27,094 : INFO : topic #2 (0.167): 0.044*\"工作\" + 0.011*\"公司\" + 0.011*\"方式\" + 0.010*\"覺得\" + 0.010*\"面試\" + 0.009*\"小時\" + 0.008*\"內容\" + 0.008*\"時間\" + 0.007*\"單位\" + 0.006*\"推定\"\n",
      "2025-04-19 00:09:27,094 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"連結\"\n",
      "2025-04-19 00:09:27,095 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:27,095 : INFO : topic diff=0.593984, rho=0.447214\n",
      "2025-04-19 00:09:27,096 : INFO : PROGRESS: pass 0, at document #12000/16310\n",
      "2025-04-19 00:09:27,357 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:27,360 : INFO : topic #5 (0.167): 0.014*\"公司\" + 0.007*\"工作\" + 0.006*\"問題\" + 0.006*\"面試\" + 0.006*\"工程師\" + 0.005*\"時間\" + 0.005*\"開發\" + 0.005*\"目前\" + 0.005*\"技術\" + 0.005*\"台灣\"\n",
      "2025-04-19 00:09:27,361 : INFO : topic #3 (0.167): 0.042*\"半導體\" + 0.021*\"製程\" + 0.013*\"公司\" + 0.013*\"表示\" + 0.012*\"研發\" + 0.009*\"職場\" + 0.008*\"工作\" + 0.007*\"方式\" + 0.007*\"中國\" + 0.006*\"時間\"\n",
      "2025-04-19 00:09:27,361 : INFO : topic #2 (0.167): 0.042*\"工作\" + 0.011*\"覺得\" + 0.011*\"公司\" + 0.009*\"方式\" + 0.009*\"面試\" + 0.008*\"小時\" + 0.008*\"時間\" + 0.008*\"內容\" + 0.006*\"單位\" + 0.006*\"程式\"\n",
      "2025-04-19 00:09:27,362 : INFO : topic #0 (0.167): 0.028*\"工作\" + 0.013*\"應徵\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"空白\" + 0.010*\"第一項\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:27,362 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:27,363 : INFO : topic diff=0.590728, rho=0.408248\n",
      "2025-04-19 00:09:27,363 : INFO : PROGRESS: pass 0, at document #14000/16310\n",
      "2025-04-19 00:09:27,621 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:27,624 : INFO : topic #1 (0.167): 0.028*\"工作\" + 0.014*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"連結\"\n",
      "2025-04-19 00:09:27,625 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.007*\"台灣\" + 0.006*\"工作\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.004*\"目前\" + 0.004*\"面試\" + 0.004*\"時間\" + 0.004*\"員工\"\n",
      "2025-04-19 00:09:27,625 : INFO : topic #2 (0.167): 0.041*\"工作\" + 0.010*\"覺得\" + 0.010*\"公司\" + 0.008*\"面試\" + 0.008*\"方式\" + 0.007*\"小時\" + 0.007*\"內容\" + 0.007*\"時間\" + 0.006*\"單位\" + 0.006*\"應該\"\n",
      "2025-04-19 00:09:27,626 : INFO : topic #0 (0.167): 0.027*\"工作\" + 0.012*\"應徵\" + 0.012*\"砍除\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"第一項\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:27,626 : INFO : topic #3 (0.167): 0.049*\"半導體\" + 0.023*\"表示\" + 0.022*\"製程\" + 0.017*\"中國\" + 0.015*\"研發\" + 0.010*\"公司\" + 0.007*\"輝達\" + 0.007*\"仁勳\" + 0.006*\"職場\" + 0.005*\"奈米\"\n",
      "2025-04-19 00:09:27,627 : INFO : topic diff=0.590928, rho=0.377964\n",
      "2025-04-19 00:09:27,628 : INFO : PROGRESS: pass 0, at document #16000/16310\n",
      "2025-04-19 00:09:27,891 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:27,894 : INFO : topic #4 (0.167): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.010*\"砍除\" + 0.010*\"方式\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:27,895 : INFO : topic #3 (0.167): 0.041*\"半導體\" + 0.024*\"表示\" + 0.020*\"中國\" + 0.020*\"製程\" + 0.012*\"研發\" + 0.009*\"輝達\" + 0.008*\"公司\" + 0.007*\"仁勳\" + 0.007*\"川普\" + 0.006*\"奈米\"\n",
      "2025-04-19 00:09:27,895 : INFO : topic #0 (0.167): 0.026*\"工作\" + 0.012*\"應徵\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"第一項\" + 0.010*\"資訊\" + 0.010*\"單位\" + 0.009*\"推定\" + 0.009*\"內容\"\n",
      "2025-04-19 00:09:27,896 : INFO : topic #1 (0.167): 0.028*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.010*\"連結\"\n",
      "2025-04-19 00:09:27,897 : INFO : topic #2 (0.167): 0.039*\"工作\" + 0.010*\"公司\" + 0.010*\"覺得\" + 0.008*\"面試\" + 0.007*\"方式\" + 0.007*\"內容\" + 0.007*\"時間\" + 0.007*\"小時\" + 0.006*\"真的\" + 0.006*\"應該\"\n",
      "2025-04-19 00:09:27,897 : INFO : topic diff=0.460963, rho=0.353553\n",
      "2025-04-19 00:09:27,979 : INFO : -8.586 per-word bound, 384.3 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:09:27,979 : INFO : PROGRESS: pass 0, at document #16310/16310\n",
      "2025-04-19 00:09:28,020 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:28,023 : INFO : topic #3 (0.167): 0.031*\"半導體\" + 0.023*\"表示\" + 0.019*\"中國\" + 0.017*\"製程\" + 0.014*\"川普\" + 0.013*\"研發\" + 0.011*\"投資\" + 0.009*\"輝達\" + 0.007*\"公司\" + 0.007*\"魏哲家\"\n",
      "2025-04-19 00:09:28,023 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.007*\"美國\" + 0.007*\"台灣\" + 0.006*\"技術\" + 0.005*\"晶片\" + 0.005*\"員工\" + 0.005*\"工作\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"工程師\"\n",
      "2025-04-19 00:09:28,024 : INFO : topic #4 (0.167): 0.035*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.011*\"第一項\" + 0.010*\"聯絡\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"方式\" + 0.010*\"國定假日\" + 0.009*\"單位\"\n",
      "2025-04-19 00:09:28,024 : INFO : topic #1 (0.167): 0.027*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.010*\"連結\" + 0.010*\"文字\"\n",
      "2025-04-19 00:09:28,025 : INFO : topic #2 (0.167): 0.037*\"工作\" + 0.011*\"覺得\" + 0.011*\"公司\" + 0.008*\"真的\" + 0.007*\"面試\" + 0.007*\"小時\" + 0.007*\"時間\" + 0.007*\"應該\" + 0.006*\"方式\" + 0.006*\"預期\"\n",
      "2025-04-19 00:09:28,025 : INFO : topic diff=0.402306, rho=0.333333\n",
      "2025-04-19 00:09:28,025 : INFO : PROGRESS: pass 1, at document #2000/16310\n",
      "2025-04-19 00:09:28,672 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:28,675 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.016*\"方式\" + 0.012*\"聯絡\" + 0.012*\"推定\" + 0.012*\"砍除\" + 0.012*\"內容\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.010*\"資訊\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:09:28,676 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:28,677 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.013*\"方式\" + 0.012*\"應徵\" + 0.010*\"內容\" + 0.010*\"推定\" + 0.009*\"單位\" + 0.009*\"工資\" + 0.009*\"聯絡\" + 0.009*\"資訊\" + 0.008*\"砍除\"\n",
      "2025-04-19 00:09:28,677 : INFO : topic #2 (0.167): 0.041*\"工作\" + 0.010*\"時間\" + 0.009*\"方式\" + 0.009*\"小時\" + 0.009*\"面試\" + 0.008*\"公司\" + 0.007*\"覺得\" + 0.007*\"內容\" + 0.005*\"單位\" + 0.005*\"真的\"\n",
      "2025-04-19 00:09:28,678 : INFO : topic #3 (0.167): 0.027*\"半導體\" + 0.021*\"表示\" + 0.017*\"中國\" + 0.015*\"製程\" + 0.012*\"川普\" + 0.011*\"研發\" + 0.010*\"投資\" + 0.008*\"輝達\" + 0.008*\"公司\" + 0.006*\"魏哲家\"\n",
      "2025-04-19 00:09:28,678 : INFO : topic diff=1.076891, rho=0.313805\n",
      "2025-04-19 00:09:28,678 : INFO : PROGRESS: pass 1, at document #4000/16310\n",
      "2025-04-19 00:09:29,317 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:29,320 : INFO : topic #3 (0.167): 0.023*\"半導體\" + 0.019*\"表示\" + 0.016*\"中國\" + 0.013*\"製程\" + 0.011*\"川普\" + 0.010*\"研發\" + 0.009*\"實驗\" + 0.009*\"投資\" + 0.008*\"參與\" + 0.008*\"公司\"\n",
      "2025-04-19 00:09:29,320 : INFO : topic #4 (0.167): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:29,321 : INFO : topic #0 (0.167): 0.030*\"工作\" + 0.015*\"方式\" + 0.012*\"應徵\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"工資\" + 0.009*\"單位\" + 0.009*\"依法\" + 0.009*\"聯絡\" + 0.009*\"發薪日\"\n",
      "2025-04-19 00:09:29,321 : INFO : topic #2 (0.167): 0.044*\"工作\" + 0.013*\"時間\" + 0.012*\"方式\" + 0.011*\"小時\" + 0.010*\"面試\" + 0.008*\"內容\" + 0.007*\"每日\" + 0.007*\"公司\" + 0.006*\"工時\" + 0.006*\"單位\"\n",
      "2025-04-19 00:09:29,322 : INFO : topic #5 (0.167): 0.011*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"工作\" + 0.004*\"員工\" + 0.004*\"資料\" + 0.004*\"科技\" + 0.004*\"問題\"\n",
      "2025-04-19 00:09:29,322 : INFO : topic diff=0.422761, rho=0.313805\n",
      "2025-04-19 00:09:29,322 : INFO : PROGRESS: pass 1, at document #6000/16310\n",
      "2025-04-19 00:09:29,890 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:29,892 : INFO : topic #3 (0.167): 0.018*\"半導體\" + 0.017*\"表示\" + 0.013*\"實驗\" + 0.012*\"中國\" + 0.010*\"參與\" + 0.009*\"製程\" + 0.009*\"報名\" + 0.009*\"舉辦\" + 0.009*\"公司\" + 0.008*\"川普\"\n",
      "2025-04-19 00:09:29,893 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.016*\"方式\" + 0.013*\"聯絡\" + 0.012*\"內容\" + 0.011*\"砍除\" + 0.011*\"推定\" + 0.011*\"情形\" + 0.011*\"文字\" + 0.011*\"單位\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:29,894 : INFO : topic #0 (0.167): 0.031*\"工作\" + 0.018*\"方式\" + 0.011*\"應徵\" + 0.011*\"推定\" + 0.011*\"內容\" + 0.011*\"依法\" + 0.011*\"工資\" + 0.010*\"聯絡\" + 0.009*\"單位\" + 0.009*\"發薪日\"\n",
      "2025-04-19 00:09:29,894 : INFO : topic #4 (0.167): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:29,895 : INFO : topic #2 (0.167): 0.047*\"工作\" + 0.015*\"時間\" + 0.014*\"方式\" + 0.013*\"小時\" + 0.010*\"面試\" + 0.008*\"內容\" + 0.008*\"每日\" + 0.007*\"休息\" + 0.006*\"工時\" + 0.006*\"公司\"\n",
      "2025-04-19 00:09:29,895 : INFO : topic diff=0.289256, rho=0.313805\n",
      "2025-04-19 00:09:29,895 : INFO : PROGRESS: pass 1, at document #8000/16310\n",
      "2025-04-19 00:09:30,192 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:30,195 : INFO : topic #3 (0.167): 0.019*\"半導體\" + 0.016*\"表示\" + 0.013*\"實驗\" + 0.012*\"中國\" + 0.010*\"參與\" + 0.009*\"製程\" + 0.009*\"公司\" + 0.009*\"報名\" + 0.009*\"舉辦\" + 0.008*\"研發\"\n",
      "2025-04-19 00:09:30,195 : INFO : topic #0 (0.167): 0.031*\"工作\" + 0.018*\"方式\" + 0.012*\"應徵\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.010*\"依法\" + 0.010*\"工資\" + 0.010*\"聯絡\" + 0.009*\"單位\" + 0.009*\"發薪日\"\n",
      "2025-04-19 00:09:30,196 : INFO : topic #4 (0.167): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:30,197 : INFO : topic #2 (0.167): 0.053*\"工作\" + 0.016*\"時間\" + 0.016*\"面試\" + 0.013*\"方式\" + 0.012*\"小時\" + 0.008*\"內容\" + 0.008*\"公司\" + 0.008*\"經驗\" + 0.007*\"覺得\" + 0.007*\"每日\"\n",
      "2025-04-19 00:09:30,197 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.006*\"工程師\" + 0.006*\"問題\" + 0.005*\"技術\" + 0.005*\"開發\" + 0.005*\"工作\" + 0.005*\"目前\" + 0.004*\"台灣\" + 0.004*\"產品\" + 0.004*\"資料\"\n",
      "2025-04-19 00:09:30,197 : INFO : topic diff=0.359110, rho=0.313805\n",
      "2025-04-19 00:09:30,198 : INFO : PROGRESS: pass 1, at document #10000/16310\n",
      "2025-04-19 00:09:30,467 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:30,469 : INFO : topic #3 (0.167): 0.019*\"半導體\" + 0.015*\"表示\" + 0.012*\"實驗\" + 0.012*\"中國\" + 0.010*\"參與\" + 0.009*\"公司\" + 0.009*\"製程\" + 0.009*\"報名\" + 0.008*\"舉辦\" + 0.008*\"研發\"\n",
      "2025-04-19 00:09:30,470 : INFO : topic #2 (0.167): 0.054*\"工作\" + 0.018*\"面試\" + 0.016*\"時間\" + 0.013*\"方式\" + 0.011*\"小時\" + 0.009*\"經驗\" + 0.009*\"公司\" + 0.009*\"內容\" + 0.008*\"覺得\" + 0.007*\"工時\"\n",
      "2025-04-19 00:09:30,471 : INFO : topic #4 (0.167): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:30,471 : INFO : topic #1 (0.167): 0.028*\"工作\" + 0.016*\"方式\" + 0.013*\"聯絡\" + 0.012*\"內容\" + 0.011*\"砍除\" + 0.011*\"推定\" + 0.011*\"情形\" + 0.011*\"文字\" + 0.011*\"單位\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:30,472 : INFO : topic #0 (0.167): 0.031*\"工作\" + 0.018*\"方式\" + 0.012*\"應徵\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.010*\"依法\" + 0.010*\"工資\" + 0.010*\"聯絡\" + 0.009*\"單位\" + 0.009*\"發薪日\"\n",
      "2025-04-19 00:09:30,472 : INFO : topic diff=0.294015, rho=0.313805\n",
      "2025-04-19 00:09:30,472 : INFO : PROGRESS: pass 1, at document #12000/16310\n",
      "2025-04-19 00:09:30,722 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:30,725 : INFO : topic #4 (0.167): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:30,726 : INFO : topic #5 (0.167): 0.014*\"公司\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.005*\"開發\" + 0.005*\"目前\" + 0.005*\"台灣\" + 0.004*\"工作\" + 0.004*\"面試\" + 0.004*\"使用\"\n",
      "2025-04-19 00:09:30,726 : INFO : topic #2 (0.167): 0.054*\"工作\" + 0.018*\"面試\" + 0.016*\"時間\" + 0.012*\"方式\" + 0.011*\"小時\" + 0.009*\"公司\" + 0.009*\"經驗\" + 0.008*\"內容\" + 0.008*\"覺得\" + 0.007*\"工時\"\n",
      "2025-04-19 00:09:30,727 : INFO : topic #1 (0.167): 0.028*\"工作\" + 0.016*\"方式\" + 0.013*\"聯絡\" + 0.012*\"內容\" + 0.011*\"砍除\" + 0.011*\"推定\" + 0.011*\"情形\" + 0.011*\"文字\" + 0.011*\"單位\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:30,727 : INFO : topic #3 (0.167): 0.025*\"半導體\" + 0.018*\"表示\" + 0.013*\"中國\" + 0.013*\"製程\" + 0.010*\"晶片\" + 0.008*\"台積電\" + 0.007*\"投資\" + 0.007*\"研發\" + 0.006*\"輝達\" + 0.006*\"公司\"\n",
      "2025-04-19 00:09:30,728 : INFO : topic diff=0.374750, rho=0.313805\n",
      "2025-04-19 00:09:30,728 : INFO : PROGRESS: pass 1, at document #14000/16310\n",
      "2025-04-19 00:09:30,985 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:30,988 : INFO : topic #0 (0.167): 0.030*\"工作\" + 0.017*\"方式\" + 0.011*\"應徵\" + 0.010*\"內容\" + 0.010*\"推定\" + 0.010*\"工資\" + 0.010*\"依法\" + 0.010*\"聯絡\" + 0.009*\"單位\" + 0.009*\"發薪日\"\n",
      "2025-04-19 00:09:30,989 : INFO : topic #4 (0.167): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:30,989 : INFO : topic #3 (0.167): 0.021*\"半導體\" + 0.019*\"表示\" + 0.016*\"中國\" + 0.015*\"晶片\" + 0.012*\"台積電\" + 0.010*\"製程\" + 0.009*\"輝達\" + 0.008*\"台灣\" + 0.008*\"台積\" + 0.007*\"投資\"\n",
      "2025-04-19 00:09:30,990 : INFO : topic #2 (0.167): 0.054*\"工作\" + 0.017*\"面試\" + 0.015*\"時間\" + 0.011*\"方式\" + 0.010*\"小時\" + 0.009*\"公司\" + 0.008*\"經驗\" + 0.008*\"內容\" + 0.007*\"覺得\" + 0.007*\"工時\"\n",
      "2025-04-19 00:09:30,990 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.006*\"台灣\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.004*\"工作\" + 0.004*\"目前\" + 0.004*\"員工\" + 0.004*\"開發\" + 0.003*\"科技\"\n",
      "2025-04-19 00:09:30,991 : INFO : topic diff=0.361131, rho=0.313805\n",
      "2025-04-19 00:09:30,991 : INFO : PROGRESS: pass 1, at document #16000/16310\n",
      "2025-04-19 00:09:31,212 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:31,215 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.017*\"方式\" + 0.011*\"應徵\" + 0.010*\"工資\" + 0.010*\"內容\" + 0.010*\"依法\" + 0.010*\"推定\" + 0.009*\"聯絡\" + 0.009*\"單位\" + 0.008*\"發薪日\"\n",
      "2025-04-19 00:09:31,216 : INFO : topic #4 (0.167): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:31,216 : INFO : topic #2 (0.167): 0.052*\"工作\" + 0.017*\"面試\" + 0.014*\"時間\" + 0.010*\"公司\" + 0.009*\"小時\" + 0.009*\"方式\" + 0.008*\"內容\" + 0.008*\"經驗\" + 0.007*\"工時\" + 0.007*\"覺得\"\n",
      "2025-04-19 00:09:31,217 : INFO : topic #1 (0.167): 0.028*\"工作\" + 0.016*\"方式\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.011*\"砍除\" + 0.011*\"情形\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.010*\"單位\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:31,217 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.006*\"台灣\" + 0.005*\"技術\" + 0.005*\"工程師\" + 0.005*\"問題\" + 0.005*\"員工\" + 0.004*\"目前\" + 0.004*\"科技\" + 0.004*\"工作\" + 0.004*\"美國\"\n",
      "2025-04-19 00:09:31,218 : INFO : topic diff=0.302248, rho=0.313805\n",
      "2025-04-19 00:09:31,295 : INFO : -8.394 per-word bound, 336.4 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:09:31,296 : INFO : PROGRESS: pass 1, at document #16310/16310\n",
      "2025-04-19 00:09:31,333 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:31,336 : INFO : topic #2 (0.167): 0.049*\"工作\" + 0.015*\"面試\" + 0.014*\"時間\" + 0.010*\"公司\" + 0.010*\"小時\" + 0.008*\"方式\" + 0.008*\"覺得\" + 0.007*\"工時\" + 0.007*\"內容\" + 0.007*\"經驗\"\n",
      "2025-04-19 00:09:31,337 : INFO : topic #4 (0.167): 0.035*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:31,337 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.006*\"技術\" + 0.005*\"台灣\" + 0.005*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"美國\" + 0.004*\"報導\" + 0.004*\"目前\"\n",
      "2025-04-19 00:09:31,338 : INFO : topic #0 (0.167): 0.028*\"工作\" + 0.016*\"方式\" + 0.010*\"應徵\" + 0.010*\"工資\" + 0.010*\"內容\" + 0.010*\"依法\" + 0.009*\"推定\" + 0.009*\"單位\" + 0.009*\"聯絡\" + 0.008*\"國定假日\"\n",
      "2025-04-19 00:09:31,338 : INFO : topic #1 (0.167): 0.028*\"工作\" + 0.016*\"方式\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.011*\"砍除\" + 0.011*\"情形\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.010*\"單位\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:31,338 : INFO : topic diff=0.311995, rho=0.313805\n",
      "2025-04-19 00:09:31,339 : INFO : PROGRESS: pass 2, at document #2000/16310\n",
      "2025-04-19 00:09:31,966 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:31,969 : INFO : topic #3 (0.167): 0.018*\"美國\" + 0.018*\"晶片\" + 0.016*\"表示\" + 0.015*\"台積電\" + 0.015*\"半導體\" + 0.015*\"中國\" + 0.012*\"投資\" + 0.012*\"台積\" + 0.011*\"台灣\" + 0.008*\"製程\"\n",
      "2025-04-19 00:09:31,970 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"應徵\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:31,970 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.006*\"技術\" + 0.005*\"台灣\" + 0.005*\"員工\" + 0.004*\"問題\" + 0.004*\"科技\" + 0.004*\"工程師\" + 0.004*\"目前\" + 0.004*\"美國\" + 0.004*\"報導\"\n",
      "2025-04-19 00:09:31,971 : INFO : topic #0 (0.167): 0.032*\"工作\" + 0.022*\"方式\" + 0.014*\"工資\" + 0.014*\"依法\" + 0.012*\"推定\" + 0.011*\"內容\" + 0.010*\"休息\" + 0.010*\"每日\" + 0.010*\"應徵\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:31,972 : INFO : topic #1 (0.167): 0.027*\"工作\" + 0.015*\"方式\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.010*\"情形\" + 0.010*\"文字\" + 0.010*\"推定\" + 0.010*\"資訊\" + 0.010*\"砍除\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:31,972 : INFO : topic diff=0.821602, rho=0.299409\n",
      "2025-04-19 00:09:31,972 : INFO : PROGRESS: pass 2, at document #4000/16310\n",
      "2025-04-19 00:09:32,552 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:32,555 : INFO : topic #2 (0.167): 0.052*\"工作\" + 0.018*\"時間\" + 0.014*\"面試\" + 0.012*\"小時\" + 0.010*\"方式\" + 0.008*\"內容\" + 0.007*\"工時\" + 0.007*\"經驗\" + 0.007*\"公司\" + 0.006*\"需要\"\n",
      "2025-04-19 00:09:32,556 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"應徵\" + 0.011*\"資訊\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:32,556 : INFO : topic #3 (0.167): 0.018*\"美國\" + 0.017*\"晶片\" + 0.016*\"表示\" + 0.015*\"台積電\" + 0.015*\"半導體\" + 0.015*\"中國\" + 0.012*\"投資\" + 0.011*\"台積\" + 0.011*\"台灣\" + 0.008*\"製程\"\n",
      "2025-04-19 00:09:32,557 : INFO : topic #1 (0.167): 0.025*\"工作\" + 0.014*\"方式\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.010*\"文字\" + 0.010*\"情形\" + 0.010*\"資訊\" + 0.009*\"砍除\" + 0.009*\"第一項\" + 0.009*\"推定\"\n",
      "2025-04-19 00:09:32,558 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.006*\"技術\" + 0.005*\"台灣\" + 0.005*\"員工\" + 0.004*\"問題\" + 0.004*\"科技\" + 0.004*\"資料\" + 0.004*\"工程師\" + 0.004*\"目前\" + 0.003*\"美國\"\n",
      "2025-04-19 00:09:32,558 : INFO : topic diff=0.335901, rho=0.299409\n",
      "2025-04-19 00:09:32,558 : INFO : PROGRESS: pass 2, at document #6000/16310\n",
      "2025-04-19 00:09:33,058 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:33,061 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"資訊\" + 0.011*\"應徵\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:33,061 : INFO : topic #1 (0.167): 0.023*\"工作\" + 0.013*\"方式\" + 0.013*\"聯絡\" + 0.012*\"內容\" + 0.010*\"資訊\" + 0.010*\"文字\" + 0.009*\"電話\" + 0.009*\"情形\" + 0.009*\"聯絡人\" + 0.009*\"砍除\"\n",
      "2025-04-19 00:09:33,062 : INFO : topic #2 (0.167): 0.051*\"工作\" + 0.019*\"時間\" + 0.014*\"面試\" + 0.012*\"小時\" + 0.011*\"方式\" + 0.009*\"經驗\" + 0.009*\"內容\" + 0.007*\"工時\" + 0.007*\"公司\" + 0.006*\"需要\"\n",
      "2025-04-19 00:09:33,062 : INFO : topic #0 (0.167): 0.036*\"工作\" + 0.025*\"方式\" + 0.015*\"工資\" + 0.015*\"依法\" + 0.015*\"推定\" + 0.012*\"每日\" + 0.012*\"未註明\" + 0.012*\"單位\" + 0.012*\"小時\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:33,063 : INFO : topic #3 (0.167): 0.016*\"美國\" + 0.015*\"晶片\" + 0.015*\"表示\" + 0.013*\"中國\" + 0.013*\"半導體\" + 0.013*\"台積電\" + 0.011*\"投資\" + 0.010*\"台積\" + 0.010*\"台灣\" + 0.007*\"製程\"\n",
      "2025-04-19 00:09:33,064 : INFO : topic diff=0.222103, rho=0.299409\n",
      "2025-04-19 00:09:33,064 : INFO : PROGRESS: pass 2, at document #8000/16310\n",
      "2025-04-19 00:09:33,353 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:33,356 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"資訊\" + 0.011*\"應徵\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:33,357 : INFO : topic #2 (0.167): 0.053*\"工作\" + 0.019*\"面試\" + 0.019*\"時間\" + 0.012*\"經驗\" + 0.011*\"小時\" + 0.011*\"方式\" + 0.009*\"公司\" + 0.009*\"內容\" + 0.007*\"工時\" + 0.006*\"職缺\"\n",
      "2025-04-19 00:09:33,357 : INFO : topic #3 (0.167): 0.015*\"美國\" + 0.014*\"晶片\" + 0.014*\"表示\" + 0.014*\"半導體\" + 0.014*\"中國\" + 0.013*\"台積電\" + 0.010*\"台灣\" + 0.010*\"投資\" + 0.010*\"台積\" + 0.007*\"製程\"\n",
      "2025-04-19 00:09:33,358 : INFO : topic #1 (0.167): 0.023*\"工作\" + 0.013*\"方式\" + 0.013*\"聯絡\" + 0.012*\"內容\" + 0.010*\"資訊\" + 0.010*\"文字\" + 0.009*\"電話\" + 0.009*\"情形\" + 0.009*\"聯絡人\" + 0.009*\"砍除\"\n",
      "2025-04-19 00:09:33,358 : INFO : topic #0 (0.167): 0.036*\"工作\" + 0.025*\"方式\" + 0.015*\"工資\" + 0.015*\"依法\" + 0.015*\"推定\" + 0.012*\"每日\" + 0.012*\"單位\" + 0.012*\"未註明\" + 0.012*\"小時\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:33,358 : INFO : topic diff=0.335649, rho=0.299409\n",
      "2025-04-19 00:09:33,359 : INFO : PROGRESS: pass 2, at document #10000/16310\n",
      "2025-04-19 00:09:33,614 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:33,617 : INFO : topic #0 (0.167): 0.036*\"工作\" + 0.025*\"方式\" + 0.015*\"工資\" + 0.015*\"依法\" + 0.015*\"推定\" + 0.012*\"每日\" + 0.012*\"單位\" + 0.012*\"未註明\" + 0.012*\"小時\" + 0.011*\"休息\"\n",
      "2025-04-19 00:09:33,618 : INFO : topic #3 (0.167): 0.014*\"美國\" + 0.014*\"表示\" + 0.014*\"半導體\" + 0.013*\"中國\" + 0.013*\"晶片\" + 0.011*\"台積電\" + 0.011*\"台灣\" + 0.010*\"投資\" + 0.009*\"台積\" + 0.008*\"問卷\"\n",
      "2025-04-19 00:09:33,618 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"資訊\" + 0.011*\"應徵\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:33,619 : INFO : topic #5 (0.167): 0.016*\"公司\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"開發\" + 0.006*\"技術\" + 0.005*\"目前\" + 0.004*\"產品\" + 0.004*\"使用\" + 0.004*\"比較\" + 0.004*\"知道\"\n",
      "2025-04-19 00:09:33,619 : INFO : topic #2 (0.167): 0.053*\"工作\" + 0.021*\"面試\" + 0.019*\"時間\" + 0.013*\"經驗\" + 0.011*\"方式\" + 0.011*\"小時\" + 0.010*\"公司\" + 0.009*\"內容\" + 0.008*\"職缺\" + 0.007*\"工時\"\n",
      "2025-04-19 00:09:33,619 : INFO : topic diff=0.270297, rho=0.299409\n",
      "2025-04-19 00:09:33,620 : INFO : PROGRESS: pass 2, at document #12000/16310\n",
      "2025-04-19 00:09:33,880 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:33,883 : INFO : topic #0 (0.167): 0.035*\"工作\" + 0.025*\"方式\" + 0.015*\"工資\" + 0.015*\"依法\" + 0.014*\"推定\" + 0.012*\"單位\" + 0.012*\"每日\" + 0.011*\"未註明\" + 0.011*\"小時\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:33,883 : INFO : topic #3 (0.167): 0.017*\"晶片\" + 0.017*\"半導體\" + 0.014*\"表示\" + 0.013*\"台積電\" + 0.012*\"台灣\" + 0.012*\"美國\" + 0.011*\"中國\" + 0.010*\"台積\" + 0.008*\"製程\" + 0.008*\"投資\"\n",
      "2025-04-19 00:09:33,884 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"資訊\" + 0.011*\"應徵\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:33,884 : INFO : topic #2 (0.167): 0.053*\"工作\" + 0.021*\"面試\" + 0.019*\"時間\" + 0.013*\"經驗\" + 0.011*\"公司\" + 0.010*\"方式\" + 0.010*\"小時\" + 0.009*\"內容\" + 0.008*\"職缺\" + 0.007*\"薪資\"\n",
      "2025-04-19 00:09:33,885 : INFO : topic #1 (0.167): 0.023*\"工作\" + 0.013*\"方式\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.010*\"資訊\" + 0.010*\"文字\" + 0.009*\"電話\" + 0.009*\"情形\" + 0.009*\"聯絡人\" + 0.009*\"報名\"\n",
      "2025-04-19 00:09:33,885 : INFO : topic diff=0.331999, rho=0.299409\n",
      "2025-04-19 00:09:33,886 : INFO : PROGRESS: pass 2, at document #14000/16310\n",
      "2025-04-19 00:09:34,105 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:34,108 : INFO : topic #1 (0.167): 0.022*\"工作\" + 0.013*\"方式\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.010*\"資訊\" + 0.010*\"文字\" + 0.009*\"電話\" + 0.009*\"情形\" + 0.009*\"報名\" + 0.009*\"聯絡人\"\n",
      "2025-04-19 00:09:34,109 : INFO : topic #5 (0.167): 0.014*\"公司\" + 0.006*\"問題\" + 0.005*\"技術\" + 0.005*\"工程師\" + 0.004*\"目前\" + 0.004*\"台灣\" + 0.004*\"開發\" + 0.004*\"員工\" + 0.004*\"工作\" + 0.004*\"產品\"\n",
      "2025-04-19 00:09:34,109 : INFO : topic #2 (0.167): 0.053*\"工作\" + 0.020*\"面試\" + 0.018*\"時間\" + 0.012*\"經驗\" + 0.011*\"公司\" + 0.010*\"小時\" + 0.009*\"方式\" + 0.009*\"內容\" + 0.008*\"薪資\" + 0.007*\"職缺\"\n",
      "2025-04-19 00:09:34,110 : INFO : topic #0 (0.167): 0.035*\"工作\" + 0.025*\"方式\" + 0.015*\"工資\" + 0.015*\"依法\" + 0.014*\"推定\" + 0.012*\"單位\" + 0.012*\"每日\" + 0.011*\"小時\" + 0.011*\"未註明\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:34,110 : INFO : topic #3 (0.167): 0.017*\"晶片\" + 0.015*\"半導體\" + 0.015*\"台灣\" + 0.014*\"表示\" + 0.013*\"台積電\" + 0.013*\"中國\" + 0.013*\"美國\" + 0.010*\"台積\" + 0.008*\"英特爾\" + 0.007*\"全球\"\n",
      "2025-04-19 00:09:34,111 : INFO : topic diff=0.320091, rho=0.299409\n",
      "2025-04-19 00:09:34,111 : INFO : PROGRESS: pass 2, at document #16000/16310\n",
      "2025-04-19 00:09:34,318 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:34,321 : INFO : topic #1 (0.167): 0.022*\"工作\" + 0.013*\"方式\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.010*\"資訊\" + 0.010*\"文字\" + 0.009*\"報名\" + 0.009*\"電話\" + 0.009*\"情形\" + 0.009*\"聯絡人\"\n",
      "2025-04-19 00:09:34,321 : INFO : topic #3 (0.167): 0.018*\"晶片\" + 0.016*\"美國\" + 0.014*\"表示\" + 0.014*\"半導體\" + 0.014*\"台灣\" + 0.013*\"台積電\" + 0.013*\"中國\" + 0.010*\"台積\" + 0.009*\"英特爾\" + 0.007*\"積電\"\n",
      "2025-04-19 00:09:34,322 : INFO : topic #5 (0.167): 0.014*\"公司\" + 0.006*\"技術\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"員工\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"產品\" + 0.003*\"工作\" + 0.003*\"開發\"\n",
      "2025-04-19 00:09:34,322 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"應徵\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:34,323 : INFO : topic #0 (0.167): 0.033*\"工作\" + 0.024*\"方式\" + 0.015*\"工資\" + 0.014*\"依法\" + 0.013*\"推定\" + 0.012*\"單位\" + 0.011*\"每日\" + 0.011*\"小時\" + 0.011*\"未註明\" + 0.011*\"內容\"\n",
      "2025-04-19 00:09:34,323 : INFO : topic diff=0.269258, rho=0.299409\n",
      "2025-04-19 00:09:34,425 : INFO : -8.344 per-word bound, 325.0 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:09:34,426 : INFO : PROGRESS: pass 2, at document #16310/16310\n",
      "2025-04-19 00:09:34,461 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:34,464 : INFO : topic #0 (0.167): 0.033*\"工作\" + 0.023*\"方式\" + 0.015*\"工資\" + 0.014*\"依法\" + 0.013*\"推定\" + 0.012*\"單位\" + 0.011*\"每日\" + 0.011*\"小時\" + 0.010*\"未註明\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:34,465 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.007*\"技術\" + 0.005*\"問題\" + 0.005*\"員工\" + 0.005*\"工程師\" + 0.004*\"科技\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"現在\" + 0.004*\"知道\"\n",
      "2025-04-19 00:09:34,465 : INFO : topic #3 (0.167): 0.021*\"美國\" + 0.018*\"晶片\" + 0.015*\"台灣\" + 0.014*\"台積電\" + 0.013*\"表示\" + 0.012*\"中國\" + 0.012*\"台積\" + 0.012*\"半導體\" + 0.011*\"投資\" + 0.009*\"英特爾\"\n",
      "2025-04-19 00:09:34,466 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.011*\"資訊\" + 0.011*\"國定假日\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:34,466 : INFO : topic #2 (0.167): 0.049*\"工作\" + 0.018*\"面試\" + 0.016*\"時間\" + 0.011*\"公司\" + 0.010*\"經驗\" + 0.010*\"小時\" + 0.008*\"內容\" + 0.008*\"工時\" + 0.008*\"薪資\" + 0.007*\"方式\"\n",
      "2025-04-19 00:09:34,467 : INFO : topic diff=0.279560, rho=0.299409\n",
      "2025-04-19 00:09:34,467 : INFO : PROGRESS: pass 3, at document #2000/16310\n",
      "2025-04-19 00:09:34,992 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:34,995 : INFO : topic #5 (0.167): 0.014*\"公司\" + 0.006*\"技術\" + 0.005*\"問題\" + 0.005*\"員工\" + 0.005*\"工程師\" + 0.004*\"科技\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"現在\" + 0.003*\"知道\"\n",
      "2025-04-19 00:09:34,996 : INFO : topic #2 (0.167): 0.050*\"工作\" + 0.017*\"時間\" + 0.017*\"面試\" + 0.010*\"小時\" + 0.010*\"經驗\" + 0.010*\"公司\" + 0.009*\"內容\" + 0.008*\"方式\" + 0.008*\"工時\" + 0.006*\"薪資\"\n",
      "2025-04-19 00:09:34,996 : INFO : topic #0 (0.167): 0.037*\"工作\" + 0.026*\"方式\" + 0.017*\"工資\" + 0.016*\"依法\" + 0.015*\"推定\" + 0.013*\"每日\" + 0.013*\"單位\" + 0.013*\"小時\" + 0.013*\"未註明\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:34,997 : INFO : topic #3 (0.167): 0.021*\"美國\" + 0.017*\"晶片\" + 0.015*\"台灣\" + 0.014*\"台積電\" + 0.013*\"表示\" + 0.012*\"中國\" + 0.012*\"台積\" + 0.012*\"半導體\" + 0.011*\"投資\" + 0.009*\"英特爾\"\n",
      "2025-04-19 00:09:34,998 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"應徵\" + 0.011*\"資訊\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:34,999 : INFO : topic diff=0.724106, rho=0.286829\n",
      "2025-04-19 00:09:34,999 : INFO : PROGRESS: pass 3, at document #4000/16310\n",
      "2025-04-19 00:09:35,538 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:35,541 : INFO : topic #0 (0.167): 0.038*\"工作\" + 0.026*\"方式\" + 0.017*\"工資\" + 0.016*\"推定\" + 0.015*\"依法\" + 0.014*\"小時\" + 0.014*\"單位\" + 0.013*\"未註明\" + 0.013*\"每日\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:35,541 : INFO : topic #3 (0.167): 0.020*\"美國\" + 0.017*\"晶片\" + 0.015*\"台灣\" + 0.014*\"台積電\" + 0.013*\"表示\" + 0.013*\"中國\" + 0.012*\"台積\" + 0.012*\"半導體\" + 0.011*\"投資\" + 0.009*\"英特爾\"\n",
      "2025-04-19 00:09:35,542 : INFO : topic #2 (0.167): 0.049*\"工作\" + 0.018*\"時間\" + 0.016*\"面試\" + 0.011*\"小時\" + 0.010*\"經驗\" + 0.009*\"內容\" + 0.008*\"公司\" + 0.008*\"方式\" + 0.007*\"工時\" + 0.007*\"需要\"\n",
      "2025-04-19 00:09:35,542 : INFO : topic #1 (0.167): 0.014*\"報名\" + 0.014*\"電話\" + 0.013*\"工作\" + 0.013*\"活動\" + 0.012*\"聯絡\" + 0.011*\"方式\" + 0.011*\"內容\" + 0.011*\"單位名稱\" + 0.010*\"單位地址\" + 0.010*\"台北市\"\n",
      "2025-04-19 00:09:35,543 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.014*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"應徵\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:35,543 : INFO : topic diff=0.293081, rho=0.286829\n",
      "2025-04-19 00:09:35,544 : INFO : PROGRESS: pass 3, at document #6000/16310\n",
      "2025-04-19 00:09:35,963 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:35,966 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.006*\"技術\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.005*\"產品\" + 0.004*\"員工\" + 0.004*\"目前\" + 0.004*\"台灣\" + 0.004*\"知道\" + 0.004*\"科技\"\n",
      "2025-04-19 00:09:35,967 : INFO : topic #1 (0.167): 0.019*\"報名\" + 0.016*\"活動\" + 0.016*\"電話\" + 0.012*\"聯絡\" + 0.011*\"台北市\" + 0.011*\"內容\" + 0.010*\"方式\" + 0.010*\"單位名稱\" + 0.010*\"工作\" + 0.010*\"單位地址\"\n",
      "2025-04-19 00:09:35,967 : INFO : topic #2 (0.167): 0.048*\"工作\" + 0.018*\"時間\" + 0.015*\"面試\" + 0.011*\"經驗\" + 0.010*\"小時\" + 0.009*\"公司\" + 0.009*\"內容\" + 0.008*\"方式\" + 0.007*\"工時\" + 0.007*\"需要\"\n",
      "2025-04-19 00:09:35,968 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.014*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"應徵\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:35,968 : INFO : topic #3 (0.167): 0.019*\"美國\" + 0.016*\"晶片\" + 0.014*\"台灣\" + 0.013*\"台積電\" + 0.013*\"表示\" + 0.012*\"中國\" + 0.011*\"台積\" + 0.011*\"半導體\" + 0.010*\"投資\" + 0.009*\"英特爾\"\n",
      "2025-04-19 00:09:35,969 : INFO : topic diff=0.205952, rho=0.286829\n",
      "2025-04-19 00:09:35,969 : INFO : PROGRESS: pass 3, at document #8000/16310\n",
      "2025-04-19 00:09:36,254 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:36,256 : INFO : topic #2 (0.167): 0.050*\"工作\" + 0.020*\"面試\" + 0.019*\"時間\" + 0.014*\"經驗\" + 0.011*\"公司\" + 0.010*\"小時\" + 0.009*\"方式\" + 0.009*\"內容\" + 0.008*\"職缺\" + 0.007*\"薪資\"\n",
      "2025-04-19 00:09:36,257 : INFO : topic #0 (0.167): 0.039*\"工作\" + 0.027*\"方式\" + 0.017*\"工資\" + 0.016*\"推定\" + 0.016*\"依法\" + 0.014*\"小時\" + 0.014*\"每日\" + 0.013*\"單位\" + 0.013*\"未註明\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:36,258 : INFO : topic #1 (0.167): 0.019*\"報名\" + 0.016*\"活動\" + 0.016*\"電話\" + 0.012*\"聯絡\" + 0.011*\"台北市\" + 0.011*\"內容\" + 0.010*\"方式\" + 0.010*\"單位名稱\" + 0.010*\"工作\" + 0.009*\"單位地址\"\n",
      "2025-04-19 00:09:36,258 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.014*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"應徵\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:36,259 : INFO : topic #3 (0.167): 0.019*\"美國\" + 0.015*\"晶片\" + 0.015*\"台灣\" + 0.013*\"台積電\" + 0.012*\"中國\" + 0.012*\"表示\" + 0.011*\"半導體\" + 0.011*\"台積\" + 0.010*\"投資\" + 0.008*\"英特爾\"\n",
      "2025-04-19 00:09:36,259 : INFO : topic diff=0.317737, rho=0.286829\n",
      "2025-04-19 00:09:36,259 : INFO : PROGRESS: pass 3, at document #10000/16310\n",
      "2025-04-19 00:09:36,503 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:36,506 : INFO : topic #1 (0.167): 0.020*\"報名\" + 0.017*\"活動\" + 0.016*\"電話\" + 0.012*\"聯絡\" + 0.011*\"台北市\" + 0.011*\"內容\" + 0.010*\"方式\" + 0.009*\"工作\" + 0.009*\"單位名稱\" + 0.009*\"單位地址\"\n",
      "2025-04-19 00:09:36,506 : INFO : topic #0 (0.167): 0.039*\"工作\" + 0.027*\"方式\" + 0.016*\"工資\" + 0.016*\"推定\" + 0.015*\"依法\" + 0.014*\"小時\" + 0.013*\"每日\" + 0.013*\"單位\" + 0.013*\"未註明\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:36,507 : INFO : topic #3 (0.167): 0.018*\"美國\" + 0.015*\"台灣\" + 0.014*\"晶片\" + 0.012*\"中國\" + 0.012*\"表示\" + 0.012*\"台積電\" + 0.012*\"半導體\" + 0.010*\"台積\" + 0.010*\"投資\" + 0.007*\"英特爾\"\n",
      "2025-04-19 00:09:36,507 : INFO : topic #5 (0.167): 0.016*\"公司\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"技術\" + 0.006*\"開發\" + 0.005*\"目前\" + 0.005*\"產品\" + 0.005*\"比較\" + 0.004*\"覺得\" + 0.004*\"知道\"\n",
      "2025-04-19 00:09:36,507 : INFO : topic #2 (0.167): 0.049*\"工作\" + 0.021*\"面試\" + 0.019*\"時間\" + 0.015*\"經驗\" + 0.012*\"公司\" + 0.010*\"小時\" + 0.010*\"內容\" + 0.009*\"職缺\" + 0.009*\"方式\" + 0.009*\"薪資\"\n",
      "2025-04-19 00:09:36,508 : INFO : topic diff=0.252901, rho=0.286829\n",
      "2025-04-19 00:09:36,509 : INFO : PROGRESS: pass 3, at document #12000/16310\n",
      "2025-04-19 00:09:36,776 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:36,778 : INFO : topic #3 (0.167): 0.016*\"晶片\" + 0.015*\"台灣\" + 0.014*\"半導體\" + 0.014*\"美國\" + 0.012*\"表示\" + 0.012*\"台積電\" + 0.010*\"中國\" + 0.010*\"台積\" + 0.008*\"投資\" + 0.007*\"全球\"\n",
      "2025-04-19 00:09:36,779 : INFO : topic #0 (0.167): 0.038*\"工作\" + 0.027*\"方式\" + 0.016*\"工資\" + 0.015*\"推定\" + 0.015*\"依法\" + 0.014*\"小時\" + 0.014*\"單位\" + 0.013*\"每日\" + 0.013*\"未註明\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:36,779 : INFO : topic #2 (0.167): 0.049*\"工作\" + 0.021*\"面試\" + 0.018*\"時間\" + 0.014*\"經驗\" + 0.012*\"公司\" + 0.010*\"小時\" + 0.009*\"內容\" + 0.009*\"薪資\" + 0.009*\"職缺\" + 0.009*\"方式\"\n",
      "2025-04-19 00:09:36,780 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.014*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"應徵\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:36,780 : INFO : topic #1 (0.167): 0.021*\"報名\" + 0.019*\"活動\" + 0.015*\"電話\" + 0.012*\"聯絡\" + 0.011*\"台北市\" + 0.011*\"內容\" + 0.011*\"方式\" + 0.009*\"工作\" + 0.009*\"人數\" + 0.009*\"時間\"\n",
      "2025-04-19 00:09:36,781 : INFO : topic diff=0.301646, rho=0.286829\n",
      "2025-04-19 00:09:36,781 : INFO : PROGRESS: pass 3, at document #14000/16310\n",
      "2025-04-19 00:09:36,995 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:36,998 : INFO : topic #2 (0.167): 0.049*\"工作\" + 0.020*\"面試\" + 0.017*\"時間\" + 0.014*\"經驗\" + 0.012*\"公司\" + 0.010*\"薪資\" + 0.009*\"內容\" + 0.009*\"小時\" + 0.009*\"職缺\" + 0.008*\"方式\"\n",
      "2025-04-19 00:09:36,999 : INFO : topic #3 (0.167): 0.016*\"台灣\" + 0.015*\"晶片\" + 0.013*\"美國\" + 0.013*\"表示\" + 0.013*\"半導體\" + 0.012*\"台積電\" + 0.011*\"中國\" + 0.010*\"台積\" + 0.008*\"產業\" + 0.007*\"全球\"\n",
      "2025-04-19 00:09:36,999 : INFO : topic #0 (0.167): 0.038*\"工作\" + 0.026*\"方式\" + 0.016*\"工資\" + 0.015*\"依法\" + 0.015*\"推定\" + 0.014*\"單位\" + 0.014*\"小時\" + 0.013*\"每日\" + 0.012*\"未註明\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:37,000 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"應徵\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:09:37,000 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.006*\"問題\" + 0.006*\"技術\" + 0.005*\"工程師\" + 0.004*\"工作\" + 0.004*\"目前\" + 0.004*\"開發\" + 0.004*\"產品\" + 0.004*\"現在\" + 0.004*\"比較\"\n",
      "2025-04-19 00:09:37,001 : INFO : topic diff=0.294485, rho=0.286829\n",
      "2025-04-19 00:09:37,001 : INFO : PROGRESS: pass 3, at document #16000/16310\n",
      "2025-04-19 00:09:37,207 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:37,210 : INFO : topic #2 (0.167): 0.048*\"工作\" + 0.020*\"面試\" + 0.016*\"時間\" + 0.013*\"公司\" + 0.012*\"經驗\" + 0.010*\"薪資\" + 0.009*\"內容\" + 0.009*\"小時\" + 0.008*\"職缺\" + 0.007*\"工時\"\n",
      "2025-04-19 00:09:37,210 : INFO : topic #3 (0.167): 0.016*\"晶片\" + 0.015*\"美國\" + 0.015*\"台灣\" + 0.013*\"表示\" + 0.012*\"半導體\" + 0.012*\"台積電\" + 0.012*\"中國\" + 0.009*\"台積\" + 0.009*\"英特爾\" + 0.008*\"報導\"\n",
      "2025-04-19 00:09:37,211 : INFO : topic #0 (0.167): 0.037*\"工作\" + 0.026*\"方式\" + 0.017*\"工資\" + 0.015*\"依法\" + 0.015*\"推定\" + 0.014*\"單位\" + 0.014*\"小時\" + 0.013*\"每日\" + 0.012*\"未註明\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:37,211 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.006*\"技術\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.004*\"目前\" + 0.004*\"工作\" + 0.004*\"產品\" + 0.004*\"現在\" + 0.004*\"員工\" + 0.004*\"開發\"\n",
      "2025-04-19 00:09:37,212 : INFO : topic #1 (0.167): 0.023*\"報名\" + 0.020*\"活動\" + 0.015*\"電話\" + 0.012*\"聯絡\" + 0.010*\"台北市\" + 0.010*\"方式\" + 0.010*\"內容\" + 0.009*\"人數\" + 0.009*\"時間\" + 0.009*\"工作\"\n",
      "2025-04-19 00:09:37,212 : INFO : topic diff=0.247734, rho=0.286829\n",
      "2025-04-19 00:09:37,288 : INFO : -8.324 per-word bound, 320.4 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:09:37,289 : INFO : PROGRESS: pass 3, at document #16310/16310\n",
      "2025-04-19 00:09:37,323 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:37,326 : INFO : topic #0 (0.167): 0.037*\"工作\" + 0.025*\"方式\" + 0.017*\"工資\" + 0.015*\"依法\" + 0.014*\"推定\" + 0.013*\"小時\" + 0.013*\"單位\" + 0.013*\"每日\" + 0.012*\"未註明\" + 0.011*\"休息\"\n",
      "2025-04-19 00:09:37,327 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"應徵\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:09:37,327 : INFO : topic #2 (0.167): 0.046*\"工作\" + 0.018*\"面試\" + 0.016*\"時間\" + 0.013*\"公司\" + 0.011*\"經驗\" + 0.010*\"薪資\" + 0.010*\"小時\" + 0.008*\"內容\" + 0.008*\"工時\" + 0.007*\"職缺\"\n",
      "2025-04-19 00:09:37,328 : INFO : topic #1 (0.167): 0.024*\"報名\" + 0.020*\"活動\" + 0.014*\"電話\" + 0.011*\"聯絡\" + 0.010*\"台北市\" + 0.010*\"問卷\" + 0.010*\"方式\" + 0.010*\"內容\" + 0.010*\"時間\" + 0.009*\"工作\"\n",
      "2025-04-19 00:09:37,328 : INFO : topic #3 (0.167): 0.020*\"美國\" + 0.016*\"晶片\" + 0.015*\"台灣\" + 0.013*\"台積電\" + 0.012*\"表示\" + 0.011*\"台積\" + 0.011*\"中國\" + 0.011*\"半導體\" + 0.010*\"投資\" + 0.009*\"英特爾\"\n",
      "2025-04-19 00:09:37,328 : INFO : topic diff=0.256199, rho=0.286829\n",
      "2025-04-19 00:09:37,329 : INFO : PROGRESS: pass 4, at document #2000/16310\n",
      "2025-04-19 00:09:37,805 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:37,808 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:37,808 : INFO : topic #1 (0.167): 0.024*\"報名\" + 0.021*\"活動\" + 0.018*\"電話\" + 0.013*\"台北市\" + 0.012*\"聯絡\" + 0.010*\"舉辦\" + 0.010*\"內容\" + 0.010*\"時間\" + 0.010*\"人數\" + 0.010*\"通知\"\n",
      "2025-04-19 00:09:37,809 : INFO : topic #3 (0.167): 0.020*\"美國\" + 0.016*\"晶片\" + 0.015*\"台灣\" + 0.013*\"台積電\" + 0.012*\"表示\" + 0.011*\"中國\" + 0.011*\"台積\" + 0.011*\"半導體\" + 0.010*\"投資\" + 0.009*\"英特爾\"\n",
      "2025-04-19 00:09:37,809 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.007*\"技術\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"工作\" + 0.004*\"現在\" + 0.004*\"員工\" + 0.004*\"知道\" + 0.004*\"目前\" + 0.004*\"產品\"\n",
      "2025-04-19 00:09:37,810 : INFO : topic #0 (0.167): 0.039*\"工作\" + 0.027*\"方式\" + 0.017*\"工資\" + 0.016*\"推定\" + 0.016*\"依法\" + 0.014*\"小時\" + 0.014*\"單位\" + 0.013*\"每日\" + 0.013*\"未註明\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:37,810 : INFO : topic diff=0.647242, rho=0.275711\n",
      "2025-04-19 00:09:37,811 : INFO : PROGRESS: pass 4, at document #4000/16310\n",
      "2025-04-19 00:09:38,239 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:38,242 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:38,243 : INFO : topic #3 (0.167): 0.019*\"美國\" + 0.016*\"晶片\" + 0.015*\"台灣\" + 0.013*\"台積電\" + 0.012*\"表示\" + 0.011*\"中國\" + 0.011*\"台積\" + 0.010*\"半導體\" + 0.010*\"投資\" + 0.009*\"英特爾\"\n",
      "2025-04-19 00:09:38,243 : INFO : topic #0 (0.167): 0.040*\"工作\" + 0.027*\"方式\" + 0.017*\"工資\" + 0.016*\"推定\" + 0.015*\"依法\" + 0.015*\"小時\" + 0.014*\"單位\" + 0.013*\"未註明\" + 0.013*\"每日\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:38,244 : INFO : topic #1 (0.167): 0.027*\"報名\" + 0.024*\"活動\" + 0.020*\"電話\" + 0.014*\"台北市\" + 0.012*\"聯絡\" + 0.011*\"人數\" + 0.011*\"車馬費\" + 0.011*\"舉辦\" + 0.011*\"時間\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:38,244 : INFO : topic #2 (0.167): 0.046*\"工作\" + 0.017*\"時間\" + 0.016*\"面試\" + 0.011*\"經驗\" + 0.010*\"小時\" + 0.010*\"公司\" + 0.009*\"內容\" + 0.007*\"工時\" + 0.007*\"相關\" + 0.007*\"薪資\"\n",
      "2025-04-19 00:09:38,245 : INFO : topic diff=0.283261, rho=0.275711\n",
      "2025-04-19 00:09:38,245 : INFO : PROGRESS: pass 4, at document #6000/16310\n",
      "2025-04-19 00:09:38,616 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:38,619 : INFO : topic #2 (0.167): 0.044*\"工作\" + 0.017*\"時間\" + 0.015*\"面試\" + 0.012*\"經驗\" + 0.010*\"公司\" + 0.010*\"小時\" + 0.009*\"內容\" + 0.007*\"方式\" + 0.007*\"相關\" + 0.007*\"工時\"\n",
      "2025-04-19 00:09:38,620 : INFO : topic #3 (0.167): 0.018*\"美國\" + 0.015*\"晶片\" + 0.015*\"台灣\" + 0.012*\"台積電\" + 0.012*\"表示\" + 0.011*\"中國\" + 0.011*\"台積\" + 0.010*\"半導體\" + 0.010*\"投資\" + 0.008*\"英特爾\"\n",
      "2025-04-19 00:09:38,621 : INFO : topic #1 (0.167): 0.030*\"報名\" + 0.026*\"活動\" + 0.021*\"電話\" + 0.015*\"台北市\" + 0.012*\"車馬費\" + 0.012*\"人數\" + 0.012*\"聯絡\" + 0.012*\"舉辦\" + 0.011*\"訪問\" + 0.011*\"通知\"\n",
      "2025-04-19 00:09:38,621 : INFO : topic #0 (0.167): 0.041*\"工作\" + 0.027*\"方式\" + 0.017*\"工資\" + 0.016*\"推定\" + 0.016*\"依法\" + 0.015*\"小時\" + 0.014*\"每日\" + 0.014*\"單位\" + 0.013*\"未註明\" + 0.013*\"休息\"\n",
      "2025-04-19 00:09:38,622 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.006*\"技術\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.005*\"產品\" + 0.004*\"知道\" + 0.004*\"工作\" + 0.004*\"目前\" + 0.004*\"現在\" + 0.004*\"開發\"\n",
      "2025-04-19 00:09:38,622 : INFO : topic diff=0.210308, rho=0.275711\n",
      "2025-04-19 00:09:38,622 : INFO : PROGRESS: pass 4, at document #8000/16310\n",
      "2025-04-19 00:09:38,913 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:38,916 : INFO : topic #2 (0.167): 0.046*\"工作\" + 0.019*\"面試\" + 0.017*\"時間\" + 0.015*\"經驗\" + 0.012*\"公司\" + 0.010*\"小時\" + 0.009*\"內容\" + 0.009*\"職缺\" + 0.008*\"方式\" + 0.008*\"薪資\"\n",
      "2025-04-19 00:09:38,917 : INFO : topic #1 (0.167): 0.030*\"報名\" + 0.026*\"活動\" + 0.021*\"電話\" + 0.015*\"台北市\" + 0.012*\"舉辦\" + 0.012*\"車馬費\" + 0.012*\"人數\" + 0.012*\"聯絡\" + 0.011*\"通知\" + 0.011*\"訪問\"\n",
      "2025-04-19 00:09:38,918 : INFO : topic #3 (0.167): 0.019*\"美國\" + 0.015*\"台灣\" + 0.014*\"晶片\" + 0.011*\"台積電\" + 0.011*\"中國\" + 0.011*\"表示\" + 0.010*\"半導體\" + 0.010*\"台積\" + 0.009*\"投資\" + 0.008*\"英特爾\"\n",
      "2025-04-19 00:09:38,918 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:38,918 : INFO : topic #5 (0.167): 0.016*\"公司\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"技術\" + 0.005*\"工作\" + 0.005*\"開發\" + 0.005*\"產品\" + 0.005*\"目前\" + 0.005*\"覺得\" + 0.004*\"知道\"\n",
      "2025-04-19 00:09:38,919 : INFO : topic diff=0.300969, rho=0.275711\n",
      "2025-04-19 00:09:38,919 : INFO : PROGRESS: pass 4, at document #10000/16310\n",
      "2025-04-19 00:09:39,163 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:39,166 : INFO : topic #1 (0.167): 0.031*\"報名\" + 0.027*\"活動\" + 0.020*\"電話\" + 0.014*\"台北市\" + 0.012*\"舉辦\" + 0.012*\"人數\" + 0.012*\"聯絡\" + 0.012*\"車馬費\" + 0.011*\"通知\" + 0.011*\"時間\"\n",
      "2025-04-19 00:09:39,167 : INFO : topic #3 (0.167): 0.018*\"美國\" + 0.016*\"台灣\" + 0.013*\"晶片\" + 0.011*\"中國\" + 0.011*\"表示\" + 0.011*\"台積電\" + 0.010*\"半導體\" + 0.010*\"台積\" + 0.009*\"投資\" + 0.007*\"報導\"\n",
      "2025-04-19 00:09:39,167 : INFO : topic #2 (0.167): 0.046*\"工作\" + 0.020*\"面試\" + 0.017*\"時間\" + 0.015*\"經驗\" + 0.013*\"公司\" + 0.010*\"職缺\" + 0.010*\"薪資\" + 0.010*\"小時\" + 0.009*\"內容\" + 0.008*\"方式\"\n",
      "2025-04-19 00:09:39,168 : INFO : topic #5 (0.167): 0.016*\"公司\" + 0.008*\"問題\" + 0.006*\"工程師\" + 0.006*\"技術\" + 0.005*\"開發\" + 0.005*\"工作\" + 0.005*\"目前\" + 0.005*\"覺得\" + 0.005*\"比較\" + 0.005*\"產品\"\n",
      "2025-04-19 00:09:39,168 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:39,169 : INFO : topic diff=0.238076, rho=0.275711\n",
      "2025-04-19 00:09:39,169 : INFO : PROGRESS: pass 4, at document #12000/16310\n",
      "2025-04-19 00:09:39,389 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:39,392 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"技術\" + 0.005*\"工作\" + 0.005*\"開發\" + 0.005*\"目前\" + 0.005*\"比較\" + 0.004*\"覺得\" + 0.004*\"知道\"\n",
      "2025-04-19 00:09:39,392 : INFO : topic #3 (0.167): 0.015*\"台灣\" + 0.015*\"晶片\" + 0.014*\"美國\" + 0.012*\"半導體\" + 0.011*\"表示\" + 0.011*\"台積電\" + 0.010*\"中國\" + 0.009*\"台積\" + 0.008*\"報導\" + 0.007*\"產業\"\n",
      "2025-04-19 00:09:39,393 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:39,393 : INFO : topic #2 (0.167): 0.045*\"工作\" + 0.020*\"面試\" + 0.017*\"時間\" + 0.015*\"經驗\" + 0.014*\"公司\" + 0.010*\"薪資\" + 0.010*\"職缺\" + 0.009*\"小時\" + 0.009*\"內容\" + 0.008*\"方式\"\n",
      "2025-04-19 00:09:39,394 : INFO : topic #0 (0.167): 0.040*\"工作\" + 0.027*\"方式\" + 0.016*\"工資\" + 0.016*\"推定\" + 0.015*\"依法\" + 0.014*\"小時\" + 0.014*\"單位\" + 0.013*\"每日\" + 0.013*\"未註明\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:39,394 : INFO : topic diff=0.276355, rho=0.275711\n",
      "2025-04-19 00:09:39,395 : INFO : PROGRESS: pass 4, at document #14000/16310\n",
      "2025-04-19 00:09:39,630 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:39,633 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.006*\"問題\" + 0.006*\"技術\" + 0.005*\"工程師\" + 0.005*\"工作\" + 0.004*\"目前\" + 0.004*\"開發\" + 0.004*\"產品\" + 0.004*\"比較\" + 0.004*\"現在\"\n",
      "2025-04-19 00:09:39,634 : INFO : topic #3 (0.167): 0.016*\"台灣\" + 0.014*\"晶片\" + 0.013*\"美國\" + 0.012*\"表示\" + 0.012*\"半導體\" + 0.011*\"台積電\" + 0.010*\"中國\" + 0.009*\"台積\" + 0.009*\"報導\" + 0.008*\"產業\"\n",
      "2025-04-19 00:09:39,634 : INFO : topic #4 (0.167): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:39,635 : INFO : topic #1 (0.167): 0.032*\"報名\" + 0.029*\"活動\" + 0.019*\"電話\" + 0.013*\"台北市\" + 0.013*\"研究\" + 0.013*\"問卷\" + 0.012*\"舉辦\" + 0.011*\"人數\" + 0.011*\"聯絡\" + 0.010*\"參加\"\n",
      "2025-04-19 00:09:39,635 : INFO : topic #2 (0.167): 0.045*\"工作\" + 0.019*\"面試\" + 0.016*\"時間\" + 0.014*\"經驗\" + 0.014*\"公司\" + 0.011*\"薪資\" + 0.009*\"職缺\" + 0.009*\"內容\" + 0.009*\"小時\" + 0.007*\"方式\"\n",
      "2025-04-19 00:09:39,636 : INFO : topic diff=0.273552, rho=0.275711\n",
      "2025-04-19 00:09:39,636 : INFO : PROGRESS: pass 4, at document #16000/16310\n",
      "2025-04-19 00:09:39,833 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:39,836 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.006*\"問題\" + 0.006*\"技術\" + 0.005*\"工程師\" + 0.005*\"工作\" + 0.004*\"目前\" + 0.004*\"產品\" + 0.004*\"現在\" + 0.004*\"開發\" + 0.003*\"知道\"\n",
      "2025-04-19 00:09:39,837 : INFO : topic #4 (0.167): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:39,838 : INFO : topic #2 (0.167): 0.044*\"工作\" + 0.018*\"面試\" + 0.015*\"時間\" + 0.014*\"公司\" + 0.013*\"經驗\" + 0.011*\"薪資\" + 0.009*\"內容\" + 0.009*\"小時\" + 0.009*\"職缺\" + 0.007*\"工時\"\n",
      "2025-04-19 00:09:39,838 : INFO : topic #0 (0.167): 0.039*\"工作\" + 0.026*\"方式\" + 0.017*\"工資\" + 0.015*\"依法\" + 0.015*\"推定\" + 0.014*\"小時\" + 0.014*\"單位\" + 0.013*\"每日\" + 0.012*\"未註明\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:39,838 : INFO : topic #3 (0.167): 0.015*\"美國\" + 0.015*\"晶片\" + 0.014*\"台灣\" + 0.012*\"表示\" + 0.012*\"半導體\" + 0.011*\"台積電\" + 0.011*\"中國\" + 0.009*\"報導\" + 0.009*\"台積\" + 0.008*\"英特爾\"\n",
      "2025-04-19 00:09:39,839 : INFO : topic diff=0.230569, rho=0.275711\n",
      "2025-04-19 00:09:39,914 : INFO : -8.309 per-word bound, 317.2 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:09:39,914 : INFO : PROGRESS: pass 4, at document #16310/16310\n",
      "2025-04-19 00:09:39,974 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:39,977 : INFO : topic #4 (0.167): 0.032*\"工作\" + 0.013*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"方式\" + 0.011*\"資訊\" + 0.011*\"內容\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:09:39,977 : INFO : topic #1 (0.167): 0.031*\"報名\" + 0.029*\"活動\" + 0.019*\"問卷\" + 0.019*\"研究\" + 0.016*\"電話\" + 0.012*\"台北市\" + 0.011*\"舉辦\" + 0.011*\"時間\" + 0.011*\"人數\" + 0.011*\"參與\"\n",
      "2025-04-19 00:09:39,978 : INFO : topic #0 (0.167): 0.038*\"工作\" + 0.026*\"方式\" + 0.017*\"工資\" + 0.015*\"依法\" + 0.014*\"推定\" + 0.014*\"小時\" + 0.014*\"單位\" + 0.013*\"每日\" + 0.012*\"未註明\" + 0.012*\"休息\"\n",
      "2025-04-19 00:09:39,978 : INFO : topic #2 (0.167): 0.043*\"工作\" + 0.017*\"面試\" + 0.015*\"時間\" + 0.014*\"公司\" + 0.012*\"經驗\" + 0.011*\"薪資\" + 0.009*\"小時\" + 0.008*\"內容\" + 0.007*\"工時\" + 0.007*\"職缺\"\n",
      "2025-04-19 00:09:39,979 : INFO : topic #3 (0.167): 0.019*\"美國\" + 0.015*\"晶片\" + 0.015*\"台灣\" + 0.012*\"台積電\" + 0.012*\"表示\" + 0.011*\"台積\" + 0.011*\"中國\" + 0.010*\"半導體\" + 0.009*\"投資\" + 0.009*\"報導\"\n",
      "2025-04-19 00:09:39,979 : INFO : topic diff=0.237303, rho=0.275711\n",
      "2025-04-19 00:09:39,980 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel<num_terms=23261, num_topics=6, decay=0.5, chunksize=2000> in 15.43s', 'datetime': '2025-04-19T00:09:39.980242', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n",
      "2025-04-19 00:09:44,710 : INFO : -6.980 per-word bound, 126.2 perplexity estimate based on a held-out corpus of 16310 documents with 3460358 words\n",
      "2025-04-19 00:09:44,712 : INFO : using ParallelWordOccurrenceAccumulator<processes=7, batch_size=64> to estimate probabilities from sliding windows\n",
      "2025-04-19 00:09:48,520 : INFO : 1 batches submitted to accumulate stats from 64 documents (22660 virtual)\n",
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      "2025-04-19 00:09:51,667 : INFO : 205 batches submitted to accumulate stats from 13120 documents (3012040 virtual)\n",
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      "2025-04-19 00:09:51,736 : INFO : 213 batches submitted to accumulate stats from 13632 documents (3083808 virtual)\n",
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      "2025-04-19 00:09:51,913 : INFO : 231 batches submitted to accumulate stats from 14784 documents (3240270 virtual)\n",
      "2025-04-19 00:09:51,916 : INFO : 232 batches submitted to accumulate stats from 14848 documents (3249755 virtual)\n",
      "2025-04-19 00:09:51,917 : INFO : 233 batches submitted to accumulate stats from 14912 documents (3259377 virtual)\n",
      "2025-04-19 00:09:51,942 : INFO : 234 batches submitted to accumulate stats from 14976 documents (3269637 virtual)\n",
      "2025-04-19 00:09:51,950 : INFO : 235 batches submitted to accumulate stats from 15040 documents (3278311 virtual)\n",
      "2025-04-19 00:09:51,953 : INFO : 236 batches submitted to accumulate stats from 15104 documents (3286321 virtual)\n",
      "2025-04-19 00:09:51,956 : INFO : 237 batches submitted to accumulate stats from 15168 documents (3293385 virtual)\n",
      "2025-04-19 00:09:51,960 : INFO : 238 batches submitted to accumulate stats from 15232 documents (3300334 virtual)\n",
      "2025-04-19 00:09:51,963 : INFO : 239 batches submitted to accumulate stats from 15296 documents (3308226 virtual)\n",
      "2025-04-19 00:09:51,966 : INFO : 240 batches submitted to accumulate stats from 15360 documents (3317325 virtual)\n",
      "2025-04-19 00:09:51,993 : INFO : 241 batches submitted to accumulate stats from 15424 documents (3325778 virtual)\n",
      "2025-04-19 00:09:51,998 : INFO : 242 batches submitted to accumulate stats from 15488 documents (3335373 virtual)\n",
      "2025-04-19 00:09:52,008 : INFO : 243 batches submitted to accumulate stats from 15552 documents (3342716 virtual)\n",
      "2025-04-19 00:09:52,013 : INFO : 244 batches submitted to accumulate stats from 15616 documents (3350508 virtual)\n",
      "2025-04-19 00:09:52,016 : INFO : 245 batches submitted to accumulate stats from 15680 documents (3360131 virtual)\n",
      "2025-04-19 00:09:52,021 : INFO : 246 batches submitted to accumulate stats from 15744 documents (3370635 virtual)\n",
      "2025-04-19 00:09:52,029 : INFO : 247 batches submitted to accumulate stats from 15808 documents (3380994 virtual)\n",
      "2025-04-19 00:09:52,075 : INFO : 248 batches submitted to accumulate stats from 15872 documents (3389920 virtual)\n",
      "2025-04-19 00:09:52,077 : INFO : 249 batches submitted to accumulate stats from 15936 documents (3397487 virtual)\n",
      "2025-04-19 00:09:52,079 : INFO : 250 batches submitted to accumulate stats from 16000 documents (3406129 virtual)\n",
      "2025-04-19 00:09:52,084 : INFO : 251 batches submitted to accumulate stats from 16064 documents (3416805 virtual)\n",
      "2025-04-19 00:09:52,092 : INFO : 252 batches submitted to accumulate stats from 16128 documents (3426189 virtual)\n",
      "2025-04-19 00:09:52,108 : INFO : 253 batches submitted to accumulate stats from 16192 documents (3433824 virtual)\n",
      "2025-04-19 00:09:52,110 : INFO : 254 batches submitted to accumulate stats from 16256 documents (3443379 virtual)\n",
      "2025-04-19 00:09:52,119 : INFO : 255 batches submitted to accumulate stats from 16320 documents (3450914 virtual)\n",
      "2025-04-19 00:09:52,286 : INFO : 7 accumulators retrieved from output queue\n",
      "2025-04-19 00:09:52,296 : INFO : accumulated word occurrence stats for 3451622 virtual documents\n",
      "2025-04-19 00:09:52,383 : INFO : using symmetric alpha at 0.14285714285714285\n",
      "2025-04-19 00:09:52,384 : INFO : using symmetric eta at 0.14285714285714285\n",
      "2025-04-19 00:09:52,385 : INFO : using serial LDA version on this node\n",
      "2025-04-19 00:09:52,392 : INFO : running online (multi-pass) LDA training, 7 topics, 5 passes over the supplied corpus of 16310 documents, updating model once every 2000 documents, evaluating perplexity every 16310 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2025-04-19 00:09:52,393 : INFO : PROGRESS: pass 0, at document #2000/16310\n",
      "2025-04-19 00:09:53,102 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:53,105 : INFO : topic #0 (0.143): 0.030*\"工作\" + 0.014*\"方式\" + 0.013*\"應徵\" + 0.013*\"推定\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.012*\"單位\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:09:53,106 : INFO : topic #5 (0.143): 0.018*\"工作\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.010*\"聯絡\" + 0.010*\"應徵\" + 0.009*\"分類\" + 0.009*\"標題\" + 0.008*\"內容\" + 0.008*\"聯絡人\" + 0.008*\"小時\"\n",
      "2025-04-19 00:09:53,106 : INFO : topic #2 (0.143): 0.041*\"工作\" + 0.013*\"內容\" + 0.013*\"推定\" + 0.012*\"工資\" + 0.012*\"方式\" + 0.012*\"應徵\" + 0.011*\"情形\" + 0.010*\"砍除\" + 0.010*\"聯絡\" + 0.010*\"小時\"\n",
      "2025-04-19 00:09:53,107 : INFO : topic #3 (0.143): 0.020*\"工作\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.010*\"應徵\" + 0.010*\"推定\" + 0.010*\"聯絡人\" + 0.010*\"文字\" + 0.009*\"空白\" + 0.009*\"情形\" + 0.009*\"資訊\"\n",
      "2025-04-19 00:09:53,107 : INFO : topic #4 (0.143): 0.039*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"方式\" + 0.011*\"聯絡\" + 0.010*\"第一項\" + 0.010*\"單位\" + 0.010*\"聯絡人\" + 0.009*\"情形\" + 0.009*\"國定假日\"\n",
      "2025-04-19 00:09:53,108 : INFO : topic diff=7.406156, rho=1.000000\n",
      "2025-04-19 00:09:53,109 : INFO : PROGRESS: pass 0, at document #4000/16310\n",
      "2025-04-19 00:09:54,152 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:54,157 : INFO : topic #3 (0.143): 0.019*\"工作\" + 0.013*\"方式\" + 0.011*\"砍除\" + 0.009*\"聯絡人\" + 0.009*\"應徵\" + 0.009*\"文字\" + 0.008*\"資訊\" + 0.008*\"時間\" + 0.008*\"推定\" + 0.008*\"內容\"\n",
      "2025-04-19 00:09:54,158 : INFO : topic #2 (0.143): 0.042*\"工作\" + 0.015*\"方式\" + 0.014*\"推定\" + 0.013*\"工資\" + 0.013*\"內容\" + 0.012*\"小時\" + 0.011*\"單位\" + 0.011*\"應徵\" + 0.011*\"未註明\" + 0.010*\"情形\"\n",
      "2025-04-19 00:09:54,160 : INFO : topic #4 (0.143): 0.038*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.010*\"聯絡人\" + 0.010*\"國定假日\" + 0.010*\"砍除\"\n",
      "2025-04-19 00:09:54,162 : INFO : topic #6 (0.143): 0.025*\"報名\" + 0.022*\"活動\" + 0.020*\"電話\" + 0.014*\"台北市\" + 0.013*\"人數\" + 0.013*\"車馬費\" + 0.011*\"資料\" + 0.011*\"聯絡\" + 0.011*\"時間\" + 0.011*\"訪問\"\n",
      "2025-04-19 00:09:54,164 : INFO : topic #0 (0.143): 0.029*\"工作\" + 0.014*\"應徵\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.013*\"方式\" + 0.011*\"推定\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:54,165 : INFO : topic diff=0.693025, rho=0.707107\n",
      "2025-04-19 00:09:54,167 : INFO : PROGRESS: pass 0, at document #6000/16310\n",
      "2025-04-19 00:09:54,855 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:54,858 : INFO : topic #0 (0.143): 0.030*\"工作\" + 0.013*\"砍除\" + 0.013*\"應徵\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:54,859 : INFO : topic #6 (0.143): 0.026*\"報名\" + 0.023*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.013*\"車馬費\" + 0.012*\"資料\" + 0.012*\"人數\" + 0.011*\"訪問\" + 0.011*\"舉辦\" + 0.011*\"時間\"\n",
      "2025-04-19 00:09:54,859 : INFO : topic #3 (0.143): 0.015*\"工作\" + 0.012*\"方式\" + 0.009*\"聯絡人\" + 0.009*\"時間\" + 0.008*\"資訊\" + 0.008*\"砍除\" + 0.008*\"公司\" + 0.008*\"文字\" + 0.008*\"內容\" + 0.008*\"分類\"\n",
      "2025-04-19 00:09:54,860 : INFO : topic #5 (0.143): 0.021*\"工作\" + 0.016*\"方式\" + 0.009*\"時間\" + 0.008*\"依法\" + 0.007*\"通知\" + 0.007*\"面試\" + 0.007*\"應徵\" + 0.007*\"聯絡\" + 0.007*\"內容\" + 0.006*\"每日\"\n",
      "2025-04-19 00:09:54,861 : INFO : topic #4 (0.143): 0.037*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:54,861 : INFO : topic diff=0.650259, rho=0.577350\n",
      "2025-04-19 00:09:54,862 : INFO : PROGRESS: pass 0, at document #8000/16310\n",
      "2025-04-19 00:09:55,211 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:55,215 : INFO : topic #5 (0.143): 0.018*\"工作\" + 0.018*\"公司\" + 0.011*\"面試\" + 0.008*\"工程師\" + 0.008*\"問題\" + 0.008*\"時間\" + 0.008*\"經驗\" + 0.007*\"開發\" + 0.006*\"團隊\" + 0.006*\"技術\"\n",
      "2025-04-19 00:09:55,215 : INFO : topic #1 (0.143): 0.029*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"連結\"\n",
      "2025-04-19 00:09:55,216 : INFO : topic #4 (0.143): 0.037*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:55,216 : INFO : topic #3 (0.143): 0.014*\"工作\" + 0.011*\"方式\" + 0.009*\"公司\" + 0.008*\"時間\" + 0.008*\"聯絡人\" + 0.008*\"資訊\" + 0.007*\"砍除\" + 0.007*\"內容\" + 0.007*\"文字\" + 0.007*\"分類\"\n",
      "2025-04-19 00:09:55,217 : INFO : topic #0 (0.143): 0.030*\"工作\" + 0.013*\"砍除\" + 0.013*\"應徵\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:55,217 : INFO : topic diff=0.846903, rho=0.500000\n",
      "2025-04-19 00:09:55,218 : INFO : PROGRESS: pass 0, at document #10000/16310\n",
      "2025-04-19 00:09:55,528 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:55,531 : INFO : topic #5 (0.143): 0.017*\"公司\" + 0.016*\"工作\" + 0.010*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.007*\"時間\" + 0.007*\"經驗\" + 0.007*\"開發\" + 0.005*\"技術\" + 0.005*\"目前\"\n",
      "2025-04-19 00:09:55,532 : INFO : topic #0 (0.143): 0.030*\"工作\" + 0.013*\"砍除\" + 0.013*\"應徵\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:55,533 : INFO : topic #2 (0.143): 0.038*\"工作\" + 0.012*\"方式\" + 0.010*\"內容\" + 0.010*\"小時\" + 0.009*\"推定\" + 0.008*\"時間\" + 0.008*\"單位\" + 0.008*\"覺得\" + 0.008*\"工資\" + 0.007*\"公司\"\n",
      "2025-04-19 00:09:55,533 : INFO : topic #3 (0.143): 0.013*\"工作\" + 0.010*\"方式\" + 0.009*\"職場\" + 0.008*\"公司\" + 0.008*\"時間\" + 0.008*\"聯絡人\" + 0.008*\"資訊\" + 0.007*\"內容\" + 0.007*\"砍除\" + 0.007*\"文字\"\n",
      "2025-04-19 00:09:55,534 : INFO : topic #6 (0.143): 0.018*\"報名\" + 0.017*\"活動\" + 0.013*\"產品\" + 0.012*\"資料\" + 0.011*\"電話\" + 0.011*\"使用\" + 0.010*\"進行\" + 0.010*\"台北市\" + 0.009*\"時間\" + 0.009*\"研究\"\n",
      "2025-04-19 00:09:55,534 : INFO : topic diff=0.569099, rho=0.447214\n",
      "2025-04-19 00:09:55,535 : INFO : PROGRESS: pass 0, at document #12000/16310\n",
      "2025-04-19 00:09:55,790 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:55,794 : INFO : topic #1 (0.143): 0.029*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"連結\"\n",
      "2025-04-19 00:09:55,795 : INFO : topic #2 (0.143): 0.035*\"工作\" + 0.010*\"方式\" + 0.009*\"內容\" + 0.009*\"小時\" + 0.008*\"時間\" + 0.008*\"單位\" + 0.008*\"覺得\" + 0.007*\"推定\" + 0.007*\"程式\" + 0.007*\"公司\"\n",
      "2025-04-19 00:09:55,795 : INFO : topic #5 (0.143): 0.016*\"公司\" + 0.013*\"工作\" + 0.008*\"面試\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.005*\"時間\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.005*\"經驗\" + 0.005*\"目前\"\n",
      "2025-04-19 00:09:55,796 : INFO : topic #4 (0.143): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:55,796 : INFO : topic #0 (0.143): 0.029*\"工作\" + 0.013*\"砍除\" + 0.013*\"應徵\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:55,797 : INFO : topic diff=0.546661, rho=0.408248\n",
      "2025-04-19 00:09:55,798 : INFO : PROGRESS: pass 0, at document #14000/16310\n",
      "2025-04-19 00:09:56,092 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:56,095 : INFO : topic #2 (0.143): 0.032*\"工作\" + 0.009*\"方式\" + 0.008*\"內容\" + 0.008*\"小時\" + 0.007*\"單位\" + 0.007*\"時間\" + 0.007*\"覺得\" + 0.007*\"公司\" + 0.006*\"程式\" + 0.006*\"推定\"\n",
      "2025-04-19 00:09:56,096 : INFO : topic #1 (0.143): 0.029*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.011*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"連結\"\n",
      "2025-04-19 00:09:56,096 : INFO : topic #5 (0.143): 0.014*\"公司\" + 0.010*\"工作\" + 0.007*\"台灣\" + 0.005*\"面試\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.004*\"時間\" + 0.004*\"員工\" + 0.004*\"目前\"\n",
      "2025-04-19 00:09:56,097 : INFO : topic #4 (0.143): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:56,098 : INFO : topic #0 (0.143): 0.029*\"工作\" + 0.013*\"砍除\" + 0.013*\"應徵\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:56,098 : INFO : topic diff=0.538009, rho=0.377964\n",
      "2025-04-19 00:09:56,099 : INFO : PROGRESS: pass 0, at document #16000/16310\n",
      "2025-04-19 00:09:56,339 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:56,343 : INFO : topic #4 (0.143): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.010*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:56,343 : INFO : topic #1 (0.143): 0.028*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.011*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.010*\"文字\" + 0.010*\"連結\"\n",
      "2025-04-19 00:09:56,344 : INFO : topic #6 (0.143): 0.012*\"三星\" + 0.012*\"今年\" + 0.011*\"研究\" + 0.011*\"模型\" + 0.011*\"蘋果\" + 0.011*\"活動\" + 0.010*\"進行\" + 0.010*\"產品\" + 0.010*\"萬元\" + 0.009*\"報名\"\n",
      "2025-04-19 00:09:56,345 : INFO : topic #0 (0.143): 0.028*\"工作\" + 0.012*\"砍除\" + 0.012*\"應徵\" + 0.012*\"空白\" + 0.011*\"方式\" + 0.010*\"第一項\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:56,345 : INFO : topic #5 (0.143): 0.013*\"公司\" + 0.008*\"工作\" + 0.007*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"員工\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"面試\"\n",
      "2025-04-19 00:09:56,345 : INFO : topic diff=0.484237, rho=0.353553\n",
      "2025-04-19 00:09:56,420 : INFO : -8.712 per-word bound, 419.3 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:09:56,420 : INFO : PROGRESS: pass 0, at document #16310/16310\n",
      "2025-04-19 00:09:56,499 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:56,503 : INFO : topic #0 (0.143): 0.027*\"工作\" + 0.012*\"應徵\" + 0.012*\"砍除\" + 0.011*\"空白\" + 0.011*\"方式\" + 0.010*\"第一項\" + 0.010*\"單位\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"內容\"\n",
      "2025-04-19 00:09:56,503 : INFO : topic #5 (0.143): 0.013*\"公司\" + 0.008*\"美國\" + 0.007*\"台灣\" + 0.007*\"工作\" + 0.006*\"技術\" + 0.005*\"晶片\" + 0.005*\"員工\" + 0.004*\"科技\" + 0.004*\"台積電\" + 0.004*\"問題\"\n",
      "2025-04-19 00:09:56,504 : INFO : topic #1 (0.143): 0.028*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.010*\"連結\"\n",
      "2025-04-19 00:09:56,504 : INFO : topic #2 (0.143): 0.027*\"工作\" + 0.009*\"預期\" + 0.008*\"小時\" + 0.007*\"營收\" + 0.007*\"方式\" + 0.007*\"時間\" + 0.007*\"內容\" + 0.007*\"覺得\" + 0.007*\"公司\" + 0.006*\"單位\"\n",
      "2025-04-19 00:09:56,505 : INFO : topic #6 (0.143): 0.012*\"三星\" + 0.012*\"今年\" + 0.011*\"研究\" + 0.011*\"蘋果\" + 0.010*\"進行\" + 0.010*\"萬元\" + 0.010*\"產品\" + 0.009*\"模型\" + 0.009*\"活動\" + 0.009*\"大陸\"\n",
      "2025-04-19 00:09:56,505 : INFO : topic diff=0.405972, rho=0.333333\n",
      "2025-04-19 00:09:56,506 : INFO : PROGRESS: pass 1, at document #2000/16310\n",
      "2025-04-19 00:09:57,209 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:57,212 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"文字\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:09:57,213 : INFO : topic #6 (0.143): 0.019*\"報名\" + 0.018*\"活動\" + 0.011*\"電話\" + 0.011*\"進行\" + 0.010*\"研究\" + 0.010*\"資料\" + 0.010*\"台北市\" + 0.010*\"舉辦\" + 0.009*\"參與\" + 0.008*\"車馬費\"\n",
      "2025-04-19 00:09:57,213 : INFO : topic #4 (0.143): 0.038*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"方式\" + 0.011*\"砍除\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:09:57,214 : INFO : topic #0 (0.143): 0.031*\"工作\" + 0.013*\"應徵\" + 0.012*\"方式\" + 0.011*\"砍除\" + 0.010*\"空白\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.010*\"第一項\" + 0.010*\"單位\"\n",
      "2025-04-19 00:09:57,214 : INFO : topic #5 (0.143): 0.013*\"公司\" + 0.008*\"美國\" + 0.007*\"台灣\" + 0.007*\"工作\" + 0.006*\"技術\" + 0.005*\"晶片\" + 0.005*\"員工\" + 0.004*\"科技\" + 0.004*\"台積電\" + 0.004*\"問題\"\n",
      "2025-04-19 00:09:57,214 : INFO : topic diff=1.061438, rho=0.313805\n",
      "2025-04-19 00:09:57,215 : INFO : PROGRESS: pass 1, at document #4000/16310\n",
      "2025-04-19 00:09:57,897 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:57,901 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"文字\" + 0.011*\"未註明\"\n",
      "2025-04-19 00:09:57,901 : INFO : topic #6 (0.143): 0.024*\"報名\" + 0.022*\"活動\" + 0.016*\"電話\" + 0.012*\"台北市\" + 0.011*\"資料\" + 0.011*\"舉辦\" + 0.011*\"進行\" + 0.011*\"車馬費\" + 0.010*\"人數\" + 0.010*\"參與\"\n",
      "2025-04-19 00:09:57,902 : INFO : topic #0 (0.143): 0.032*\"工作\" + 0.012*\"方式\" + 0.012*\"應徵\" + 0.010*\"砍除\" + 0.010*\"推定\" + 0.010*\"空白\" + 0.010*\"內容\" + 0.010*\"情形\" + 0.010*\"第一項\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:57,902 : INFO : topic #5 (0.143): 0.013*\"公司\" + 0.007*\"美國\" + 0.007*\"台灣\" + 0.007*\"工作\" + 0.006*\"技術\" + 0.005*\"晶片\" + 0.005*\"員工\" + 0.004*\"科技\" + 0.004*\"台積電\" + 0.004*\"問題\"\n",
      "2025-04-19 00:09:57,902 : INFO : topic #3 (0.143): 0.053*\"半導體\" + 0.029*\"製程\" + 0.023*\"川普\" + 0.021*\"表示\" + 0.012*\"投資\" + 0.012*\"中國\" + 0.011*\"魏哲家\" + 0.010*\"研發\" + 0.009*\"奈米\" + 0.006*\"晶圓廠\"\n",
      "2025-04-19 00:09:57,903 : INFO : topic diff=0.418343, rho=0.313805\n",
      "2025-04-19 00:09:57,903 : INFO : PROGRESS: pass 1, at document #6000/16310\n",
      "2025-04-19 00:09:58,435 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:58,438 : INFO : topic #0 (0.143): 0.032*\"工作\" + 0.012*\"方式\" + 0.012*\"應徵\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"空白\" + 0.010*\"內容\" + 0.010*\"推定\" + 0.010*\"第一項\" + 0.010*\"文字\"\n",
      "2025-04-19 00:09:58,439 : INFO : topic #5 (0.143): 0.014*\"公司\" + 0.008*\"工作\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"面試\" + 0.004*\"晶片\"\n",
      "2025-04-19 00:09:58,439 : INFO : topic #6 (0.143): 0.028*\"報名\" + 0.025*\"活動\" + 0.018*\"電話\" + 0.015*\"台北市\" + 0.012*\"車馬費\" + 0.012*\"舉辦\" + 0.012*\"資料\" + 0.011*\"人數\" + 0.011*\"訪問\" + 0.011*\"進行\"\n",
      "2025-04-19 00:09:58,440 : INFO : topic #2 (0.143): 0.044*\"工作\" + 0.020*\"方式\" + 0.016*\"時間\" + 0.014*\"小時\" + 0.011*\"每日\" + 0.010*\"內容\" + 0.010*\"休息\" + 0.010*\"工資\" + 0.010*\"依法\" + 0.008*\"面試\"\n",
      "2025-04-19 00:09:58,440 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"內容\" + 0.011*\"未註明\"\n",
      "2025-04-19 00:09:58,441 : INFO : topic diff=0.296699, rho=0.313805\n",
      "2025-04-19 00:09:58,441 : INFO : PROGRESS: pass 1, at document #8000/16310\n",
      "2025-04-19 00:09:58,745 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:58,750 : INFO : topic #4 (0.143): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 00:09:58,752 : INFO : topic #2 (0.143): 0.048*\"工作\" + 0.021*\"方式\" + 0.018*\"時間\" + 0.016*\"小時\" + 0.011*\"每日\" + 0.011*\"內容\" + 0.009*\"休息\" + 0.009*\"面試\" + 0.008*\"工資\" + 0.008*\"依法\"\n",
      "2025-04-19 00:09:58,752 : INFO : topic #6 (0.143): 0.026*\"報名\" + 0.024*\"活動\" + 0.017*\"電話\" + 0.014*\"台北市\" + 0.012*\"資料\" + 0.012*\"舉辦\" + 0.011*\"車馬費\" + 0.011*\"進行\" + 0.011*\"人數\" + 0.010*\"參加\"\n",
      "2025-04-19 00:09:58,753 : INFO : topic #0 (0.143): 0.032*\"工作\" + 0.012*\"應徵\" + 0.012*\"方式\" + 0.010*\"內容\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"空白\" + 0.010*\"推定\" + 0.010*\"第一項\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:58,755 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"內容\" + 0.011*\"未註明\"\n",
      "2025-04-19 00:09:58,756 : INFO : topic diff=0.333959, rho=0.313805\n",
      "2025-04-19 00:09:58,756 : INFO : PROGRESS: pass 1, at document #10000/16310\n",
      "2025-04-19 00:09:58,993 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:58,996 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"內容\" + 0.011*\"未註明\"\n",
      "2025-04-19 00:09:58,997 : INFO : topic #6 (0.143): 0.026*\"報名\" + 0.024*\"活動\" + 0.016*\"電話\" + 0.013*\"台北市\" + 0.012*\"資料\" + 0.012*\"研究\" + 0.011*\"舉辦\" + 0.011*\"進行\" + 0.010*\"參加\" + 0.010*\"參與\"\n",
      "2025-04-19 00:09:58,997 : INFO : topic #2 (0.143): 0.050*\"工作\" + 0.021*\"方式\" + 0.019*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"每日\" + 0.009*\"面試\" + 0.008*\"工時\" + 0.008*\"休息\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:09:58,998 : INFO : topic #5 (0.143): 0.016*\"公司\" + 0.010*\"工作\" + 0.008*\"面試\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"開發\" + 0.005*\"技術\" + 0.005*\"經驗\" + 0.005*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 00:09:58,998 : INFO : topic #0 (0.143): 0.032*\"工作\" + 0.012*\"應徵\" + 0.012*\"方式\" + 0.010*\"內容\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"空白\" + 0.010*\"推定\" + 0.010*\"第一項\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:58,999 : INFO : topic diff=0.289564, rho=0.313805\n",
      "2025-04-19 00:09:58,999 : INFO : PROGRESS: pass 1, at document #12000/16310\n",
      "2025-04-19 00:09:59,248 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:59,252 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"內容\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:09:59,252 : INFO : topic #0 (0.143): 0.032*\"工作\" + 0.012*\"應徵\" + 0.012*\"方式\" + 0.010*\"內容\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"空白\" + 0.010*\"推定\" + 0.010*\"第一項\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:59,253 : INFO : topic #4 (0.143): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 00:09:59,253 : INFO : topic #2 (0.143): 0.049*\"工作\" + 0.020*\"方式\" + 0.019*\"時間\" + 0.016*\"小時\" + 0.011*\"內容\" + 0.010*\"每日\" + 0.008*\"工時\" + 0.008*\"面試\" + 0.007*\"休息\" + 0.007*\"聯絡\"\n",
      "2025-04-19 00:09:59,254 : INFO : topic #5 (0.143): 0.015*\"公司\" + 0.009*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.005*\"台灣\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 00:09:59,254 : INFO : topic diff=0.392427, rho=0.313805\n",
      "2025-04-19 00:09:59,255 : INFO : PROGRESS: pass 1, at document #14000/16310\n",
      "2025-04-19 00:09:59,495 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:59,498 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"內容\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:09:59,499 : INFO : topic #0 (0.143): 0.031*\"工作\" + 0.012*\"應徵\" + 0.011*\"方式\" + 0.010*\"內容\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"空白\" + 0.010*\"推定\" + 0.010*\"第一項\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:09:59,499 : INFO : topic #4 (0.143): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 00:09:59,500 : INFO : topic #3 (0.143): 0.042*\"半導體\" + 0.028*\"表示\" + 0.024*\"中國\" + 0.023*\"晶片\" + 0.019*\"製程\" + 0.013*\"輝達\" + 0.012*\"投資\" + 0.010*\"先進\" + 0.010*\"研發\" + 0.008*\"奈米\"\n",
      "2025-04-19 00:09:59,500 : INFO : topic #6 (0.143): 0.021*\"活動\" + 0.020*\"報名\" + 0.014*\"研究\" + 0.011*\"進行\" + 0.011*\"蘋果\" + 0.011*\"電話\" + 0.010*\"資料\" + 0.010*\"問卷\" + 0.010*\"三星\" + 0.009*\"參與\"\n",
      "2025-04-19 00:09:59,500 : INFO : topic diff=0.370313, rho=0.313805\n",
      "2025-04-19 00:09:59,501 : INFO : PROGRESS: pass 1, at document #16000/16310\n",
      "2025-04-19 00:09:59,745 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:59,749 : INFO : topic #0 (0.143): 0.031*\"工作\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"內容\" + 0.010*\"情形\" + 0.010*\"空白\" + 0.010*\"砍除\" + 0.009*\"第一項\" + 0.009*\"推定\" + 0.009*\"資訊\"\n",
      "2025-04-19 00:09:59,750 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.015*\"方式\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"單位\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:09:59,750 : INFO : topic #5 (0.143): 0.014*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.005*\"技術\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"面試\" + 0.004*\"科技\"\n",
      "2025-04-19 00:09:59,751 : INFO : topic #6 (0.143): 0.018*\"活動\" + 0.017*\"報名\" + 0.015*\"研究\" + 0.013*\"三星\" + 0.013*\"蘋果\" + 0.011*\"進行\" + 0.009*\"資料\" + 0.009*\"問卷\" + 0.009*\"參與\" + 0.008*\"使用\"\n",
      "2025-04-19 00:09:59,751 : INFO : topic #4 (0.143): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 00:09:59,751 : INFO : topic diff=0.313321, rho=0.313805\n",
      "2025-04-19 00:09:59,839 : INFO : -8.438 per-word bound, 346.8 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:09:59,840 : INFO : PROGRESS: pass 1, at document #16310/16310\n",
      "2025-04-19 00:09:59,875 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:09:59,878 : INFO : topic #4 (0.143): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 00:09:59,878 : INFO : topic #0 (0.143): 0.030*\"工作\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"內容\" + 0.009*\"情形\" + 0.009*\"空白\" + 0.009*\"砍除\" + 0.009*\"資訊\" + 0.009*\"第一項\" + 0.009*\"推定\"\n",
      "2025-04-19 00:09:59,879 : INFO : topic #6 (0.143): 0.016*\"活動\" + 0.015*\"研究\" + 0.014*\"報名\" + 0.013*\"蘋果\" + 0.013*\"三星\" + 0.011*\"問卷\" + 0.010*\"進行\" + 0.010*\"機器人\" + 0.009*\"華為\" + 0.009*\"參與\"\n",
      "2025-04-19 00:09:59,880 : INFO : topic #1 (0.143): 0.029*\"工作\" + 0.015*\"方式\" + 0.012*\"推定\" + 0.012*\"單位\" + 0.011*\"情形\" + 0.011*\"砍除\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:09:59,880 : INFO : topic #3 (0.143): 0.031*\"晶片\" + 0.030*\"半導體\" + 0.024*\"表示\" + 0.023*\"中國\" + 0.022*\"投資\" + 0.016*\"製程\" + 0.014*\"川普\" + 0.013*\"輝達\" + 0.013*\"先進\" + 0.013*\"美國\"\n",
      "2025-04-19 00:09:59,881 : INFO : topic diff=0.295690, rho=0.313805\n",
      "2025-04-19 00:09:59,881 : INFO : PROGRESS: pass 2, at document #2000/16310\n",
      "2025-04-19 00:10:00,566 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:00,569 : INFO : topic #3 (0.143): 0.030*\"晶片\" + 0.029*\"半導體\" + 0.023*\"表示\" + 0.023*\"中國\" + 0.021*\"投資\" + 0.016*\"製程\" + 0.014*\"川普\" + 0.013*\"輝達\" + 0.013*\"先進\" + 0.013*\"美國\"\n",
      "2025-04-19 00:10:00,570 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.012*\"單位\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"砍除\" + 0.011*\"內容\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:10:00,571 : INFO : topic #0 (0.143): 0.031*\"工作\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"情形\" + 0.010*\"資訊\" + 0.009*\"空白\" + 0.009*\"第一項\" + 0.009*\"徵才\"\n",
      "2025-04-19 00:10:00,571 : INFO : topic #4 (0.143): 0.034*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"國定假日\"\n",
      "2025-04-19 00:10:00,572 : INFO : topic #5 (0.143): 0.014*\"公司\" + 0.006*\"台灣\" + 0.006*\"工作\" + 0.006*\"美國\" + 0.006*\"技術\" + 0.005*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"台積\"\n",
      "2025-04-19 00:10:00,572 : INFO : topic diff=0.833026, rho=0.299409\n",
      "2025-04-19 00:10:00,572 : INFO : PROGRESS: pass 2, at document #4000/16310\n",
      "2025-04-19 00:10:01,208 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:01,211 : INFO : topic #4 (0.143): 0.034*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:01,212 : INFO : topic #0 (0.143): 0.032*\"工作\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"情形\" + 0.010*\"空白\" + 0.010*\"第一項\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.009*\"徵才\"\n",
      "2025-04-19 00:10:01,213 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"未註明\" + 0.011*\"砍除\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:01,213 : INFO : topic #2 (0.143): 0.047*\"工作\" + 0.022*\"方式\" + 0.019*\"時間\" + 0.016*\"小時\" + 0.011*\"每日\" + 0.011*\"內容\" + 0.010*\"休息\" + 0.009*\"工資\" + 0.009*\"面試\" + 0.009*\"地點\"\n",
      "2025-04-19 00:10:01,214 : INFO : topic #6 (0.143): 0.027*\"報名\" + 0.025*\"活動\" + 0.018*\"電話\" + 0.014*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"車馬費\" + 0.011*\"人數\" + 0.011*\"資料\" + 0.011*\"參與\" + 0.011*\"進行\"\n",
      "2025-04-19 00:10:01,214 : INFO : topic diff=0.359124, rho=0.299409\n",
      "2025-04-19 00:10:01,214 : INFO : PROGRESS: pass 2, at document #6000/16310\n",
      "2025-04-19 00:10:01,770 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:01,773 : INFO : topic #3 (0.143): 0.027*\"晶片\" + 0.026*\"半導體\" + 0.022*\"中國\" + 0.021*\"表示\" + 0.019*\"投資\" + 0.014*\"製程\" + 0.012*\"川普\" + 0.011*\"輝達\" + 0.011*\"先進\" + 0.011*\"美國\"\n",
      "2025-04-19 00:10:01,774 : INFO : topic #5 (0.143): 0.015*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"技術\" + 0.005*\"美國\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"面試\"\n",
      "2025-04-19 00:10:01,774 : INFO : topic #4 (0.143): 0.034*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:01,775 : INFO : topic #6 (0.143): 0.030*\"報名\" + 0.027*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.013*\"車馬費\" + 0.013*\"舉辦\" + 0.012*\"人數\" + 0.012*\"訪問\" + 0.012*\"資料\" + 0.011*\"參與\"\n",
      "2025-04-19 00:10:01,775 : INFO : topic #2 (0.143): 0.048*\"工作\" + 0.024*\"方式\" + 0.019*\"時間\" + 0.016*\"小時\" + 0.013*\"每日\" + 0.011*\"內容\" + 0.011*\"休息\" + 0.010*\"依法\" + 0.010*\"工資\" + 0.009*\"地點\"\n",
      "2025-04-19 00:10:01,776 : INFO : topic diff=0.230380, rho=0.299409\n",
      "2025-04-19 00:10:01,776 : INFO : PROGRESS: pass 2, at document #8000/16310\n",
      "2025-04-19 00:10:02,016 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:02,019 : INFO : topic #5 (0.143): 0.017*\"公司\" + 0.009*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.005*\"技術\" + 0.005*\"開發\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"經驗\"\n",
      "2025-04-19 00:10:02,020 : INFO : topic #2 (0.143): 0.052*\"工作\" + 0.024*\"方式\" + 0.021*\"時間\" + 0.017*\"小時\" + 0.012*\"內容\" + 0.012*\"每日\" + 0.010*\"面試\" + 0.009*\"休息\" + 0.009*\"工時\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:10:02,021 : INFO : topic #6 (0.143): 0.029*\"報名\" + 0.027*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"車馬費\" + 0.012*\"資料\" + 0.012*\"人數\" + 0.011*\"訪問\" + 0.011*\"參與\"\n",
      "2025-04-19 00:10:02,021 : INFO : topic #3 (0.143): 0.027*\"半導體\" + 0.026*\"晶片\" + 0.022*\"中國\" + 0.021*\"表示\" + 0.018*\"投資\" + 0.014*\"製程\" + 0.012*\"川普\" + 0.011*\"輝達\" + 0.011*\"先進\" + 0.011*\"美國\"\n",
      "2025-04-19 00:10:02,022 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"未註明\" + 0.011*\"砍除\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:02,022 : INFO : topic diff=0.305917, rho=0.299409\n",
      "2025-04-19 00:10:02,022 : INFO : PROGRESS: pass 2, at document #10000/16310\n",
      "2025-04-19 00:10:02,284 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:02,287 : INFO : topic #3 (0.143): 0.029*\"半導體\" + 0.025*\"晶片\" + 0.022*\"中國\" + 0.021*\"表示\" + 0.018*\"投資\" + 0.013*\"製程\" + 0.011*\"川普\" + 0.011*\"先進\" + 0.010*\"輝達\" + 0.010*\"美國\"\n",
      "2025-04-19 00:10:02,288 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"未註明\" + 0.011*\"砍除\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:02,289 : INFO : topic #4 (0.143): 0.034*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:02,289 : INFO : topic #5 (0.143): 0.017*\"公司\" + 0.009*\"工作\" + 0.008*\"面試\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"開發\" + 0.005*\"技術\" + 0.005*\"目前\" + 0.005*\"比較\" + 0.004*\"經驗\"\n",
      "2025-04-19 00:10:02,290 : INFO : topic #6 (0.143): 0.029*\"報名\" + 0.027*\"活動\" + 0.017*\"電話\" + 0.014*\"台北市\" + 0.012*\"研究\" + 0.012*\"舉辦\" + 0.012*\"資料\" + 0.011*\"車馬費\" + 0.011*\"參與\" + 0.011*\"參加\"\n",
      "2025-04-19 00:10:02,290 : INFO : topic diff=0.273938, rho=0.299409\n",
      "2025-04-19 00:10:02,290 : INFO : PROGRESS: pass 2, at document #12000/16310\n",
      "2025-04-19 00:10:02,492 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:02,495 : INFO : topic #3 (0.143): 0.029*\"晶片\" + 0.029*\"半導體\" + 0.020*\"表示\" + 0.018*\"中國\" + 0.015*\"製程\" + 0.014*\"台積電\" + 0.013*\"投資\" + 0.012*\"美國\" + 0.012*\"台灣\" + 0.010*\"英特爾\"\n",
      "2025-04-19 00:10:02,496 : INFO : topic #2 (0.143): 0.052*\"工作\" + 0.023*\"方式\" + 0.022*\"時間\" + 0.018*\"小時\" + 0.011*\"內容\" + 0.010*\"工時\" + 0.010*\"每日\" + 0.009*\"面試\" + 0.008*\"地點\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:10:02,496 : INFO : topic #6 (0.143): 0.028*\"報名\" + 0.027*\"活動\" + 0.015*\"電話\" + 0.014*\"研究\" + 0.012*\"舉辦\" + 0.012*\"台北市\" + 0.011*\"資料\" + 0.011*\"參加\" + 0.011*\"進行\" + 0.011*\"參與\"\n",
      "2025-04-19 00:10:02,497 : INFO : topic #0 (0.143): 0.031*\"工作\" + 0.011*\"應徵\" + 0.010*\"方式\" + 0.010*\"徵才\" + 0.010*\"情形\" + 0.010*\"資訊\" + 0.010*\"內容\" + 0.010*\"第一項\" + 0.010*\"空白\" + 0.009*\"文字\"\n",
      "2025-04-19 00:10:02,497 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"未註明\" + 0.010*\"砍除\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:02,498 : INFO : topic diff=0.350334, rho=0.299409\n",
      "2025-04-19 00:10:02,498 : INFO : PROGRESS: pass 2, at document #14000/16310\n",
      "2025-04-19 00:10:02,739 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:02,742 : INFO : topic #4 (0.143): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:02,743 : INFO : topic #3 (0.143): 0.028*\"晶片\" + 0.025*\"半導體\" + 0.021*\"表示\" + 0.020*\"中國\" + 0.017*\"台積電\" + 0.017*\"台灣\" + 0.015*\"美國\" + 0.013*\"英特爾\" + 0.011*\"投資\" + 0.011*\"製程\"\n",
      "2025-04-19 00:10:02,743 : INFO : topic #2 (0.143): 0.050*\"工作\" + 0.021*\"方式\" + 0.020*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.010*\"工時\" + 0.009*\"每日\" + 0.009*\"地點\" + 0.008*\"面試\" + 0.007*\"聯絡\"\n",
      "2025-04-19 00:10:02,744 : INFO : topic #0 (0.143): 0.031*\"工作\" + 0.011*\"應徵\" + 0.010*\"徵才\" + 0.010*\"情形\" + 0.010*\"方式\" + 0.010*\"資訊\" + 0.010*\"內容\" + 0.009*\"第一項\" + 0.009*\"空白\" + 0.009*\"文字\"\n",
      "2025-04-19 00:10:02,744 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.010*\"未註明\" + 0.010*\"砍除\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:02,745 : INFO : topic diff=0.322385, rho=0.299409\n",
      "2025-04-19 00:10:02,745 : INFO : PROGRESS: pass 2, at document #16000/16310\n",
      "2025-04-19 00:10:02,941 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:02,944 : INFO : topic #2 (0.143): 0.049*\"工作\" + 0.019*\"時間\" + 0.018*\"方式\" + 0.016*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.009*\"地點\" + 0.008*\"面試\" + 0.008*\"每日\" + 0.007*\"以上\"\n",
      "2025-04-19 00:10:02,944 : INFO : topic #4 (0.143): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:02,945 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.010*\"未註明\" + 0.010*\"砍除\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:02,945 : INFO : topic #5 (0.143): 0.014*\"公司\" + 0.007*\"工作\" + 0.005*\"技術\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.005*\"台灣\" + 0.004*\"員工\" + 0.004*\"面試\" + 0.004*\"目前\" + 0.004*\"科技\"\n",
      "2025-04-19 00:10:02,946 : INFO : topic #0 (0.143): 0.030*\"工作\" + 0.010*\"應徵\" + 0.010*\"情形\" + 0.010*\"徵才\" + 0.010*\"方式\" + 0.010*\"資訊\" + 0.009*\"內容\" + 0.009*\"空白\" + 0.009*\"第一項\" + 0.009*\"文字\"\n",
      "2025-04-19 00:10:02,946 : INFO : topic diff=0.270860, rho=0.299409\n",
      "2025-04-19 00:10:03,038 : INFO : -8.378 per-word bound, 332.7 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:10:03,038 : INFO : PROGRESS: pass 2, at document #16310/16310\n",
      "2025-04-19 00:10:03,070 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:03,073 : INFO : topic #0 (0.143): 0.029*\"工作\" + 0.011*\"徵才\" + 0.010*\"應徵\" + 0.010*\"情形\" + 0.010*\"方式\" + 0.010*\"資訊\" + 0.009*\"內容\" + 0.009*\"空白\" + 0.009*\"第一項\" + 0.009*\"文字\"\n",
      "2025-04-19 00:10:03,073 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"文字\" + 0.010*\"聯絡\" + 0.010*\"未註明\" + 0.010*\"砍除\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:03,074 : INFO : topic #5 (0.143): 0.015*\"公司\" + 0.006*\"工作\" + 0.006*\"技術\" + 0.005*\"員工\" + 0.004*\"問題\" + 0.004*\"台灣\" + 0.004*\"科技\" + 0.004*\"工程師\" + 0.004*\"台積\" + 0.004*\"面試\"\n",
      "2025-04-19 00:10:03,074 : INFO : topic #4 (0.143): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:10:03,075 : INFO : topic #6 (0.143): 0.019*\"活動\" + 0.017*\"報名\" + 0.016*\"研究\" + 0.012*\"問卷\" + 0.010*\"華為\" + 0.010*\"進行\" + 0.010*\"機器人\" + 0.009*\"參與\" + 0.009*\"蘋果\" + 0.008*\"女性\"\n",
      "2025-04-19 00:10:03,075 : INFO : topic diff=0.259379, rho=0.299409\n",
      "2025-04-19 00:10:03,076 : INFO : PROGRESS: pass 3, at document #2000/16310\n",
      "2025-04-19 00:10:03,712 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:03,716 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.014*\"方式\" + 0.014*\"推定\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.011*\"聯絡\" + 0.010*\"內容\" + 0.010*\"砍除\"\n",
      "2025-04-19 00:10:03,717 : INFO : topic #4 (0.143): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:03,717 : INFO : topic #2 (0.143): 0.049*\"工作\" + 0.021*\"方式\" + 0.020*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.010*\"每日\" + 0.010*\"工時\" + 0.009*\"地點\" + 0.009*\"休息\" + 0.008*\"面試\"\n",
      "2025-04-19 00:10:03,718 : INFO : topic #0 (0.143): 0.030*\"工作\" + 0.010*\"應徵\" + 0.010*\"徵才\" + 0.010*\"情形\" + 0.010*\"資訊\" + 0.010*\"方式\" + 0.009*\"內容\" + 0.009*\"第一項\" + 0.009*\"空白\" + 0.009*\"文字\"\n",
      "2025-04-19 00:10:03,718 : INFO : topic #3 (0.143): 0.029*\"美國\" + 0.029*\"晶片\" + 0.021*\"台積電\" + 0.020*\"中國\" + 0.020*\"半導體\" + 0.019*\"台灣\" + 0.019*\"表示\" + 0.017*\"投資\" + 0.015*\"英特爾\" + 0.011*\"輝達\"\n",
      "2025-04-19 00:10:03,718 : INFO : topic diff=0.765701, rho=0.286829\n",
      "2025-04-19 00:10:03,719 : INFO : PROGRESS: pass 3, at document #4000/16310\n",
      "2025-04-19 00:10:04,362 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:04,365 : INFO : topic #5 (0.143): 0.015*\"公司\" + 0.006*\"工作\" + 0.006*\"技術\" + 0.005*\"員工\" + 0.004*\"問題\" + 0.004*\"台灣\" + 0.004*\"科技\" + 0.004*\"工程師\" + 0.004*\"台積\" + 0.004*\"面試\"\n",
      "2025-04-19 00:10:04,366 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.014*\"推定\" + 0.014*\"方式\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.010*\"聯絡\" + 0.010*\"砍除\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:04,367 : INFO : topic #2 (0.143): 0.049*\"工作\" + 0.023*\"方式\" + 0.019*\"時間\" + 0.017*\"小時\" + 0.012*\"每日\" + 0.011*\"內容\" + 0.010*\"休息\" + 0.009*\"工時\" + 0.009*\"地點\" + 0.009*\"依法\"\n",
      "2025-04-19 00:10:04,367 : INFO : topic #4 (0.143): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.014*\"砍除\" + 0.013*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:04,368 : INFO : topic #0 (0.143): 0.030*\"工作\" + 0.010*\"情形\" + 0.010*\"資訊\" + 0.010*\"應徵\" + 0.010*\"徵才\" + 0.010*\"方式\" + 0.009*\"內容\" + 0.009*\"第一項\" + 0.009*\"空白\" + 0.009*\"文字\"\n",
      "2025-04-19 00:10:04,368 : INFO : topic diff=0.338462, rho=0.286829\n",
      "2025-04-19 00:10:04,369 : INFO : PROGRESS: pass 3, at document #6000/16310\n",
      "2025-04-19 00:10:04,865 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:04,869 : INFO : topic #0 (0.143): 0.030*\"工作\" + 0.010*\"情形\" + 0.010*\"資訊\" + 0.010*\"應徵\" + 0.010*\"內容\" + 0.010*\"徵才\" + 0.009*\"方式\" + 0.009*\"第一項\" + 0.009*\"文字\" + 0.009*\"空白\"\n",
      "2025-04-19 00:10:04,869 : INFO : topic #3 (0.143): 0.027*\"美國\" + 0.027*\"晶片\" + 0.019*\"中國\" + 0.019*\"台積電\" + 0.018*\"半導體\" + 0.018*\"表示\" + 0.018*\"台灣\" + 0.016*\"投資\" + 0.014*\"英特爾\" + 0.010*\"輝達\"\n",
      "2025-04-19 00:10:04,870 : INFO : topic #5 (0.143): 0.015*\"公司\" + 0.007*\"工作\" + 0.005*\"技術\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"員工\" + 0.004*\"台灣\" + 0.004*\"面試\" + 0.004*\"科技\" + 0.004*\"目前\"\n",
      "2025-04-19 00:10:04,870 : INFO : topic #2 (0.143): 0.048*\"工作\" + 0.024*\"方式\" + 0.019*\"時間\" + 0.017*\"小時\" + 0.013*\"每日\" + 0.011*\"內容\" + 0.011*\"休息\" + 0.010*\"依法\" + 0.010*\"工資\" + 0.009*\"地點\"\n",
      "2025-04-19 00:10:04,871 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.014*\"推定\" + 0.014*\"方式\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.010*\"聯絡\" + 0.010*\"內容\" + 0.010*\"砍除\"\n",
      "2025-04-19 00:10:04,871 : INFO : topic diff=0.209269, rho=0.286829\n",
      "2025-04-19 00:10:04,871 : INFO : PROGRESS: pass 3, at document #8000/16310\n",
      "2025-04-19 00:10:05,131 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:05,134 : INFO : topic #4 (0.143): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.014*\"砍除\" + 0.013*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"資訊\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:10:05,134 : INFO : topic #0 (0.143): 0.030*\"工作\" + 0.010*\"徵才\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.010*\"應徵\" + 0.010*\"內容\" + 0.009*\"方式\" + 0.009*\"文字\" + 0.009*\"第一項\" + 0.009*\"空白\"\n",
      "2025-04-19 00:10:05,135 : INFO : topic #5 (0.143): 0.017*\"公司\" + 0.008*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.005*\"技術\" + 0.005*\"開發\" + 0.004*\"目前\" + 0.004*\"比較\" + 0.004*\"覺得\"\n",
      "2025-04-19 00:10:05,136 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.014*\"推定\" + 0.014*\"方式\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.010*\"聯絡\" + 0.010*\"內容\" + 0.010*\"砍除\"\n",
      "2025-04-19 00:10:05,136 : INFO : topic #6 (0.143): 0.030*\"報名\" + 0.027*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.012*\"人數\" + 0.012*\"資料\" + 0.012*\"訪問\" + 0.011*\"參加\"\n",
      "2025-04-19 00:10:05,137 : INFO : topic diff=0.287448, rho=0.286829\n",
      "2025-04-19 00:10:05,137 : INFO : PROGRESS: pass 3, at document #10000/16310\n",
      "2025-04-19 00:10:05,341 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:05,344 : INFO : topic #4 (0.143): 0.034*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.014*\"砍除\" + 0.013*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"資訊\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:10:05,344 : INFO : topic #6 (0.143): 0.030*\"報名\" + 0.027*\"活動\" + 0.018*\"電話\" + 0.014*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"研究\" + 0.012*\"資料\" + 0.012*\"車馬費\" + 0.011*\"人數\" + 0.011*\"參加\"\n",
      "2025-04-19 00:10:05,345 : INFO : topic #2 (0.143): 0.052*\"工作\" + 0.023*\"方式\" + 0.022*\"時間\" + 0.018*\"小時\" + 0.012*\"內容\" + 0.011*\"每日\" + 0.011*\"工時\" + 0.009*\"面試\" + 0.008*\"聯絡\" + 0.008*\"休息\"\n",
      "2025-04-19 00:10:05,345 : INFO : topic #5 (0.143): 0.017*\"公司\" + 0.009*\"工作\" + 0.008*\"面試\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"開發\" + 0.005*\"技術\" + 0.005*\"目前\" + 0.005*\"比較\" + 0.004*\"覺得\"\n",
      "2025-04-19 00:10:05,346 : INFO : topic #3 (0.143): 0.026*\"美國\" + 0.025*\"晶片\" + 0.020*\"中國\" + 0.020*\"半導體\" + 0.019*\"台灣\" + 0.018*\"表示\" + 0.018*\"台積電\" + 0.016*\"投資\" + 0.013*\"英特爾\" + 0.010*\"製程\"\n",
      "2025-04-19 00:10:05,346 : INFO : topic diff=0.259690, rho=0.286829\n",
      "2025-04-19 00:10:05,346 : INFO : PROGRESS: pass 3, at document #12000/16310\n",
      "2025-04-19 00:10:05,551 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:05,554 : INFO : topic #2 (0.143): 0.052*\"工作\" + 0.023*\"方式\" + 0.021*\"時間\" + 0.018*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"每日\" + 0.009*\"面試\" + 0.008*\"地點\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:10:05,555 : INFO : topic #3 (0.143): 0.026*\"晶片\" + 0.022*\"半導體\" + 0.020*\"台灣\" + 0.019*\"美國\" + 0.018*\"台積電\" + 0.017*\"表示\" + 0.016*\"中國\" + 0.012*\"投資\" + 0.011*\"製程\" + 0.011*\"產業\"\n",
      "2025-04-19 00:10:05,555 : INFO : topic #0 (0.143): 0.029*\"工作\" + 0.010*\"徵才\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.010*\"應徵\" + 0.009*\"內容\" + 0.009*\"方式\" + 0.009*\"文字\" + 0.009*\"第一項\" + 0.009*\"空白\"\n",
      "2025-04-19 00:10:05,556 : INFO : topic #4 (0.143): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.014*\"砍除\" + 0.013*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"資訊\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:10:05,556 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.014*\"推定\" + 0.014*\"方式\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.010*\"聯絡\" + 0.010*\"內容\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:10:05,557 : INFO : topic diff=0.312646, rho=0.286829\n",
      "2025-04-19 00:10:05,557 : INFO : PROGRESS: pass 3, at document #14000/16310\n",
      "2025-04-19 00:10:05,750 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:05,753 : INFO : topic #4 (0.143): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:10:05,754 : INFO : topic #0 (0.143): 0.028*\"工作\" + 0.011*\"徵才\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.010*\"應徵\" + 0.009*\"內容\" + 0.009*\"方式\" + 0.009*\"文字\" + 0.009*\"第一項\" + 0.009*\"空白\"\n",
      "2025-04-19 00:10:05,754 : INFO : topic #6 (0.143): 0.026*\"活動\" + 0.025*\"報名\" + 0.016*\"研究\" + 0.014*\"電話\" + 0.012*\"問卷\" + 0.011*\"舉辦\" + 0.011*\"參與\" + 0.011*\"進行\" + 0.010*\"參加\" + 0.010*\"資料\"\n",
      "2025-04-19 00:10:05,755 : INFO : topic #5 (0.143): 0.015*\"公司\" + 0.008*\"工作\" + 0.005*\"問題\" + 0.005*\"面試\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.004*\"目前\" + 0.004*\"開發\" + 0.004*\"員工\" + 0.004*\"比較\"\n",
      "2025-04-19 00:10:05,755 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.014*\"推定\" + 0.014*\"方式\" + 0.012*\"情形\" + 0.012*\"單位\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.010*\"聯絡\" + 0.010*\"內容\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:10:05,756 : INFO : topic diff=0.295359, rho=0.286829\n",
      "2025-04-19 00:10:05,756 : INFO : PROGRESS: pass 3, at document #16000/16310\n",
      "2025-04-19 00:10:05,964 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:05,968 : INFO : topic #2 (0.143): 0.049*\"工作\" + 0.020*\"時間\" + 0.019*\"方式\" + 0.017*\"小時\" + 0.011*\"工時\" + 0.011*\"內容\" + 0.009*\"地點\" + 0.008*\"每日\" + 0.008*\"面試\" + 0.007*\"經驗\"\n",
      "2025-04-19 00:10:05,968 : INFO : topic #0 (0.143): 0.028*\"工作\" + 0.010*\"徵才\" + 0.010*\"情形\" + 0.010*\"資訊\" + 0.009*\"應徵\" + 0.009*\"內容\" + 0.009*\"方式\" + 0.009*\"文字\" + 0.008*\"第一項\" + 0.008*\"空白\"\n",
      "2025-04-19 00:10:05,968 : INFO : topic #6 (0.143): 0.024*\"活動\" + 0.023*\"報名\" + 0.017*\"研究\" + 0.011*\"電話\" + 0.011*\"問卷\" + 0.010*\"進行\" + 0.010*\"舉辦\" + 0.010*\"參與\" + 0.010*\"參加\" + 0.009*\"資料\"\n",
      "2025-04-19 00:10:05,969 : INFO : topic #4 (0.143): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:10:05,970 : INFO : topic #3 (0.143): 0.025*\"晶片\" + 0.023*\"美國\" + 0.021*\"台灣\" + 0.020*\"半導體\" + 0.019*\"表示\" + 0.018*\"台積電\" + 0.018*\"中國\" + 0.014*\"英特爾\" + 0.011*\"產業\" + 0.011*\"全球\"\n",
      "2025-04-19 00:10:05,970 : INFO : topic diff=0.249440, rho=0.286829\n",
      "2025-04-19 00:10:06,036 : INFO : -8.362 per-word bound, 329.1 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:10:06,036 : INFO : PROGRESS: pass 3, at document #16310/16310\n",
      "2025-04-19 00:10:06,067 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:06,070 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.010*\"聯絡\" + 0.010*\"內容\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:10:06,070 : INFO : topic #2 (0.143): 0.049*\"工作\" + 0.019*\"時間\" + 0.018*\"小時\" + 0.017*\"方式\" + 0.012*\"工時\" + 0.010*\"內容\" + 0.008*\"地點\" + 0.007*\"每日\" + 0.007*\"面試\" + 0.007*\"經驗\"\n",
      "2025-04-19 00:10:06,071 : INFO : topic #0 (0.143): 0.027*\"工作\" + 0.011*\"徵才\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.009*\"應徵\" + 0.009*\"內容\" + 0.009*\"水桶\" + 0.009*\"方式\" + 0.008*\"文字\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:10:06,072 : INFO : topic #5 (0.143): 0.015*\"公司\" + 0.006*\"工作\" + 0.006*\"技術\" + 0.005*\"員工\" + 0.005*\"問題\" + 0.004*\"工程師\" + 0.004*\"台積\" + 0.004*\"科技\" + 0.004*\"面試\" + 0.004*\"目前\"\n",
      "2025-04-19 00:10:06,072 : INFO : topic #3 (0.143): 0.031*\"美國\" + 0.026*\"晶片\" + 0.022*\"台灣\" + 0.021*\"台積電\" + 0.018*\"中國\" + 0.018*\"表示\" + 0.017*\"半導體\" + 0.016*\"投資\" + 0.014*\"英特爾\" + 0.011*\"產業\"\n",
      "2025-04-19 00:10:06,072 : INFO : topic diff=0.238406, rho=0.286829\n",
      "2025-04-19 00:10:06,073 : INFO : PROGRESS: pass 4, at document #2000/16310\n",
      "2025-04-19 00:10:06,692 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:06,695 : INFO : topic #2 (0.143): 0.049*\"工作\" + 0.021*\"方式\" + 0.020*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"每日\" + 0.009*\"地點\" + 0.009*\"休息\" + 0.008*\"面試\"\n",
      "2025-04-19 00:10:06,695 : INFO : topic #0 (0.143): 0.027*\"工作\" + 0.010*\"徵才\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.009*\"內容\" + 0.009*\"應徵\" + 0.009*\"文字\" + 0.009*\"方式\" + 0.008*\"水桶\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:10:06,696 : INFO : topic #6 (0.143): 0.026*\"報名\" + 0.025*\"活動\" + 0.016*\"電話\" + 0.013*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"研究\" + 0.012*\"參與\" + 0.011*\"車馬費\" + 0.010*\"人數\" + 0.010*\"進行\"\n",
      "2025-04-19 00:10:06,696 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.014*\"推定\" + 0.014*\"方式\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.010*\"聯絡\" + 0.010*\"內容\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:10:06,697 : INFO : topic #4 (0.143): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.014*\"砍除\" + 0.013*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:06,697 : INFO : topic diff=0.708066, rho=0.275711\n",
      "2025-04-19 00:10:06,697 : INFO : PROGRESS: pass 4, at document #4000/16310\n",
      "2025-04-19 00:10:07,294 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:07,297 : INFO : topic #5 (0.143): 0.015*\"公司\" + 0.006*\"工作\" + 0.006*\"技術\" + 0.005*\"員工\" + 0.005*\"問題\" + 0.004*\"工程師\" + 0.004*\"科技\" + 0.004*\"台積\" + 0.004*\"面試\" + 0.004*\"目前\"\n",
      "2025-04-19 00:10:07,298 : INFO : topic #2 (0.143): 0.049*\"工作\" + 0.023*\"方式\" + 0.019*\"時間\" + 0.017*\"小時\" + 0.012*\"每日\" + 0.011*\"內容\" + 0.010*\"休息\" + 0.010*\"工時\" + 0.009*\"地點\" + 0.009*\"依法\"\n",
      "2025-04-19 00:10:07,298 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.015*\"推定\" + 0.014*\"方式\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"未註明\" + 0.011*\"文字\" + 0.010*\"聯絡\" + 0.010*\"第一項\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:07,299 : INFO : topic #3 (0.143): 0.030*\"美國\" + 0.025*\"晶片\" + 0.022*\"台灣\" + 0.020*\"台積電\" + 0.018*\"中國\" + 0.017*\"表示\" + 0.017*\"半導體\" + 0.015*\"投資\" + 0.014*\"英特爾\" + 0.011*\"產業\"\n",
      "2025-04-19 00:10:07,299 : INFO : topic #4 (0.143): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.014*\"砍除\" + 0.013*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:07,299 : INFO : topic diff=0.327185, rho=0.275711\n",
      "2025-04-19 00:10:07,300 : INFO : PROGRESS: pass 4, at document #6000/16310\n",
      "2025-04-19 00:10:07,782 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:07,786 : INFO : topic #5 (0.143): 0.016*\"公司\" + 0.007*\"工作\" + 0.006*\"技術\" + 0.005*\"問題\" + 0.005*\"工程師\" + 0.004*\"員工\" + 0.004*\"面試\" + 0.004*\"目前\" + 0.004*\"產品\" + 0.004*\"知道\"\n",
      "2025-04-19 00:10:07,786 : INFO : topic #2 (0.143): 0.048*\"工作\" + 0.024*\"方式\" + 0.019*\"時間\" + 0.017*\"小時\" + 0.012*\"每日\" + 0.011*\"內容\" + 0.011*\"休息\" + 0.010*\"依法\" + 0.009*\"工資\" + 0.009*\"工時\"\n",
      "2025-04-19 00:10:07,787 : INFO : topic #4 (0.143): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.014*\"砍除\" + 0.013*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:07,787 : INFO : topic #6 (0.143): 0.031*\"報名\" + 0.028*\"活動\" + 0.020*\"電話\" + 0.016*\"台北市\" + 0.014*\"車馬費\" + 0.013*\"舉辦\" + 0.013*\"人數\" + 0.012*\"訪問\" + 0.012*\"資料\" + 0.011*\"參與\"\n",
      "2025-04-19 00:10:07,787 : INFO : topic #0 (0.143): 0.027*\"工作\" + 0.010*\"資訊\" + 0.010*\"徵才\" + 0.010*\"情形\" + 0.009*\"內容\" + 0.009*\"文字\" + 0.009*\"分類\" + 0.009*\"應徵\" + 0.008*\"方式\" + 0.008*\"水桶\"\n",
      "2025-04-19 00:10:07,788 : INFO : topic diff=0.199973, rho=0.275711\n",
      "2025-04-19 00:10:07,788 : INFO : PROGRESS: pass 4, at document #8000/16310\n",
      "2025-04-19 00:10:08,047 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:08,050 : INFO : topic #4 (0.143): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.014*\"砍除\" + 0.013*\"方式\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:08,051 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"未註明\" + 0.011*\"文字\" + 0.010*\"聯絡\" + 0.010*\"第一項\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:08,051 : INFO : topic #6 (0.143): 0.030*\"報名\" + 0.028*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.012*\"人數\" + 0.012*\"資料\" + 0.012*\"訪問\" + 0.011*\"參加\"\n",
      "2025-04-19 00:10:08,052 : INFO : topic #5 (0.143): 0.017*\"公司\" + 0.008*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.005*\"技術\" + 0.005*\"開發\" + 0.004*\"目前\" + 0.004*\"比較\" + 0.004*\"產品\"\n",
      "2025-04-19 00:10:08,052 : INFO : topic #2 (0.143): 0.051*\"工作\" + 0.024*\"方式\" + 0.021*\"時間\" + 0.017*\"小時\" + 0.012*\"內容\" + 0.011*\"每日\" + 0.010*\"工時\" + 0.009*\"休息\" + 0.009*\"面試\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:10:08,053 : INFO : topic diff=0.271208, rho=0.275711\n",
      "2025-04-19 00:10:08,053 : INFO : PROGRESS: pass 4, at document #10000/16310\n",
      "2025-04-19 00:10:08,258 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:08,261 : INFO : topic #2 (0.143): 0.052*\"工作\" + 0.023*\"方式\" + 0.021*\"時間\" + 0.018*\"小時\" + 0.012*\"內容\" + 0.011*\"工時\" + 0.011*\"每日\" + 0.009*\"面試\" + 0.008*\"地點\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:10:08,261 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"未註明\" + 0.011*\"文字\" + 0.010*\"聯絡\" + 0.010*\"第一項\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:08,262 : INFO : topic #5 (0.143): 0.017*\"公司\" + 0.009*\"工作\" + 0.008*\"面試\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.006*\"開發\" + 0.005*\"技術\" + 0.005*\"目前\" + 0.005*\"比較\" + 0.004*\"覺得\"\n",
      "2025-04-19 00:10:08,262 : INFO : topic #4 (0.143): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.014*\"砍除\" + 0.013*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:08,263 : INFO : topic #3 (0.143): 0.029*\"美國\" + 0.023*\"台灣\" + 0.022*\"晶片\" + 0.018*\"中國\" + 0.017*\"台積電\" + 0.017*\"半導體\" + 0.016*\"表示\" + 0.015*\"投資\" + 0.012*\"英特爾\" + 0.011*\"產業\"\n",
      "2025-04-19 00:10:08,263 : INFO : topic diff=0.246010, rho=0.275711\n",
      "2025-04-19 00:10:08,263 : INFO : PROGRESS: pass 4, at document #12000/16310\n",
      "2025-04-19 00:10:08,455 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:08,458 : INFO : topic #6 (0.143): 0.029*\"報名\" + 0.029*\"活動\" + 0.016*\"電話\" + 0.014*\"研究\" + 0.013*\"舉辦\" + 0.012*\"台北市\" + 0.011*\"參加\" + 0.011*\"資料\" + 0.011*\"問卷\" + 0.011*\"參與\"\n",
      "2025-04-19 00:10:08,459 : INFO : topic #2 (0.143): 0.051*\"工作\" + 0.022*\"方式\" + 0.021*\"時間\" + 0.018*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"每日\" + 0.008*\"面試\" + 0.008*\"地點\" + 0.008*\"經驗\"\n",
      "2025-04-19 00:10:08,459 : INFO : topic #5 (0.143): 0.016*\"公司\" + 0.008*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.005*\"目前\" + 0.004*\"比較\" + 0.004*\"軟體\"\n",
      "2025-04-19 00:10:08,460 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.010*\"聯絡\" + 0.010*\"第一項\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:08,460 : INFO : topic #3 (0.143): 0.023*\"晶片\" + 0.022*\"台灣\" + 0.021*\"美國\" + 0.020*\"半導體\" + 0.017*\"台積電\" + 0.016*\"表示\" + 0.015*\"中國\" + 0.012*\"產業\" + 0.011*\"投資\" + 0.011*\"全球\"\n",
      "2025-04-19 00:10:08,460 : INFO : topic diff=0.288108, rho=0.275711\n",
      "2025-04-19 00:10:08,461 : INFO : PROGRESS: pass 4, at document #14000/16310\n",
      "2025-04-19 00:10:08,679 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:08,682 : INFO : topic #0 (0.143): 0.025*\"工作\" + 0.011*\"徵才\" + 0.010*\"資訊\" + 0.010*\"水桶\" + 0.009*\"情形\" + 0.009*\"文字\" + 0.009*\"內容\" + 0.008*\"應徵\" + 0.008*\"分類\" + 0.008*\"方式\"\n",
      "2025-04-19 00:10:08,682 : INFO : topic #6 (0.143): 0.027*\"活動\" + 0.026*\"報名\" + 0.015*\"研究\" + 0.014*\"電話\" + 0.012*\"問卷\" + 0.011*\"舉辦\" + 0.011*\"參與\" + 0.011*\"參加\" + 0.010*\"台北市\" + 0.010*\"進行\"\n",
      "2025-04-19 00:10:08,683 : INFO : topic #4 (0.143): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.014*\"砍除\" + 0.013*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:08,683 : INFO : topic #5 (0.143): 0.015*\"公司\" + 0.008*\"工作\" + 0.005*\"問題\" + 0.005*\"面試\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.004*\"目前\" + 0.004*\"開發\" + 0.004*\"員工\" + 0.004*\"比較\"\n",
      "2025-04-19 00:10:08,684 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.010*\"聯絡\" + 0.010*\"內容\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:10:08,684 : INFO : topic diff=0.277037, rho=0.275711\n",
      "2025-04-19 00:10:08,684 : INFO : PROGRESS: pass 4, at document #16000/16310\n",
      "2025-04-19 00:10:08,863 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:08,866 : INFO : topic #0 (0.143): 0.024*\"工作\" + 0.011*\"徵才\" + 0.010*\"資訊\" + 0.010*\"水桶\" + 0.009*\"情形\" + 0.008*\"內容\" + 0.008*\"文字\" + 0.008*\"應徵\" + 0.008*\"分類\" + 0.008*\"方式\"\n",
      "2025-04-19 00:10:08,866 : INFO : topic #3 (0.143): 0.024*\"晶片\" + 0.023*\"美國\" + 0.022*\"台灣\" + 0.018*\"半導體\" + 0.018*\"表示\" + 0.017*\"台積電\" + 0.017*\"中國\" + 0.013*\"英特爾\" + 0.012*\"產業\" + 0.011*\"全球\"\n",
      "2025-04-19 00:10:08,867 : INFO : topic #4 (0.143): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.014*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:08,867 : INFO : topic #6 (0.143): 0.025*\"活動\" + 0.024*\"報名\" + 0.017*\"研究\" + 0.012*\"電話\" + 0.011*\"問卷\" + 0.010*\"舉辦\" + 0.010*\"參與\" + 0.010*\"參加\" + 0.010*\"進行\" + 0.010*\"資料\"\n",
      "2025-04-19 00:10:08,868 : INFO : topic #1 (0.143): 0.031*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.010*\"聯絡\" + 0.010*\"內容\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:10:08,868 : INFO : topic diff=0.234404, rho=0.275711\n",
      "2025-04-19 00:10:08,934 : INFO : -8.356 per-word bound, 327.6 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:10:08,934 : INFO : PROGRESS: pass 4, at document #16310/16310\n",
      "2025-04-19 00:10:08,991 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:08,994 : INFO : topic #1 (0.143): 0.030*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"單位\" + 0.012*\"情形\" + 0.011*\"文字\" + 0.011*\"未註明\" + 0.010*\"聯絡\" + 0.010*\"內容\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:10:08,995 : INFO : topic #2 (0.143): 0.049*\"工作\" + 0.019*\"時間\" + 0.018*\"小時\" + 0.017*\"方式\" + 0.013*\"工時\" + 0.010*\"內容\" + 0.008*\"地點\" + 0.007*\"每日\" + 0.007*\"經驗\" + 0.007*\"面試\"\n",
      "2025-04-19 00:10:08,996 : INFO : topic #0 (0.143): 0.023*\"工作\" + 0.011*\"徵才\" + 0.010*\"水桶\" + 0.010*\"資訊\" + 0.009*\"詐騙\" + 0.009*\"情形\" + 0.008*\"內容\" + 0.008*\"應徵\" + 0.008*\"文字\" + 0.007*\"分類\"\n",
      "2025-04-19 00:10:08,996 : INFO : topic #3 (0.143): 0.030*\"美國\" + 0.024*\"晶片\" + 0.023*\"台灣\" + 0.020*\"台積電\" + 0.017*\"表示\" + 0.017*\"中國\" + 0.016*\"半導體\" + 0.015*\"投資\" + 0.013*\"英特爾\" + 0.011*\"產業\"\n",
      "2025-04-19 00:10:08,997 : INFO : topic #5 (0.143): 0.015*\"公司\" + 0.006*\"工作\" + 0.006*\"技術\" + 0.005*\"員工\" + 0.005*\"問題\" + 0.004*\"工程師\" + 0.004*\"台積\" + 0.004*\"面試\" + 0.004*\"目前\" + 0.004*\"報導\"\n",
      "2025-04-19 00:10:08,997 : INFO : topic diff=0.222449, rho=0.275711\n",
      "2025-04-19 00:10:08,997 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel<num_terms=23261, num_topics=7, decay=0.5, chunksize=2000> in 16.61s', 'datetime': '2025-04-19T00:10:08.997783', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n",
      "2025-04-19 00:10:14,125 : INFO : -7.022 per-word bound, 130.0 perplexity estimate based on a held-out corpus of 16310 documents with 3460358 words\n",
      "2025-04-19 00:10:14,128 : INFO : using ParallelWordOccurrenceAccumulator<processes=7, batch_size=64> to estimate probabilities from sliding windows\n",
      "2025-04-19 00:10:17,728 : INFO : 1 batches submitted to accumulate stats from 64 documents (22660 virtual)\n",
      "2025-04-19 00:10:17,731 : INFO : 2 batches submitted to accumulate stats from 128 documents (45646 virtual)\n",
      "2025-04-19 00:10:17,733 : INFO : 3 batches submitted to accumulate stats from 192 documents (67171 virtual)\n",
      "2025-04-19 00:10:17,735 : INFO : 4 batches submitted to accumulate stats from 256 documents (88330 virtual)\n",
      "2025-04-19 00:10:17,738 : INFO : 5 batches submitted to accumulate stats from 320 documents (109687 virtual)\n",
      "2025-04-19 00:10:17,741 : INFO : 6 batches submitted to accumulate stats from 384 documents (131042 virtual)\n",
      "2025-04-19 00:10:17,746 : INFO : 7 batches submitted to accumulate stats from 448 documents (153774 virtual)\n",
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      "2025-04-19 00:10:18,080 : INFO : 26 batches submitted to accumulate stats from 1664 documents (575041 virtual)\n",
      "2025-04-19 00:10:18,085 : INFO : 27 batches submitted to accumulate stats from 1728 documents (598912 virtual)\n",
      "2025-04-19 00:10:18,197 : INFO : 28 batches submitted to accumulate stats from 1792 documents (622487 virtual)\n",
      "2025-04-19 00:10:18,211 : INFO : 29 batches submitted to accumulate stats from 1856 documents (648902 virtual)\n",
      "2025-04-19 00:10:18,230 : INFO : 30 batches submitted to accumulate stats from 1920 documents (671126 virtual)\n",
      "2025-04-19 00:10:18,249 : INFO : 31 batches submitted to accumulate stats from 1984 documents (693717 virtual)\n",
      "2025-04-19 00:10:18,261 : INFO : 32 batches submitted to accumulate stats from 2048 documents (714139 virtual)\n",
      "2025-04-19 00:10:18,265 : INFO : 33 batches submitted to accumulate stats from 2112 documents (736202 virtual)\n",
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      "2025-04-19 00:10:18,340 : INFO : 35 batches submitted to accumulate stats from 2240 documents (779677 virtual)\n",
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      "2025-04-19 00:10:21,350 : INFO : 7 accumulators retrieved from output queue\n",
      "2025-04-19 00:10:21,360 : INFO : accumulated word occurrence stats for 3451622 virtual documents\n",
      "2025-04-19 00:10:21,448 : INFO : using symmetric alpha at 0.125\n",
      "2025-04-19 00:10:21,448 : INFO : using symmetric eta at 0.125\n",
      "2025-04-19 00:10:21,449 : INFO : using serial LDA version on this node\n",
      "2025-04-19 00:10:21,457 : INFO : running online (multi-pass) LDA training, 8 topics, 5 passes over the supplied corpus of 16310 documents, updating model once every 2000 documents, evaluating perplexity every 16310 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2025-04-19 00:10:21,457 : INFO : PROGRESS: pass 0, at document #2000/16310\n",
      "2025-04-19 00:10:22,102 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:22,106 : INFO : topic #3 (0.125): 0.021*\"工作\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.011*\"應徵\" + 0.010*\"聯絡人\" + 0.010*\"推定\" + 0.010*\"文字\" + 0.009*\"空白\" + 0.009*\"情形\" + 0.009*\"資訊\"\n",
      "2025-04-19 00:10:22,106 : INFO : topic #4 (0.125): 0.039*\"工作\" + 0.017*\"推定\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.011*\"聯絡\" + 0.010*\"單位\" + 0.010*\"聯絡人\" + 0.010*\"第一項\" + 0.009*\"國定假日\" + 0.009*\"內容\"\n",
      "2025-04-19 00:10:22,107 : INFO : topic #2 (0.125): 0.041*\"工作\" + 0.013*\"內容\" + 0.013*\"推定\" + 0.012*\"工資\" + 0.012*\"方式\" + 0.012*\"應徵\" + 0.010*\"情形\" + 0.010*\"小時\" + 0.010*\"砍除\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:10:22,107 : INFO : topic #0 (0.125): 0.030*\"工作\" + 0.015*\"方式\" + 0.014*\"應徵\" + 0.012*\"推定\" + 0.012*\"單位\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:10:22,108 : INFO : topic #7 (0.125): 0.026*\"工作\" + 0.014*\"空白\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"砍除\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"資訊\" + 0.010*\"方式\"\n",
      "2025-04-19 00:10:22,108 : INFO : topic diff=8.045865, rho=1.000000\n",
      "2025-04-19 00:10:22,109 : INFO : PROGRESS: pass 0, at document #4000/16310\n",
      "2025-04-19 00:10:22,686 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:22,689 : INFO : topic #5 (0.125): 0.019*\"工作\" + 0.016*\"方式\" + 0.010*\"應徵\" + 0.010*\"依法\" + 0.009*\"聯絡\" + 0.009*\"標題\" + 0.008*\"通知\" + 0.008*\"時間\" + 0.008*\"內容\" + 0.008*\"小時\"\n",
      "2025-04-19 00:10:22,690 : INFO : topic #2 (0.125): 0.043*\"工作\" + 0.015*\"方式\" + 0.014*\"推定\" + 0.013*\"工資\" + 0.013*\"內容\" + 0.012*\"小時\" + 0.011*\"單位\" + 0.011*\"應徵\" + 0.011*\"未註明\" + 0.010*\"依法\"\n",
      "2025-04-19 00:10:22,691 : INFO : topic #7 (0.125): 0.026*\"工作\" + 0.014*\"空白\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"砍除\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.010*\"資訊\" + 0.009*\"方式\"\n",
      "2025-04-19 00:10:22,691 : INFO : topic #4 (0.125): 0.038*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.010*\"聯絡人\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:22,692 : INFO : topic #3 (0.125): 0.019*\"工作\" + 0.013*\"方式\" + 0.011*\"砍除\" + 0.010*\"應徵\" + 0.010*\"聯絡人\" + 0.009*\"文字\" + 0.008*\"推定\" + 0.008*\"資訊\" + 0.008*\"分類\" + 0.008*\"空白\"\n",
      "2025-04-19 00:10:22,692 : INFO : topic diff=0.742718, rho=0.707107\n",
      "2025-04-19 00:10:22,693 : INFO : PROGRESS: pass 0, at document #6000/16310\n",
      "2025-04-19 00:10:23,224 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:23,228 : INFO : topic #7 (0.125): 0.025*\"工作\" + 0.013*\"空白\" + 0.011*\"推定\" + 0.011*\"內容\" + 0.010*\"聯絡\" + 0.010*\"第一項\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.009*\"文字\"\n",
      "2025-04-19 00:10:23,228 : INFO : topic #6 (0.125): 0.026*\"報名\" + 0.023*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.012*\"車馬費\" + 0.012*\"資料\" + 0.012*\"人數\" + 0.011*\"訪問\" + 0.011*\"時間\" + 0.011*\"舉辦\"\n",
      "2025-04-19 00:10:23,229 : INFO : topic #0 (0.125): 0.030*\"工作\" + 0.013*\"應徵\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"推定\" + 0.011*\"資訊\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:23,229 : INFO : topic #5 (0.125): 0.022*\"工作\" + 0.017*\"方式\" + 0.009*\"時間\" + 0.009*\"依法\" + 0.008*\"通知\" + 0.007*\"應徵\" + 0.007*\"聯絡\" + 0.007*\"每日\" + 0.007*\"面試\" + 0.007*\"內容\"\n",
      "2025-04-19 00:10:23,230 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"單位\"\n",
      "2025-04-19 00:10:23,230 : INFO : topic diff=0.607741, rho=0.577350\n",
      "2025-04-19 00:10:23,231 : INFO : PROGRESS: pass 0, at document #8000/16310\n",
      "2025-04-19 00:10:23,543 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:23,547 : INFO : topic #5 (0.125): 0.018*\"工作\" + 0.016*\"公司\" + 0.012*\"面試\" + 0.010*\"工程師\" + 0.009*\"經驗\" + 0.008*\"問題\" + 0.008*\"團隊\" + 0.008*\"時間\" + 0.007*\"方式\" + 0.007*\"技術\"\n",
      "2025-04-19 00:10:23,547 : INFO : topic #2 (0.125): 0.037*\"工作\" + 0.012*\"公司\" + 0.011*\"方式\" + 0.009*\"內容\" + 0.009*\"小時\" + 0.009*\"時間\" + 0.008*\"面試\" + 0.008*\"推定\" + 0.007*\"工資\" + 0.007*\"覺得\"\n",
      "2025-04-19 00:10:23,548 : INFO : topic #3 (0.125): 0.014*\"工作\" + 0.011*\"方式\" + 0.009*\"公司\" + 0.008*\"聯絡人\" + 0.008*\"資訊\" + 0.008*\"時間\" + 0.008*\"砍除\" + 0.008*\"研發\" + 0.007*\"文字\" + 0.007*\"分類\"\n",
      "2025-04-19 00:10:23,548 : INFO : topic #0 (0.125): 0.030*\"工作\" + 0.013*\"應徵\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"推定\" + 0.011*\"資訊\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:23,549 : INFO : topic #7 (0.125): 0.025*\"工作\" + 0.012*\"空白\" + 0.011*\"推定\" + 0.010*\"內容\" + 0.010*\"聯絡\" + 0.010*\"第一項\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.009*\"文字\"\n",
      "2025-04-19 00:10:23,549 : INFO : topic diff=0.746824, rho=0.500000\n",
      "2025-04-19 00:10:23,550 : INFO : PROGRESS: pass 0, at document #10000/16310\n",
      "2025-04-19 00:10:23,814 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:23,833 : INFO : topic #6 (0.125): 0.016*\"產品\" + 0.013*\"資料\" + 0.013*\"報名\" + 0.012*\"活動\" + 0.012*\"公司\" + 0.012*\"使用\" + 0.010*\"目前\" + 0.009*\"進行\" + 0.009*\"時間\" + 0.009*\"電話\"\n",
      "2025-04-19 00:10:23,839 : INFO : topic #3 (0.125): 0.014*\"工作\" + 0.010*\"方式\" + 0.010*\"資工\" + 0.010*\"職場\" + 0.010*\"研發\" + 0.009*\"數學\" + 0.008*\"公司\" + 0.008*\"資訊\" + 0.008*\"聯絡人\" + 0.008*\"時間\"\n",
      "2025-04-19 00:10:23,842 : INFO : topic #2 (0.125): 0.033*\"工作\" + 0.012*\"公司\" + 0.010*\"覺得\" + 0.008*\"方式\" + 0.008*\"程式\" + 0.008*\"內容\" + 0.008*\"時間\" + 0.008*\"面試\" + 0.007*\"小時\" + 0.007*\"比較\"\n",
      "2025-04-19 00:10:23,843 : INFO : topic #7 (0.125): 0.024*\"工作\" + 0.012*\"空白\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"聯絡\" + 0.010*\"砍除\" + 0.009*\"第一項\" + 0.009*\"資訊\" + 0.009*\"情形\" + 0.008*\"文字\"\n",
      "2025-04-19 00:10:23,844 : INFO : topic #5 (0.125): 0.017*\"公司\" + 0.016*\"工作\" + 0.012*\"面試\" + 0.009*\"問題\" + 0.009*\"工程師\" + 0.008*\"經驗\" + 0.007*\"時間\" + 0.007*\"開發\" + 0.007*\"技術\" + 0.007*\"團隊\"\n",
      "2025-04-19 00:10:23,844 : INFO : topic diff=0.586785, rho=0.447214\n",
      "2025-04-19 00:10:23,845 : INFO : PROGRESS: pass 0, at document #12000/16310\n",
      "2025-04-19 00:10:24,093 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:24,097 : INFO : topic #0 (0.125): 0.029*\"工作\" + 0.013*\"應徵\" + 0.013*\"砍除\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"推定\" + 0.011*\"資訊\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:24,097 : INFO : topic #3 (0.125): 0.052*\"半導體\" + 0.035*\"製程\" + 0.017*\"研發\" + 0.014*\"職場\" + 0.013*\"表示\" + 0.011*\"工作\" + 0.010*\"資工\" + 0.008*\"數學\" + 0.008*\"方式\" + 0.007*\"公司\"\n",
      "2025-04-19 00:10:24,098 : INFO : topic #1 (0.125): 0.029*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"單位\"\n",
      "2025-04-19 00:10:24,098 : INFO : topic #6 (0.125): 0.014*\"產品\" + 0.012*\"資料\" + 0.011*\"使用\" + 0.011*\"活動\" + 0.011*\"報名\" + 0.010*\"公司\" + 0.010*\"進行\" + 0.009*\"目前\" + 0.009*\"研究\" + 0.008*\"日本\"\n",
      "2025-04-19 00:10:24,099 : INFO : topic #5 (0.125): 0.016*\"公司\" + 0.012*\"工作\" + 0.009*\"面試\" + 0.008*\"問題\" + 0.007*\"工程師\" + 0.006*\"技術\" + 0.006*\"經驗\" + 0.006*\"開發\" + 0.006*\"時間\" + 0.005*\"台灣\"\n",
      "2025-04-19 00:10:24,099 : INFO : topic diff=0.535218, rho=0.408248\n",
      "2025-04-19 00:10:24,100 : INFO : PROGRESS: pass 0, at document #14000/16310\n",
      "2025-04-19 00:10:24,433 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:24,441 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.010*\"工作\" + 0.008*\"台灣\" + 0.006*\"工程師\" + 0.006*\"技術\" + 0.006*\"問題\" + 0.006*\"面試\" + 0.005*\"員工\" + 0.005*\"美國\" + 0.004*\"科技\"\n",
      "2025-04-19 00:10:24,442 : INFO : topic #4 (0.125): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:24,443 : INFO : topic #1 (0.125): 0.029*\"工作\" + 0.014*\"方式\" + 0.013*\"砍除\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"單位\"\n",
      "2025-04-19 00:10:24,444 : INFO : topic #6 (0.125): 0.013*\"產品\" + 0.010*\"進行\" + 0.010*\"資料\" + 0.010*\"使用\" + 0.009*\"模型\" + 0.009*\"研究\" + 0.009*\"今年\" + 0.009*\"日本\" + 0.009*\"公司\" + 0.009*\"活動\"\n",
      "2025-04-19 00:10:24,445 : INFO : topic #2 (0.125): 0.025*\"工作\" + 0.011*\"公司\" + 0.009*\"覺得\" + 0.007*\"程式\" + 0.006*\"應該\" + 0.006*\"時間\" + 0.006*\"內容\" + 0.006*\"比較\" + 0.006*\"面試\" + 0.006*\"方式\"\n",
      "2025-04-19 00:10:24,446 : INFO : topic diff=0.505230, rho=0.377964\n",
      "2025-04-19 00:10:24,447 : INFO : PROGRESS: pass 0, at document #16000/16310\n",
      "2025-04-19 00:10:24,692 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:24,696 : INFO : topic #2 (0.125): 0.023*\"工作\" + 0.011*\"公司\" + 0.009*\"覺得\" + 0.006*\"應該\" + 0.006*\"記者\" + 0.006*\"時間\" + 0.005*\"程式\" + 0.005*\"比較\" + 0.005*\"內容\" + 0.005*\"真的\"\n",
      "2025-04-19 00:10:24,696 : INFO : topic #4 (0.125): 0.036*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.010*\"方式\" + 0.010*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:24,697 : INFO : topic #6 (0.125): 0.012*\"產品\" + 0.011*\"模型\" + 0.011*\"今年\" + 0.010*\"生產\" + 0.010*\"進行\" + 0.009*\"研究\" + 0.009*\"日本\" + 0.009*\"資料\" + 0.009*\"蘋果\" + 0.009*\"影響\"\n",
      "2025-04-19 00:10:24,698 : INFO : topic #7 (0.125): 0.018*\"工作\" + 0.009*\"空白\" + 0.008*\"推定\" + 0.008*\"內容\" + 0.007*\"第一項\" + 0.007*\"聯絡\" + 0.007*\"砍除\" + 0.007*\"資訊\" + 0.007*\"惠普\" + 0.007*\"情形\"\n",
      "2025-04-19 00:10:24,698 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.008*\"台灣\" + 0.008*\"工作\" + 0.007*\"美國\" + 0.006*\"技術\" + 0.006*\"員工\" + 0.005*\"工程師\" + 0.005*\"晶片\" + 0.005*\"問題\" + 0.005*\"科技\"\n",
      "2025-04-19 00:10:24,700 : INFO : topic diff=0.470208, rho=0.353553\n",
      "2025-04-19 00:10:24,801 : INFO : -8.974 per-word bound, 502.9 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:10:24,801 : INFO : PROGRESS: pass 0, at document #16310/16310\n",
      "2025-04-19 00:10:24,839 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:24,843 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.009*\"美國\" + 0.008*\"台灣\" + 0.007*\"技術\" + 0.007*\"工作\" + 0.006*\"員工\" + 0.006*\"晶片\" + 0.005*\"台積電\" + 0.005*\"科技\" + 0.005*\"台積\"\n",
      "2025-04-19 00:10:24,843 : INFO : topic #2 (0.125): 0.021*\"工作\" + 0.011*\"公司\" + 0.009*\"覺得\" + 0.007*\"應該\" + 0.007*\"真的\" + 0.006*\"記者\" + 0.006*\"時間\" + 0.005*\"東西\" + 0.005*\"比較\" + 0.005*\"一下\"\n",
      "2025-04-19 00:10:24,844 : INFO : topic #7 (0.125): 0.016*\"工作\" + 0.008*\"空白\" + 0.007*\"推定\" + 0.007*\"內容\" + 0.006*\"第一項\" + 0.006*\"聯絡\" + 0.006*\"砍除\" + 0.006*\"資訊\" + 0.006*\"惠普\" + 0.006*\"情形\"\n",
      "2025-04-19 00:10:24,844 : INFO : topic #1 (0.125): 0.028*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.010*\"文字\"\n",
      "2025-04-19 00:10:24,845 : INFO : topic #0 (0.125): 0.027*\"工作\" + 0.012*\"應徵\" + 0.012*\"砍除\" + 0.011*\"空白\" + 0.011*\"方式\" + 0.010*\"單位\" + 0.010*\"第一項\" + 0.010*\"推定\" + 0.010*\"資訊\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:24,845 : INFO : topic diff=0.427759, rho=0.333333\n",
      "2025-04-19 00:10:24,845 : INFO : PROGRESS: pass 1, at document #2000/16310\n",
      "2025-04-19 00:10:25,440 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:25,444 : INFO : topic #7 (0.125): 0.019*\"工作\" + 0.009*\"內容\" + 0.008*\"聯絡\" + 0.007*\"方式\" + 0.007*\"工資\" + 0.007*\"台北市\" + 0.007*\"時間\" + 0.006*\"資訊\" + 0.006*\"推定\" + 0.006*\"空白\"\n",
      "2025-04-19 00:10:25,444 : INFO : topic #1 (0.125): 0.032*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.012*\"砍除\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"單位\" + 0.010*\"國定假日\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:10:25,445 : INFO : topic #0 (0.125): 0.033*\"工作\" + 0.013*\"方式\" + 0.012*\"應徵\" + 0.011*\"推定\" + 0.010*\"內容\" + 0.010*\"砍除\" + 0.010*\"空白\" + 0.010*\"單位\" + 0.009*\"資訊\" + 0.009*\"工資\"\n",
      "2025-04-19 00:10:25,446 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.009*\"美國\" + 0.008*\"台灣\" + 0.007*\"技術\" + 0.007*\"工作\" + 0.006*\"員工\" + 0.006*\"晶片\" + 0.005*\"台積電\" + 0.005*\"科技\" + 0.005*\"台積\"\n",
      "2025-04-19 00:10:25,446 : INFO : topic #3 (0.125): 0.065*\"半導體\" + 0.039*\"製程\" + 0.025*\"研發\" + 0.024*\"表示\" + 0.021*\"川普\" + 0.018*\"投資\" + 0.015*\"中國\" + 0.015*\"魏哲家\" + 0.011*\"晶圓廠\" + 0.010*\"奈米\"\n",
      "2025-04-19 00:10:25,447 : INFO : topic diff=0.938244, rho=0.313805\n",
      "2025-04-19 00:10:25,447 : INFO : PROGRESS: pass 1, at document #4000/16310\n",
      "2025-04-19 00:10:26,034 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:26,037 : INFO : topic #0 (0.125): 0.034*\"工作\" + 0.014*\"方式\" + 0.012*\"應徵\" + 0.011*\"推定\" + 0.010*\"內容\" + 0.010*\"單位\" + 0.010*\"工資\" + 0.009*\"聯絡\" + 0.009*\"情形\" + 0.009*\"砍除\"\n",
      "2025-04-19 00:10:26,038 : INFO : topic #3 (0.125): 0.055*\"半導體\" + 0.033*\"製程\" + 0.021*\"研發\" + 0.021*\"表示\" + 0.018*\"川普\" + 0.015*\"投資\" + 0.015*\"中國\" + 0.012*\"魏哲家\" + 0.010*\"晶圓廠\" + 0.009*\"奈米\"\n",
      "2025-04-19 00:10:26,038 : INFO : topic #4 (0.125): 0.037*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"聯絡\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 00:10:26,039 : INFO : topic #6 (0.125): 0.020*\"報名\" + 0.019*\"活動\" + 0.014*\"電話\" + 0.011*\"資料\" + 0.011*\"進行\" + 0.011*\"台北市\" + 0.010*\"舉辦\" + 0.009*\"車馬費\" + 0.009*\"研究\" + 0.009*\"參與\"\n",
      "2025-04-19 00:10:26,039 : INFO : topic #5 (0.125): 0.013*\"公司\" + 0.009*\"美國\" + 0.008*\"台灣\" + 0.007*\"工作\" + 0.007*\"技術\" + 0.006*\"員工\" + 0.005*\"晶片\" + 0.005*\"台積電\" + 0.005*\"科技\" + 0.004*\"台積\"\n",
      "2025-04-19 00:10:26,039 : INFO : topic diff=0.398428, rho=0.313805\n",
      "2025-04-19 00:10:26,040 : INFO : PROGRESS: pass 1, at document #6000/16310\n",
      "2025-04-19 00:10:26,525 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:26,542 : INFO : topic #1 (0.125): 0.032*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"砍除\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"文字\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:10:26,552 : INFO : topic #7 (0.125): 0.022*\"工作\" + 0.012*\"時間\" + 0.012*\"訪員\" + 0.010*\"內容\" + 0.009*\"規定\" + 0.009*\"南港\" + 0.008*\"南港區\" + 0.008*\"台北市\" + 0.008*\"工資\" + 0.007*\"方式\"\n",
      "2025-04-19 00:10:26,553 : INFO : topic #6 (0.125): 0.025*\"報名\" + 0.022*\"活動\" + 0.016*\"電話\" + 0.013*\"台北市\" + 0.012*\"資料\" + 0.011*\"車馬費\" + 0.011*\"舉辦\" + 0.011*\"進行\" + 0.010*\"人數\" + 0.010*\"訪問\"\n",
      "2025-04-19 00:10:26,553 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.007*\"美國\" + 0.007*\"台灣\" + 0.007*\"工作\" + 0.006*\"技術\" + 0.005*\"員工\" + 0.005*\"工程師\" + 0.005*\"問題\" + 0.004*\"科技\" + 0.004*\"晶片\"\n",
      "2025-04-19 00:10:26,554 : INFO : topic #3 (0.125): 0.047*\"半導體\" + 0.028*\"製程\" + 0.019*\"研發\" + 0.018*\"表示\" + 0.015*\"川普\" + 0.013*\"中國\" + 0.013*\"投資\" + 0.011*\"魏哲家\" + 0.008*\"時間\" + 0.008*\"職場\"\n",
      "2025-04-19 00:10:26,554 : INFO : topic diff=0.251908, rho=0.313805\n",
      "2025-04-19 00:10:26,555 : INFO : PROGRESS: pass 1, at document #8000/16310\n",
      "2025-04-19 00:10:26,818 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:26,822 : INFO : topic #1 (0.125): 0.032*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"砍除\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"文字\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:10:26,822 : INFO : topic #7 (0.125): 0.021*\"工作\" + 0.013*\"時間\" + 0.010*\"訪員\" + 0.009*\"內容\" + 0.009*\"南港\" + 0.008*\"規定\" + 0.008*\"南港區\" + 0.007*\"台北市\" + 0.007*\"勞基法\" + 0.007*\"工資\"\n",
      "2025-04-19 00:10:26,823 : INFO : topic #2 (0.125): 0.030*\"工作\" + 0.011*\"覺得\" + 0.011*\"公司\" + 0.011*\"面試\" + 0.011*\"時間\" + 0.008*\"程式\" + 0.007*\"比較\" + 0.006*\"內容\" + 0.006*\"應該\" + 0.006*\"小時\"\n",
      "2025-04-19 00:10:26,823 : INFO : topic #5 (0.125): 0.017*\"公司\" + 0.009*\"工作\" + 0.007*\"工程師\" + 0.007*\"問題\" + 0.006*\"技術\" + 0.006*\"面試\" + 0.005*\"開發\" + 0.005*\"台灣\" + 0.005*\"經驗\" + 0.005*\"團隊\"\n",
      "2025-04-19 00:10:26,824 : INFO : topic #6 (0.125): 0.023*\"報名\" + 0.021*\"活動\" + 0.015*\"電話\" + 0.013*\"台北市\" + 0.012*\"資料\" + 0.011*\"進行\" + 0.010*\"舉辦\" + 0.010*\"使用\" + 0.010*\"時間\" + 0.010*\"車馬費\"\n",
      "2025-04-19 00:10:26,824 : INFO : topic diff=0.350872, rho=0.313805\n",
      "2025-04-19 00:10:26,824 : INFO : PROGRESS: pass 1, at document #10000/16310\n",
      "2025-04-19 00:10:27,055 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:27,059 : INFO : topic #3 (0.125): 0.050*\"半導體\" + 0.025*\"製程\" + 0.020*\"研發\" + 0.016*\"數學\" + 0.015*\"表示\" + 0.012*\"川普\" + 0.012*\"中國\" + 0.011*\"投資\" + 0.009*\"職場\" + 0.009*\"魏哲家\"\n",
      "2025-04-19 00:10:27,060 : INFO : topic #0 (0.125): 0.038*\"工作\" + 0.016*\"方式\" + 0.012*\"應徵\" + 0.011*\"內容\" + 0.010*\"推定\" + 0.010*\"聯絡\" + 0.009*\"工資\" + 0.009*\"依法\" + 0.009*\"單位\" + 0.009*\"小時\"\n",
      "2025-04-19 00:10:27,060 : INFO : topic #7 (0.125): 0.020*\"工作\" + 0.012*\"時間\" + 0.011*\"東京\" + 0.009*\"南港\" + 0.009*\"訪員\" + 0.008*\"內容\" + 0.008*\"規定\" + 0.008*\"接案\" + 0.008*\"勞基法\" + 0.007*\"南港區\"\n",
      "2025-04-19 00:10:27,061 : INFO : topic #5 (0.125): 0.017*\"公司\" + 0.009*\"工作\" + 0.007*\"問題\" + 0.007*\"工程師\" + 0.007*\"面試\" + 0.006*\"開發\" + 0.006*\"技術\" + 0.006*\"經驗\" + 0.006*\"目前\" + 0.005*\"團隊\"\n",
      "2025-04-19 00:10:27,061 : INFO : topic #2 (0.125): 0.030*\"工作\" + 0.013*\"面試\" + 0.013*\"覺得\" + 0.011*\"公司\" + 0.011*\"時間\" + 0.009*\"程式\" + 0.008*\"比較\" + 0.007*\"應該\" + 0.007*\"東西\" + 0.007*\"一下\"\n",
      "2025-04-19 00:10:27,061 : INFO : topic diff=0.291535, rho=0.313805\n",
      "2025-04-19 00:10:27,062 : INFO : PROGRESS: pass 1, at document #12000/16310\n",
      "2025-04-19 00:10:27,364 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:27,368 : INFO : topic #0 (0.125): 0.038*\"工作\" + 0.017*\"方式\" + 0.012*\"應徵\" + 0.011*\"內容\" + 0.010*\"聯絡\" + 0.010*\"推定\" + 0.009*\"工資\" + 0.009*\"依法\" + 0.009*\"單位\" + 0.009*\"小時\"\n",
      "2025-04-19 00:10:27,368 : INFO : topic #3 (0.125): 0.057*\"半導體\" + 0.030*\"製程\" + 0.024*\"表示\" + 0.018*\"中國\" + 0.015*\"研發\" + 0.014*\"投資\" + 0.012*\"熊本\" + 0.012*\"奈米\" + 0.011*\"晶圓廠\" + 0.009*\"先進\"\n",
      "2025-04-19 00:10:27,369 : INFO : topic #5 (0.125): 0.016*\"公司\" + 0.008*\"工作\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.006*\"技術\" + 0.006*\"開發\" + 0.006*\"台灣\" + 0.005*\"面試\" + 0.005*\"目前\" + 0.005*\"經驗\"\n",
      "2025-04-19 00:10:27,369 : INFO : topic #1 (0.125): 0.032*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"砍除\" + 0.011*\"內容\" + 0.010*\"文字\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:10:27,370 : INFO : topic #6 (0.125): 0.020*\"活動\" + 0.020*\"報名\" + 0.012*\"進行\" + 0.012*\"資料\" + 0.012*\"研究\" + 0.011*\"電話\" + 0.010*\"使用\" + 0.010*\"台北市\" + 0.009*\"參加\" + 0.009*\"舉辦\"\n",
      "2025-04-19 00:10:27,370 : INFO : topic diff=0.367805, rho=0.313805\n",
      "2025-04-19 00:10:27,371 : INFO : PROGRESS: pass 1, at document #14000/16310\n",
      "2025-04-19 00:10:27,704 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:27,711 : INFO : topic #2 (0.125): 0.025*\"工作\" + 0.011*\"覺得\" + 0.010*\"公司\" + 0.010*\"面試\" + 0.009*\"時間\" + 0.008*\"比較\" + 0.007*\"程式\" + 0.007*\"應該\" + 0.006*\"真的\" + 0.006*\"一下\"\n",
      "2025-04-19 00:10:27,712 : INFO : topic #4 (0.125): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 00:10:27,713 : INFO : topic #3 (0.125): 0.047*\"半導體\" + 0.029*\"表示\" + 0.027*\"中國\" + 0.022*\"製程\" + 0.015*\"研發\" + 0.014*\"投資\" + 0.013*\"晶片\" + 0.013*\"英特爾\" + 0.012*\"先進\" + 0.009*\"奈米\"\n",
      "2025-04-19 00:10:27,714 : INFO : topic #0 (0.125): 0.038*\"工作\" + 0.017*\"方式\" + 0.012*\"應徵\" + 0.011*\"內容\" + 0.010*\"聯絡\" + 0.010*\"推定\" + 0.009*\"工資\" + 0.009*\"單位\" + 0.009*\"依法\" + 0.009*\"小時\"\n",
      "2025-04-19 00:10:27,716 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.007*\"台灣\" + 0.006*\"工作\" + 0.006*\"技術\" + 0.005*\"工程師\" + 0.005*\"問題\" + 0.005*\"員工\" + 0.005*\"美國\" + 0.004*\"目前\" + 0.004*\"開發\"\n",
      "2025-04-19 00:10:27,716 : INFO : topic diff=0.351682, rho=0.313805\n",
      "2025-04-19 00:10:27,732 : INFO : PROGRESS: pass 1, at document #16000/16310\n",
      "2025-04-19 00:10:27,947 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:27,951 : INFO : topic #6 (0.125): 0.014*\"活動\" + 0.013*\"蘋果\" + 0.013*\"報名\" + 0.013*\"研究\" + 0.011*\"進行\" + 0.011*\"三星\" + 0.010*\"生產\" + 0.009*\"資料\" + 0.009*\"今年\" + 0.009*\"使用\"\n",
      "2025-04-19 00:10:27,951 : INFO : topic #1 (0.125): 0.032*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"砍除\" + 0.011*\"內容\" + 0.010*\"文字\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:10:27,952 : INFO : topic #2 (0.125): 0.024*\"工作\" + 0.011*\"公司\" + 0.010*\"覺得\" + 0.009*\"面試\" + 0.008*\"時間\" + 0.007*\"比較\" + 0.007*\"應該\" + 0.006*\"真的\" + 0.006*\"一下\" + 0.006*\"程式\"\n",
      "2025-04-19 00:10:27,953 : INFO : topic #4 (0.125): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:27,953 : INFO : topic #7 (0.125): 0.027*\"東京\" + 0.017*\"南港\" + 0.012*\"惠普\" + 0.011*\"工作\" + 0.009*\"三家\" + 0.009*\"投保\" + 0.009*\"給付\" + 0.009*\"展覽館\" + 0.007*\"規定\" + 0.007*\"店家\"\n",
      "2025-04-19 00:10:27,954 : INFO : topic diff=0.300320, rho=0.313805\n",
      "2025-04-19 00:10:28,062 : INFO : -8.528 per-word bound, 369.1 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:10:28,062 : INFO : PROGRESS: pass 1, at document #16310/16310\n",
      "2025-04-19 00:10:28,114 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:28,118 : INFO : topic #7 (0.125): 0.036*\"東京\" + 0.017*\"南港\" + 0.013*\"投保\" + 0.010*\"三家\" + 0.010*\"展覽館\" + 0.010*\"惠普\" + 0.009*\"工作\" + 0.007*\"給付\" + 0.006*\"規定\" + 0.006*\"店家\"\n",
      "2025-04-19 00:10:28,119 : INFO : topic #1 (0.125): 0.031*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.012*\"單位\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"文字\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:10:28,119 : INFO : topic #0 (0.125): 0.038*\"工作\" + 0.016*\"方式\" + 0.012*\"應徵\" + 0.011*\"小時\" + 0.010*\"內容\" + 0.010*\"聯絡\" + 0.009*\"工資\" + 0.009*\"推定\" + 0.009*\"單位\" + 0.009*\"依法\"\n",
      "2025-04-19 00:10:28,120 : INFO : topic #6 (0.125): 0.014*\"蘋果\" + 0.013*\"研究\" + 0.013*\"活動\" + 0.012*\"生產\" + 0.011*\"進行\" + 0.011*\"三星\" + 0.011*\"機器人\" + 0.011*\"報名\" + 0.009*\"今年\" + 0.009*\"資料\"\n",
      "2025-04-19 00:10:28,120 : INFO : topic #3 (0.125): 0.032*\"半導體\" + 0.025*\"中國\" + 0.025*\"表示\" + 0.024*\"晶片\" + 0.024*\"投資\" + 0.019*\"英特爾\" + 0.018*\"製程\" + 0.015*\"川普\" + 0.015*\"先進\" + 0.012*\"研發\"\n",
      "2025-04-19 00:10:28,121 : INFO : topic diff=0.299043, rho=0.313805\n",
      "2025-04-19 00:10:28,121 : INFO : PROGRESS: pass 2, at document #2000/16310\n",
      "2025-04-19 00:10:28,716 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:28,720 : INFO : topic #3 (0.125): 0.031*\"半導體\" + 0.025*\"中國\" + 0.024*\"表示\" + 0.024*\"晶片\" + 0.023*\"投資\" + 0.019*\"英特爾\" + 0.017*\"製程\" + 0.014*\"川普\" + 0.014*\"先進\" + 0.012*\"研發\"\n",
      "2025-04-19 00:10:28,721 : INFO : topic #6 (0.125): 0.020*\"報名\" + 0.020*\"活動\" + 0.012*\"電話\" + 0.011*\"進行\" + 0.011*\"研究\" + 0.011*\"台北市\" + 0.011*\"參與\" + 0.010*\"舉辦\" + 0.010*\"資料\" + 0.008*\"時間\"\n",
      "2025-04-19 00:10:28,721 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.008*\"台灣\" + 0.008*\"美國\" + 0.007*\"技術\" + 0.006*\"員工\" + 0.005*\"科技\" + 0.005*\"台積電\" + 0.005*\"工作\" + 0.005*\"工程師\" + 0.005*\"台積\"\n",
      "2025-04-19 00:10:28,722 : INFO : topic #0 (0.125): 0.041*\"工作\" + 0.020*\"方式\" + 0.012*\"工資\" + 0.011*\"依法\" + 0.011*\"小時\" + 0.011*\"應徵\" + 0.011*\"內容\" + 0.011*\"推定\" + 0.010*\"聯絡\" + 0.010*\"每日\"\n",
      "2025-04-19 00:10:28,722 : INFO : topic #1 (0.125): 0.032*\"工作\" + 0.015*\"方式\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"砍除\" + 0.011*\"內容\" + 0.011*\"文字\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:10:28,722 : INFO : topic diff=0.734008, rho=0.299409\n",
      "2025-04-19 00:10:28,723 : INFO : PROGRESS: pass 2, at document #4000/16310\n",
      "2025-04-19 00:10:29,334 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:29,338 : INFO : topic #2 (0.125): 0.023*\"工作\" + 0.010*\"公司\" + 0.009*\"時間\" + 0.009*\"面試\" + 0.007*\"覺得\" + 0.006*\"真的\" + 0.006*\"比較\" + 0.006*\"應該\" + 0.005*\"需要\" + 0.005*\"東西\"\n",
      "2025-04-19 00:10:29,338 : INFO : topic #1 (0.125): 0.032*\"工作\" + 0.014*\"方式\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.011*\"砍除\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:29,339 : INFO : topic #6 (0.125): 0.025*\"報名\" + 0.023*\"活動\" + 0.016*\"電話\" + 0.013*\"台北市\" + 0.012*\"舉辦\" + 0.012*\"資料\" + 0.011*\"進行\" + 0.011*\"車馬費\" + 0.011*\"參與\" + 0.011*\"人數\"\n",
      "2025-04-19 00:10:29,339 : INFO : topic #4 (0.125): 0.034*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:29,340 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.008*\"台灣\" + 0.007*\"美國\" + 0.007*\"技術\" + 0.006*\"員工\" + 0.005*\"科技\" + 0.005*\"台積電\" + 0.005*\"工作\" + 0.004*\"工程師\" + 0.004*\"台積\"\n",
      "2025-04-19 00:10:29,340 : INFO : topic diff=0.344892, rho=0.299409\n",
      "2025-04-19 00:10:29,341 : INFO : PROGRESS: pass 2, at document #6000/16310\n",
      "2025-04-19 00:10:29,797 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:29,801 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:29,802 : INFO : topic #7 (0.125): 0.021*\"勞基法\" + 0.017*\"南港\" + 0.014*\"訪員\" + 0.013*\"時間\" + 0.012*\"規定\" + 0.011*\"勞務\" + 0.011*\"展覽館\" + 0.010*\"投保\" + 0.010*\"南港區\" + 0.010*\"工作\"\n",
      "2025-04-19 00:10:29,802 : INFO : topic #5 (0.125): 0.015*\"公司\" + 0.007*\"台灣\" + 0.006*\"技術\" + 0.006*\"美國\" + 0.005*\"員工\" + 0.005*\"工作\" + 0.005*\"工程師\" + 0.005*\"科技\" + 0.005*\"問題\" + 0.004*\"台積電\"\n",
      "2025-04-19 00:10:29,802 : INFO : topic #2 (0.125): 0.024*\"工作\" + 0.010*\"公司\" + 0.010*\"時間\" + 0.009*\"面試\" + 0.007*\"覺得\" + 0.006*\"比較\" + 0.006*\"需要\" + 0.005*\"真的\" + 0.005*\"應該\" + 0.005*\"東西\"\n",
      "2025-04-19 00:10:29,803 : INFO : topic #6 (0.125): 0.028*\"報名\" + 0.025*\"活動\" + 0.018*\"電話\" + 0.015*\"台北市\" + 0.012*\"車馬費\" + 0.012*\"舉辦\" + 0.012*\"資料\" + 0.012*\"人數\" + 0.011*\"訪問\" + 0.011*\"進行\"\n",
      "2025-04-19 00:10:29,803 : INFO : topic diff=0.197971, rho=0.299409\n",
      "2025-04-19 00:10:29,804 : INFO : PROGRESS: pass 2, at document #8000/16310\n",
      "2025-04-19 00:10:30,045 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:30,049 : INFO : topic #7 (0.125): 0.034*\"勞基法\" + 0.017*\"南港\" + 0.014*\"時間\" + 0.012*\"給付\" + 0.011*\"訪員\" + 0.011*\"工作\" + 0.011*\"規定\" + 0.010*\"時數\" + 0.010*\"東京\" + 0.009*\"接案\"\n",
      "2025-04-19 00:10:30,050 : INFO : topic #3 (0.125): 0.030*\"半導體\" + 0.024*\"中國\" + 0.021*\"表示\" + 0.020*\"晶片\" + 0.020*\"投資\" + 0.016*\"英特爾\" + 0.015*\"製程\" + 0.012*\"先進\" + 0.012*\"川普\" + 0.012*\"研發\"\n",
      "2025-04-19 00:10:30,050 : INFO : topic #1 (0.125): 0.031*\"工作\" + 0.014*\"方式\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:30,051 : INFO : topic #2 (0.125): 0.027*\"工作\" + 0.016*\"面試\" + 0.012*\"公司\" + 0.011*\"覺得\" + 0.011*\"時間\" + 0.009*\"比較\" + 0.007*\"程式\" + 0.007*\"一下\" + 0.006*\"應該\" + 0.006*\"真的\"\n",
      "2025-04-19 00:10:30,051 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:30,052 : INFO : topic diff=0.318363, rho=0.299409\n",
      "2025-04-19 00:10:30,052 : INFO : PROGRESS: pass 2, at document #10000/16310\n",
      "2025-04-19 00:10:30,298 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:30,302 : INFO : topic #3 (0.125): 0.031*\"半導體\" + 0.024*\"中國\" + 0.021*\"表示\" + 0.020*\"投資\" + 0.019*\"晶片\" + 0.015*\"英特爾\" + 0.015*\"製程\" + 0.012*\"先進\" + 0.012*\"研發\" + 0.011*\"川普\"\n",
      "2025-04-19 00:10:30,303 : INFO : topic #0 (0.125): 0.049*\"工作\" + 0.025*\"方式\" + 0.016*\"小時\" + 0.013*\"每日\" + 0.013*\"時間\" + 0.012*\"依法\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.012*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:10:30,303 : INFO : topic #2 (0.125): 0.027*\"工作\" + 0.018*\"面試\" + 0.012*\"公司\" + 0.012*\"覺得\" + 0.011*\"時間\" + 0.010*\"比較\" + 0.008*\"程式\" + 0.007*\"一下\" + 0.007*\"應該\" + 0.007*\"真的\"\n",
      "2025-04-19 00:10:30,304 : INFO : topic #7 (0.125): 0.040*\"勞基法\" + 0.017*\"南港\" + 0.016*\"給付\" + 0.016*\"時數\" + 0.016*\"東京\" + 0.015*\"時間\" + 0.011*\"填寫\" + 0.011*\"工作日\" + 0.011*\"規定\" + 0.011*\"工作\"\n",
      "2025-04-19 00:10:30,304 : INFO : topic #1 (0.125): 0.031*\"工作\" + 0.014*\"方式\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:30,305 : INFO : topic diff=0.261058, rho=0.299409\n",
      "2025-04-19 00:10:30,305 : INFO : PROGRESS: pass 2, at document #12000/16310\n",
      "2025-04-19 00:10:30,517 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:30,521 : INFO : topic #7 (0.125): 0.039*\"勞基法\" + 0.024*\"東京\" + 0.018*\"南港\" + 0.016*\"給付\" + 0.016*\"時數\" + 0.015*\"時間\" + 0.013*\"填寫\" + 0.010*\"工作日\" + 0.010*\"店家\" + 0.010*\"工作\"\n",
      "2025-04-19 00:10:30,522 : INFO : topic #6 (0.125): 0.025*\"活動\" + 0.024*\"報名\" + 0.014*\"電話\" + 0.013*\"研究\" + 0.012*\"進行\" + 0.011*\"台北市\" + 0.011*\"資料\" + 0.011*\"舉辦\" + 0.011*\"參加\" + 0.010*\"參與\"\n",
      "2025-04-19 00:10:30,522 : INFO : topic #2 (0.125): 0.025*\"工作\" + 0.017*\"面試\" + 0.012*\"公司\" + 0.011*\"覺得\" + 0.010*\"時間\" + 0.010*\"比較\" + 0.008*\"程式\" + 0.007*\"真的\" + 0.007*\"應該\" + 0.007*\"一下\"\n",
      "2025-04-19 00:10:30,523 : INFO : topic #3 (0.125): 0.032*\"半導體\" + 0.028*\"晶片\" + 0.021*\"表示\" + 0.020*\"中國\" + 0.016*\"製程\" + 0.015*\"投資\" + 0.013*\"英特爾\" + 0.012*\"美國\" + 0.011*\"先進\" + 0.011*\"台灣\"\n",
      "2025-04-19 00:10:30,523 : INFO : topic #1 (0.125): 0.031*\"工作\" + 0.014*\"方式\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:30,524 : INFO : topic diff=0.316441, rho=0.299409\n",
      "2025-04-19 00:10:30,524 : INFO : PROGRESS: pass 2, at document #14000/16310\n",
      "2025-04-19 00:10:30,813 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:30,823 : INFO : topic #0 (0.125): 0.050*\"工作\" + 0.025*\"方式\" + 0.016*\"小時\" + 0.014*\"時間\" + 0.013*\"每日\" + 0.012*\"工資\" + 0.012*\"依法\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:10:30,825 : INFO : topic #5 (0.125): 0.015*\"公司\" + 0.006*\"台灣\" + 0.006*\"技術\" + 0.006*\"工程師\" + 0.005*\"員工\" + 0.005*\"工作\" + 0.005*\"開發\" + 0.005*\"問題\" + 0.004*\"目前\" + 0.004*\"科技\"\n",
      "2025-04-19 00:10:30,827 : INFO : topic #2 (0.125): 0.024*\"工作\" + 0.015*\"面試\" + 0.012*\"公司\" + 0.010*\"覺得\" + 0.009*\"時間\" + 0.009*\"比較\" + 0.007*\"真的\" + 0.007*\"應該\" + 0.007*\"程式\" + 0.006*\"一下\"\n",
      "2025-04-19 00:10:30,828 : INFO : topic #1 (0.125): 0.031*\"工作\" + 0.013*\"方式\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:30,829 : INFO : topic #3 (0.125): 0.029*\"晶片\" + 0.028*\"半導體\" + 0.023*\"表示\" + 0.023*\"中國\" + 0.016*\"英特爾\" + 0.015*\"台灣\" + 0.014*\"美國\" + 0.013*\"製程\" + 0.013*\"投資\" + 0.011*\"全球\"\n",
      "2025-04-19 00:10:30,830 : INFO : topic diff=0.296754, rho=0.299409\n",
      "2025-04-19 00:10:30,831 : INFO : PROGRESS: pass 2, at document #16000/16310\n",
      "2025-04-19 00:10:31,083 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:31,087 : INFO : topic #3 (0.125): 0.031*\"晶片\" + 0.026*\"半導體\" + 0.023*\"中國\" + 0.023*\"表示\" + 0.021*\"美國\" + 0.018*\"英特爾\" + 0.015*\"台灣\" + 0.013*\"製程\" + 0.012*\"投資\" + 0.011*\"全球\"\n",
      "2025-04-19 00:10:31,088 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.006*\"台灣\" + 0.006*\"技術\" + 0.006*\"員工\" + 0.005*\"工程師\" + 0.005*\"科技\" + 0.004*\"台積電\" + 0.004*\"工作\" + 0.004*\"目前\" + 0.004*\"問題\"\n",
      "2025-04-19 00:10:31,088 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:31,089 : INFO : topic #2 (0.125): 0.023*\"工作\" + 0.014*\"面試\" + 0.012*\"公司\" + 0.009*\"覺得\" + 0.009*\"時間\" + 0.009*\"比較\" + 0.007*\"真的\" + 0.007*\"應該\" + 0.007*\"主管\" + 0.006*\"一下\"\n",
      "2025-04-19 00:10:31,090 : INFO : topic #7 (0.125): 0.029*\"東京\" + 0.029*\"勞基法\" + 0.021*\"南港\" + 0.020*\"給付\" + 0.014*\"發放\" + 0.013*\"時數\" + 0.013*\"投保\" + 0.010*\"時間\" + 0.010*\"惠普\" + 0.010*\"規定\"\n",
      "2025-04-19 00:10:31,090 : INFO : topic diff=0.249445, rho=0.299409\n",
      "2025-04-19 00:10:31,159 : INFO : -8.407 per-word bound, 339.4 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:10:31,160 : INFO : PROGRESS: pass 2, at document #16310/16310\n",
      "2025-04-19 00:10:31,193 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:31,196 : INFO : topic #6 (0.125): 0.018*\"活動\" + 0.017*\"蘋果\" + 0.015*\"研究\" + 0.015*\"報名\" + 0.013*\"機器人\" + 0.011*\"進行\" + 0.011*\"問卷\" + 0.009*\"參與\" + 0.009*\"華為\" + 0.009*\"三星\"\n",
      "2025-04-19 00:10:31,197 : INFO : topic #7 (0.125): 0.036*\"東京\" + 0.023*\"勞基法\" + 0.020*\"南港\" + 0.020*\"時數\" + 0.018*\"給付\" + 0.015*\"投保\" + 0.013*\"發放\" + 0.010*\"展覽館\" + 0.009*\"三家\" + 0.008*\"時間\"\n",
      "2025-04-19 00:10:31,197 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:31,198 : INFO : topic #1 (0.125): 0.031*\"工作\" + 0.013*\"方式\" + 0.012*\"情形\" + 0.011*\"砍除\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.010*\"聯絡\" + 0.010*\"內容\"\n",
      "2025-04-19 00:10:31,198 : INFO : topic #3 (0.125): 0.031*\"晶片\" + 0.030*\"美國\" + 0.023*\"半導體\" + 0.022*\"中國\" + 0.021*\"表示\" + 0.019*\"投資\" + 0.018*\"英特爾\" + 0.018*\"台灣\" + 0.012*\"製程\" + 0.012*\"先進\"\n",
      "2025-04-19 00:10:31,199 : INFO : topic diff=0.251512, rho=0.299409\n",
      "2025-04-19 00:10:31,199 : INFO : PROGRESS: pass 3, at document #2000/16310\n",
      "2025-04-19 00:10:31,825 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:31,829 : INFO : topic #0 (0.125): 0.050*\"工作\" + 0.026*\"方式\" + 0.017*\"小時\" + 0.015*\"工資\" + 0.015*\"時間\" + 0.014*\"依法\" + 0.013*\"每日\" + 0.013*\"推定\" + 0.012*\"內容\" + 0.012*\"休息\"\n",
      "2025-04-19 00:10:31,830 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:31,830 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.007*\"技術\" + 0.006*\"台灣\" + 0.006*\"員工\" + 0.005*\"科技\" + 0.005*\"台積電\" + 0.005*\"台積\" + 0.005*\"工程師\" + 0.004*\"報導\" + 0.004*\"工作\"\n",
      "2025-04-19 00:10:31,831 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.012*\"方式\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"砍除\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.010*\"聯絡\" + 0.010*\"空白\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:31,831 : INFO : topic #6 (0.125): 0.024*\"報名\" + 0.023*\"活動\" + 0.015*\"電話\" + 0.012*\"台北市\" + 0.012*\"舉辦\" + 0.012*\"研究\" + 0.012*\"參與\" + 0.011*\"進行\" + 0.010*\"資料\" + 0.010*\"人數\"\n",
      "2025-04-19 00:10:31,832 : INFO : topic diff=0.686165, rho=0.286829\n",
      "2025-04-19 00:10:31,832 : INFO : PROGRESS: pass 3, at document #4000/16310\n",
      "2025-04-19 00:10:32,373 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:32,377 : INFO : topic #6 (0.125): 0.027*\"報名\" + 0.026*\"活動\" + 0.018*\"電話\" + 0.014*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"車馬費\" + 0.012*\"人數\" + 0.012*\"資料\" + 0.011*\"參與\" + 0.011*\"進行\"\n",
      "2025-04-19 00:10:32,378 : INFO : topic #2 (0.125): 0.021*\"工作\" + 0.012*\"面試\" + 0.011*\"公司\" + 0.009*\"時間\" + 0.007*\"覺得\" + 0.007*\"比較\" + 0.007*\"真的\" + 0.006*\"知道\" + 0.006*\"需要\" + 0.006*\"應該\"\n",
      "2025-04-19 00:10:32,379 : INFO : topic #0 (0.125): 0.049*\"工作\" + 0.026*\"方式\" + 0.017*\"小時\" + 0.015*\"工資\" + 0.015*\"時間\" + 0.014*\"推定\" + 0.014*\"依法\" + 0.013*\"每日\" + 0.012*\"單位\" + 0.012*\"內容\"\n",
      "2025-04-19 00:10:32,379 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.007*\"技術\" + 0.006*\"台灣\" + 0.006*\"員工\" + 0.005*\"科技\" + 0.005*\"台積電\" + 0.005*\"台積\" + 0.005*\"工程師\" + 0.004*\"報導\" + 0.004*\"工作\"\n",
      "2025-04-19 00:10:32,380 : INFO : topic #1 (0.125): 0.031*\"工作\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.011*\"文字\" + 0.011*\"空白\" + 0.011*\"推定\" + 0.010*\"聯絡\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:10:32,380 : INFO : topic diff=0.326209, rho=0.286829\n",
      "2025-04-19 00:10:32,381 : INFO : PROGRESS: pass 3, at document #6000/16310\n",
      "2025-04-19 00:10:32,862 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:32,865 : INFO : topic #6 (0.125): 0.030*\"報名\" + 0.027*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.013*\"車馬費\" + 0.013*\"舉辦\" + 0.012*\"人數\" + 0.012*\"資料\" + 0.012*\"訪問\" + 0.011*\"參與\"\n",
      "2025-04-19 00:10:32,866 : INFO : topic #2 (0.125): 0.021*\"工作\" + 0.012*\"面試\" + 0.012*\"公司\" + 0.009*\"時間\" + 0.007*\"比較\" + 0.007*\"覺得\" + 0.007*\"需要\" + 0.006*\"知道\" + 0.006*\"真的\" + 0.006*\"應該\"\n",
      "2025-04-19 00:10:32,866 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:32,867 : INFO : topic #0 (0.125): 0.050*\"工作\" + 0.027*\"方式\" + 0.017*\"小時\" + 0.015*\"時間\" + 0.015*\"工資\" + 0.014*\"依法\" + 0.014*\"推定\" + 0.014*\"每日\" + 0.012*\"內容\" + 0.012*\"休息\"\n",
      "2025-04-19 00:10:32,868 : INFO : topic #7 (0.125): 0.028*\"勞基法\" + 0.018*\"南港\" + 0.016*\"勞務\" + 0.016*\"發放\" + 0.014*\"時間\" + 0.012*\"報名者\" + 0.012*\"規定\" + 0.012*\"訪員\" + 0.011*\"投保\" + 0.010*\"展覽館\"\n",
      "2025-04-19 00:10:32,868 : INFO : topic diff=0.182729, rho=0.286829\n",
      "2025-04-19 00:10:32,868 : INFO : PROGRESS: pass 3, at document #8000/16310\n",
      "2025-04-19 00:10:33,097 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:33,101 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"文字\" + 0.011*\"空白\" + 0.010*\"推定\" + 0.010*\"資訊\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:10:33,101 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:33,102 : INFO : topic #0 (0.125): 0.053*\"工作\" + 0.028*\"方式\" + 0.019*\"小時\" + 0.016*\"時間\" + 0.015*\"每日\" + 0.014*\"工資\" + 0.013*\"依法\" + 0.013*\"推定\" + 0.013*\"內容\" + 0.012*\"休息\"\n",
      "2025-04-19 00:10:33,102 : INFO : topic #3 (0.125): 0.028*\"晶片\" + 0.027*\"美國\" + 0.022*\"中國\" + 0.022*\"半導體\" + 0.019*\"表示\" + 0.018*\"投資\" + 0.017*\"台灣\" + 0.016*\"英特爾\" + 0.011*\"製程\" + 0.011*\"先進\"\n",
      "2025-04-19 00:10:33,103 : INFO : topic #7 (0.125): 0.047*\"勞基法\" + 0.017*\"時間\" + 0.017*\"加班費\" + 0.016*\"南港\" + 0.016*\"填寫\" + 0.016*\"時數\" + 0.015*\"給付\" + 0.013*\"發放\" + 0.012*\"超過\" + 0.012*\"勞務\"\n",
      "2025-04-19 00:10:33,103 : INFO : topic diff=0.291625, rho=0.286829\n",
      "2025-04-19 00:10:33,104 : INFO : PROGRESS: pass 3, at document #10000/16310\n",
      "2025-04-19 00:10:33,346 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:33,350 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"文字\" + 0.011*\"空白\" + 0.010*\"推定\" + 0.010*\"資訊\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:10:33,351 : INFO : topic #7 (0.125): 0.052*\"勞基法\" + 0.030*\"加班費\" + 0.022*\"填寫\" + 0.021*\"時數\" + 0.020*\"超過\" + 0.019*\"時間\" + 0.017*\"給付\" + 0.015*\"南港\" + 0.013*\"符合\" + 0.013*\"東京\"\n",
      "2025-04-19 00:10:33,351 : INFO : topic #0 (0.125): 0.055*\"工作\" + 0.029*\"方式\" + 0.020*\"小時\" + 0.017*\"時間\" + 0.015*\"每日\" + 0.013*\"工資\" + 0.013*\"依法\" + 0.013*\"內容\" + 0.012*\"推定\" + 0.012*\"休息\"\n",
      "2025-04-19 00:10:33,352 : INFO : topic #3 (0.125): 0.027*\"美國\" + 0.026*\"晶片\" + 0.023*\"中國\" + 0.022*\"半導體\" + 0.019*\"表示\" + 0.018*\"台灣\" + 0.017*\"投資\" + 0.015*\"英特爾\" + 0.011*\"製程\" + 0.011*\"先進\"\n",
      "2025-04-19 00:10:33,352 : INFO : topic #5 (0.125): 0.017*\"公司\" + 0.008*\"工程師\" + 0.007*\"開發\" + 0.007*\"技術\" + 0.006*\"團隊\" + 0.005*\"產品\" + 0.005*\"目前\" + 0.005*\"台灣\" + 0.005*\"問題\" + 0.005*\"工作\"\n",
      "2025-04-19 00:10:33,353 : INFO : topic diff=0.238144, rho=0.286829\n",
      "2025-04-19 00:10:33,353 : INFO : PROGRESS: pass 3, at document #12000/16310\n",
      "2025-04-19 00:10:33,547 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:33,551 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"文字\" + 0.012*\"方式\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:10:33,551 : INFO : topic #2 (0.125): 0.023*\"工作\" + 0.019*\"面試\" + 0.014*\"公司\" + 0.011*\"比較\" + 0.010*\"覺得\" + 0.009*\"時間\" + 0.009*\"問題\" + 0.008*\"真的\" + 0.008*\"知道\" + 0.007*\"程式\"\n",
      "2025-04-19 00:10:33,552 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:33,552 : INFO : topic #6 (0.125): 0.027*\"活動\" + 0.027*\"報名\" + 0.015*\"電話\" + 0.013*\"研究\" + 0.012*\"台北市\" + 0.012*\"舉辦\" + 0.011*\"資料\" + 0.011*\"進行\" + 0.011*\"參加\" + 0.011*\"參與\"\n",
      "2025-04-19 00:10:33,553 : INFO : topic #0 (0.125): 0.056*\"工作\" + 0.029*\"方式\" + 0.020*\"小時\" + 0.018*\"時間\" + 0.015*\"每日\" + 0.013*\"工資\" + 0.012*\"內容\" + 0.012*\"依法\" + 0.012*\"聯絡\" + 0.012*\"休息\"\n",
      "2025-04-19 00:10:33,553 : INFO : topic diff=0.277253, rho=0.286829\n",
      "2025-04-19 00:10:33,554 : INFO : PROGRESS: pass 3, at document #14000/16310\n",
      "2025-04-19 00:10:33,833 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:33,837 : INFO : topic #0 (0.125): 0.056*\"工作\" + 0.029*\"方式\" + 0.020*\"小時\" + 0.017*\"時間\" + 0.015*\"每日\" + 0.013*\"工資\" + 0.012*\"內容\" + 0.012*\"依法\" + 0.012*\"聯絡\" + 0.012*\"休息\"\n",
      "2025-04-19 00:10:33,838 : INFO : topic #7 (0.125): 0.042*\"勞基法\" + 0.034*\"加班費\" + 0.021*\"超過\" + 0.020*\"填寫\" + 0.020*\"時數\" + 0.019*\"東京\" + 0.017*\"時間\" + 0.017*\"發放\" + 0.016*\"給付\" + 0.016*\"南港\"\n",
      "2025-04-19 00:10:33,841 : INFO : topic #3 (0.125): 0.028*\"晶片\" + 0.023*\"半導體\" + 0.022*\"台灣\" + 0.021*\"美國\" + 0.021*\"表示\" + 0.020*\"中國\" + 0.014*\"英特爾\" + 0.012*\"產業\" + 0.012*\"全球\" + 0.011*\"投資\"\n",
      "2025-04-19 00:10:33,842 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.006*\"技術\" + 0.006*\"工程師\" + 0.005*\"員工\" + 0.005*\"台灣\" + 0.005*\"開發\" + 0.005*\"科技\" + 0.004*\"產品\" + 0.004*\"目前\" + 0.004*\"工作\"\n",
      "2025-04-19 00:10:33,843 : INFO : topic #6 (0.125): 0.025*\"活動\" + 0.023*\"報名\" + 0.014*\"研究\" + 0.013*\"電話\" + 0.011*\"進行\" + 0.011*\"問卷\" + 0.010*\"舉辦\" + 0.010*\"參與\" + 0.010*\"台北市\" + 0.010*\"資料\"\n",
      "2025-04-19 00:10:33,844 : INFO : topic diff=0.271297, rho=0.286829\n",
      "2025-04-19 00:10:33,845 : INFO : PROGRESS: pass 3, at document #16000/16310\n",
      "2025-04-19 00:10:34,063 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:34,067 : INFO : topic #7 (0.125): 0.039*\"勞基法\" + 0.030*\"加班費\" + 0.023*\"東京\" + 0.022*\"發放\" + 0.020*\"超過\" + 0.019*\"給付\" + 0.018*\"南港\" + 0.017*\"時數\" + 0.015*\"填寫\" + 0.015*\"時間\"\n",
      "2025-04-19 00:10:34,068 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:10:34,069 : INFO : topic #3 (0.125): 0.029*\"晶片\" + 0.025*\"美國\" + 0.022*\"半導體\" + 0.021*\"中國\" + 0.021*\"表示\" + 0.020*\"台灣\" + 0.016*\"英特爾\" + 0.012*\"產業\" + 0.011*\"全球\" + 0.011*\"投資\"\n",
      "2025-04-19 00:10:34,069 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.006*\"技術\" + 0.006*\"員工\" + 0.005*\"工程師\" + 0.005*\"台灣\" + 0.005*\"科技\" + 0.005*\"台積電\" + 0.004*\"開發\" + 0.004*\"報導\" + 0.004*\"目前\"\n",
      "2025-04-19 00:10:34,070 : INFO : topic #2 (0.125): 0.022*\"工作\" + 0.015*\"面試\" + 0.014*\"公司\" + 0.009*\"比較\" + 0.009*\"覺得\" + 0.008*\"時間\" + 0.008*\"問題\" + 0.008*\"主管\" + 0.008*\"真的\" + 0.007*\"知道\"\n",
      "2025-04-19 00:10:34,070 : INFO : topic diff=0.227152, rho=0.286829\n",
      "2025-04-19 00:10:34,170 : INFO : -8.360 per-word bound, 328.5 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:10:34,171 : INFO : PROGRESS: pass 3, at document #16310/16310\n",
      "2025-04-19 00:10:34,215 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:34,219 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.008*\"技術\" + 0.006*\"員工\" + 0.006*\"台積電\" + 0.006*\"科技\" + 0.005*\"台積\" + 0.005*\"工程師\" + 0.005*\"台灣\" + 0.005*\"報導\" + 0.004*\"產品\"\n",
      "2025-04-19 00:10:34,220 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.011*\"資訊\" + 0.011*\"聯絡\"\n",
      "2025-04-19 00:10:34,220 : INFO : topic #0 (0.125): 0.058*\"工作\" + 0.027*\"方式\" + 0.022*\"小時\" + 0.017*\"時間\" + 0.015*\"工時\" + 0.015*\"每日\" + 0.012*\"工資\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.012*\"休息\"\n",
      "2025-04-19 00:10:34,221 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"文字\" + 0.012*\"砍除\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:10:34,221 : INFO : topic #2 (0.125): 0.020*\"工作\" + 0.014*\"公司\" + 0.013*\"面試\" + 0.009*\"知道\" + 0.009*\"真的\" + 0.008*\"覺得\" + 0.008*\"比較\" + 0.008*\"問題\" + 0.008*\"時間\" + 0.007*\"應該\"\n",
      "2025-04-19 00:10:34,222 : INFO : topic diff=0.227475, rho=0.286829\n",
      "2025-04-19 00:10:34,222 : INFO : PROGRESS: pass 4, at document #2000/16310\n",
      "2025-04-19 00:10:34,759 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:34,763 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"文字\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"推定\" + 0.010*\"資訊\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:10:34,763 : INFO : topic #0 (0.125): 0.054*\"工作\" + 0.028*\"方式\" + 0.019*\"小時\" + 0.016*\"時間\" + 0.015*\"工資\" + 0.014*\"每日\" + 0.014*\"依法\" + 0.013*\"推定\" + 0.013*\"休息\" + 0.012*\"內容\"\n",
      "2025-04-19 00:10:34,764 : INFO : topic #2 (0.125): 0.020*\"工作\" + 0.014*\"公司\" + 0.013*\"面試\" + 0.008*\"知道\" + 0.008*\"真的\" + 0.008*\"比較\" + 0.008*\"時間\" + 0.008*\"覺得\" + 0.007*\"問題\" + 0.007*\"主管\"\n",
      "2025-04-19 00:10:34,764 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.008*\"技術\" + 0.006*\"員工\" + 0.006*\"台積電\" + 0.006*\"科技\" + 0.005*\"台積\" + 0.005*\"台灣\" + 0.005*\"工程師\" + 0.004*\"報導\" + 0.004*\"產品\"\n",
      "2025-04-19 00:10:34,765 : INFO : topic #6 (0.125): 0.025*\"報名\" + 0.025*\"活動\" + 0.016*\"電話\" + 0.013*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"研究\" + 0.012*\"參與\" + 0.011*\"進行\" + 0.010*\"人數\" + 0.010*\"資料\"\n",
      "2025-04-19 00:10:34,765 : INFO : topic diff=0.640666, rho=0.275711\n",
      "2025-04-19 00:10:34,765 : INFO : PROGRESS: pass 4, at document #4000/16310\n",
      "2025-04-19 00:10:35,333 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:35,337 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.012*\"文字\" + 0.011*\"空白\" + 0.011*\"方式\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:10:35,337 : INFO : topic #6 (0.125): 0.028*\"報名\" + 0.027*\"活動\" + 0.019*\"電話\" + 0.014*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"車馬費\" + 0.012*\"人數\" + 0.012*\"參與\" + 0.011*\"資料\" + 0.011*\"進行\"\n",
      "2025-04-19 00:10:35,338 : INFO : topic #3 (0.125): 0.032*\"美國\" + 0.028*\"晶片\" + 0.021*\"台灣\" + 0.020*\"中國\" + 0.019*\"半導體\" + 0.019*\"表示\" + 0.017*\"投資\" + 0.015*\"英特爾\" + 0.011*\"產業\" + 0.011*\"全球\"\n",
      "2025-04-19 00:10:35,338 : INFO : topic #2 (0.125): 0.020*\"工作\" + 0.013*\"公司\" + 0.013*\"面試\" + 0.008*\"時間\" + 0.008*\"知道\" + 0.008*\"比較\" + 0.007*\"問題\" + 0.007*\"真的\" + 0.007*\"覺得\" + 0.006*\"主管\"\n",
      "2025-04-19 00:10:35,339 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:35,339 : INFO : topic diff=0.310708, rho=0.275711\n",
      "2025-04-19 00:10:35,339 : INFO : PROGRESS: pass 4, at document #6000/16310\n",
      "2025-04-19 00:10:35,799 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:35,803 : INFO : topic #0 (0.125): 0.052*\"工作\" + 0.028*\"方式\" + 0.018*\"小時\" + 0.016*\"時間\" + 0.015*\"工資\" + 0.015*\"每日\" + 0.015*\"推定\" + 0.014*\"依法\" + 0.013*\"內容\" + 0.013*\"休息\"\n",
      "2025-04-19 00:10:35,804 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:35,804 : INFO : topic #3 (0.125): 0.031*\"美國\" + 0.028*\"晶片\" + 0.021*\"台灣\" + 0.020*\"中國\" + 0.019*\"半導體\" + 0.018*\"表示\" + 0.017*\"投資\" + 0.015*\"英特爾\" + 0.011*\"產業\" + 0.010*\"全球\"\n",
      "2025-04-19 00:10:35,805 : INFO : topic #6 (0.125): 0.030*\"報名\" + 0.028*\"活動\" + 0.020*\"電話\" + 0.016*\"台北市\" + 0.013*\"車馬費\" + 0.013*\"舉辦\" + 0.013*\"人數\" + 0.012*\"資料\" + 0.012*\"訪問\" + 0.011*\"參與\"\n",
      "2025-04-19 00:10:35,805 : INFO : topic #5 (0.125): 0.015*\"公司\" + 0.007*\"技術\" + 0.006*\"員工\" + 0.005*\"科技\" + 0.005*\"工程師\" + 0.005*\"產品\" + 0.005*\"台灣\" + 0.005*\"台積電\" + 0.004*\"開發\" + 0.004*\"台積\"\n",
      "2025-04-19 00:10:35,806 : INFO : topic diff=0.175712, rho=0.275711\n",
      "2025-04-19 00:10:35,806 : INFO : PROGRESS: pass 4, at document #8000/16310\n",
      "2025-04-19 00:10:36,057 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:36,060 : INFO : topic #3 (0.125): 0.032*\"美國\" + 0.027*\"晶片\" + 0.022*\"台灣\" + 0.021*\"中國\" + 0.019*\"半導體\" + 0.018*\"表示\" + 0.016*\"投資\" + 0.014*\"英特爾\" + 0.011*\"產業\" + 0.011*\"全球\"\n",
      "2025-04-19 00:10:36,061 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"第一項\"\n",
      "2025-04-19 00:10:36,061 : INFO : topic #6 (0.125): 0.029*\"報名\" + 0.027*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"車馬費\" + 0.012*\"人數\" + 0.012*\"資料\" + 0.011*\"參與\" + 0.011*\"參加\"\n",
      "2025-04-19 00:10:36,062 : INFO : topic #7 (0.125): 0.051*\"勞基法\" + 0.047*\"加班費\" + 0.021*\"時間\" + 0.021*\"填寫\" + 0.019*\"超過\" + 0.016*\"計算\" + 0.016*\"發放\" + 0.015*\"時數\" + 0.014*\"南港\" + 0.013*\"給付\"\n",
      "2025-04-19 00:10:36,063 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.012*\"文字\" + 0.012*\"空白\" + 0.011*\"方式\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"推定\"\n",
      "2025-04-19 00:10:36,063 : INFO : topic diff=0.275699, rho=0.275711\n",
      "2025-04-19 00:10:36,063 : INFO : PROGRESS: pass 4, at document #10000/16310\n",
      "2025-04-19 00:10:36,262 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:36,266 : INFO : topic #5 (0.125): 0.016*\"公司\" + 0.008*\"開發\" + 0.008*\"技術\" + 0.007*\"工程師\" + 0.006*\"團隊\" + 0.006*\"產品\" + 0.005*\"台灣\" + 0.005*\"員工\" + 0.005*\"軟體\" + 0.005*\"目前\"\n",
      "2025-04-19 00:10:36,266 : INFO : topic #0 (0.125): 0.057*\"工作\" + 0.030*\"方式\" + 0.020*\"小時\" + 0.018*\"時間\" + 0.015*\"每日\" + 0.013*\"工資\" + 0.013*\"內容\" + 0.013*\"依法\" + 0.012*\"休息\" + 0.012*\"推定\"\n",
      "2025-04-19 00:10:36,267 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"第一項\"\n",
      "2025-04-19 00:10:36,267 : INFO : topic #6 (0.125): 0.029*\"報名\" + 0.027*\"活動\" + 0.018*\"電話\" + 0.014*\"台北市\" + 0.012*\"舉辦\" + 0.012*\"資料\" + 0.012*\"研究\" + 0.012*\"人數\" + 0.011*\"車馬費\" + 0.011*\"時間\"\n",
      "2025-04-19 00:10:36,268 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.012*\"文字\" + 0.011*\"空白\" + 0.011*\"方式\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"推定\"\n",
      "2025-04-19 00:10:36,268 : INFO : topic diff=0.222152, rho=0.275711\n",
      "2025-04-19 00:10:36,268 : INFO : PROGRESS: pass 4, at document #12000/16310\n",
      "2025-04-19 00:10:36,496 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:36,500 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.012*\"文字\" + 0.011*\"空白\" + 0.011*\"方式\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"推定\"\n",
      "2025-04-19 00:10:36,500 : INFO : topic #3 (0.125): 0.027*\"晶片\" + 0.023*\"美國\" + 0.023*\"半導體\" + 0.022*\"台灣\" + 0.018*\"表示\" + 0.017*\"中國\" + 0.012*\"投資\" + 0.012*\"產業\" + 0.012*\"製程\" + 0.011*\"英特爾\"\n",
      "2025-04-19 00:10:36,501 : INFO : topic #7 (0.125): 0.064*\"加班費\" + 0.053*\"勞基法\" + 0.028*\"填寫\" + 0.027*\"超過\" + 0.024*\"時間\" + 0.022*\"計算\" + 0.018*\"薪資\" + 0.018*\"時數\" + 0.017*\"發放\" + 0.016*\"符合\"\n",
      "2025-04-19 00:10:36,502 : INFO : topic #5 (0.125): 0.015*\"公司\" + 0.007*\"技術\" + 0.007*\"開發\" + 0.007*\"工程師\" + 0.005*\"產品\" + 0.005*\"團隊\" + 0.005*\"員工\" + 0.005*\"台灣\" + 0.004*\"軟體\" + 0.004*\"目前\"\n",
      "2025-04-19 00:10:36,502 : INFO : topic #2 (0.125): 0.022*\"工作\" + 0.018*\"面試\" + 0.015*\"公司\" + 0.011*\"問題\" + 0.010*\"比較\" + 0.010*\"覺得\" + 0.008*\"時間\" + 0.008*\"知道\" + 0.007*\"真的\" + 0.007*\"一些\"\n",
      "2025-04-19 00:10:36,502 : INFO : topic diff=0.254074, rho=0.275711\n",
      "2025-04-19 00:10:36,503 : INFO : PROGRESS: pass 4, at document #14000/16310\n",
      "2025-04-19 00:10:36,781 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:36,787 : INFO : topic #1 (0.125): 0.030*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.012*\"文字\" + 0.011*\"空白\" + 0.011*\"方式\" + 0.010*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"推定\"\n",
      "2025-04-19 00:10:36,788 : INFO : topic #3 (0.125): 0.026*\"晶片\" + 0.024*\"台灣\" + 0.022*\"美國\" + 0.021*\"半導體\" + 0.019*\"表示\" + 0.019*\"中國\" + 0.013*\"英特爾\" + 0.012*\"產業\" + 0.012*\"全球\" + 0.011*\"投資\"\n",
      "2025-04-19 00:10:36,792 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"資訊\" + 0.011*\"單位\"\n",
      "2025-04-19 00:10:36,793 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.007*\"技術\" + 0.006*\"工程師\" + 0.006*\"員工\" + 0.005*\"開發\" + 0.005*\"台灣\" + 0.005*\"科技\" + 0.005*\"產品\" + 0.004*\"團隊\" + 0.004*\"目前\"\n",
      "2025-04-19 00:10:36,794 : INFO : topic #0 (0.125): 0.059*\"工作\" + 0.029*\"方式\" + 0.020*\"小時\" + 0.018*\"時間\" + 0.015*\"每日\" + 0.013*\"工資\" + 0.013*\"內容\" + 0.012*\"依法\" + 0.012*\"休息\" + 0.012*\"聯絡\"\n",
      "2025-04-19 00:10:36,795 : INFO : topic diff=0.256628, rho=0.275711\n",
      "2025-04-19 00:10:36,795 : INFO : PROGRESS: pass 4, at document #16000/16310\n",
      "2025-04-19 00:10:37,032 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:37,035 : INFO : topic #6 (0.125): 0.025*\"活動\" + 0.023*\"報名\" + 0.015*\"研究\" + 0.012*\"電話\" + 0.011*\"問卷\" + 0.011*\"進行\" + 0.010*\"舉辦\" + 0.010*\"參加\" + 0.010*\"參與\" + 0.010*\"台北市\"\n",
      "2025-04-19 00:10:37,036 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.007*\"技術\" + 0.006*\"員工\" + 0.005*\"工程師\" + 0.005*\"科技\" + 0.005*\"台積電\" + 0.005*\"台灣\" + 0.004*\"開發\" + 0.004*\"報導\" + 0.004*\"產品\"\n",
      "2025-04-19 00:10:37,036 : INFO : topic #4 (0.125): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:37,037 : INFO : topic #7 (0.125): 0.057*\"加班費\" + 0.042*\"勞基法\" + 0.025*\"發放\" + 0.024*\"超過\" + 0.020*\"填寫\" + 0.019*\"時間\" + 0.019*\"東京\" + 0.019*\"計算\" + 0.017*\"薪資\" + 0.016*\"時數\"\n",
      "2025-04-19 00:10:37,037 : INFO : topic #0 (0.125): 0.058*\"工作\" + 0.029*\"方式\" + 0.021*\"小時\" + 0.018*\"時間\" + 0.015*\"每日\" + 0.013*\"工資\" + 0.012*\"內容\" + 0.012*\"工時\" + 0.012*\"依法\" + 0.012*\"休息\"\n",
      "2025-04-19 00:10:37,038 : INFO : topic diff=0.215227, rho=0.275711\n",
      "2025-04-19 00:10:37,115 : INFO : -8.339 per-word bound, 323.8 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:10:37,116 : INFO : PROGRESS: pass 4, at document #16310/16310\n",
      "2025-04-19 00:10:37,146 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:37,150 : INFO : topic #6 (0.125): 0.023*\"活動\" + 0.020*\"報名\" + 0.017*\"研究\" + 0.013*\"問卷\" + 0.010*\"進行\" + 0.010*\"華為\" + 0.010*\"時間\" + 0.010*\"電話\" + 0.010*\"參與\" + 0.010*\"台北市\"\n",
      "2025-04-19 00:10:37,150 : INFO : topic #5 (0.125): 0.014*\"公司\" + 0.008*\"技術\" + 0.007*\"員工\" + 0.006*\"台積電\" + 0.006*\"科技\" + 0.005*\"台積\" + 0.005*\"工程師\" + 0.005*\"報導\" + 0.004*\"台灣\" + 0.004*\"產品\"\n",
      "2025-04-19 00:10:37,151 : INFO : topic #1 (0.125): 0.029*\"工作\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.012*\"文字\" + 0.012*\"砍除\" + 0.011*\"空白\" + 0.011*\"方式\" + 0.010*\"資訊\" + 0.010*\"分類\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:10:37,151 : INFO : topic #3 (0.125): 0.034*\"美國\" + 0.028*\"晶片\" + 0.023*\"台灣\" + 0.020*\"中國\" + 0.019*\"半導體\" + 0.019*\"表示\" + 0.016*\"投資\" + 0.015*\"英特爾\" + 0.011*\"產業\" + 0.011*\"全球\"\n",
      "2025-04-19 00:10:37,152 : INFO : topic #2 (0.125): 0.019*\"工作\" + 0.015*\"公司\" + 0.013*\"面試\" + 0.009*\"知道\" + 0.009*\"問題\" + 0.008*\"真的\" + 0.008*\"比較\" + 0.008*\"覺得\" + 0.007*\"時間\" + 0.007*\"主管\"\n",
      "2025-04-19 00:10:37,152 : INFO : topic diff=0.214082, rho=0.275711\n",
      "2025-04-19 00:10:37,153 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel<num_terms=23261, num_topics=8, decay=0.5, chunksize=2000> in 15.70s', 'datetime': '2025-04-19T00:10:37.153168', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n",
      "2025-04-19 00:10:42,243 : INFO : -7.035 per-word bound, 131.1 perplexity estimate based on a held-out corpus of 16310 documents with 3460358 words\n",
      "2025-04-19 00:10:42,246 : INFO : using ParallelWordOccurrenceAccumulator<processes=7, batch_size=64> to estimate probabilities from sliding windows\n",
      "2025-04-19 00:10:46,154 : INFO : 1 batches submitted to accumulate stats from 64 documents (22660 virtual)\n",
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      "2025-04-19 00:10:50,385 : INFO : 7 accumulators retrieved from output queue\n",
      "2025-04-19 00:10:50,395 : INFO : accumulated word occurrence stats for 3451622 virtual documents\n",
      "2025-04-19 00:10:50,518 : INFO : using symmetric alpha at 0.1111111111111111\n",
      "2025-04-19 00:10:50,519 : INFO : using symmetric eta at 0.1111111111111111\n",
      "2025-04-19 00:10:50,520 : INFO : using serial LDA version on this node\n",
      "2025-04-19 00:10:50,528 : INFO : running online (multi-pass) LDA training, 9 topics, 5 passes over the supplied corpus of 16310 documents, updating model once every 2000 documents, evaluating perplexity every 16310 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2025-04-19 00:10:50,529 : INFO : PROGRESS: pass 0, at document #2000/16310\n",
      "2025-04-19 00:10:51,191 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:51,195 : INFO : topic #5 (0.111): 0.018*\"工作\" + 0.013*\"方式\" + 0.011*\"空白\" + 0.010*\"聯絡\" + 0.010*\"應徵\" + 0.009*\"標題\" + 0.009*\"小時\" + 0.008*\"內容\" + 0.008*\"分類\" + 0.008*\"資訊\"\n",
      "2025-04-19 00:10:51,196 : INFO : topic #4 (0.111): 0.039*\"工作\" + 0.017*\"推定\" + 0.013*\"空白\" + 0.012*\"方式\" + 0.011*\"聯絡\" + 0.010*\"單位\" + 0.010*\"第一項\" + 0.010*\"聯絡人\" + 0.009*\"內容\" + 0.009*\"承攬\"\n",
      "2025-04-19 00:10:51,196 : INFO : topic #2 (0.111): 0.041*\"工作\" + 0.013*\"內容\" + 0.012*\"工資\" + 0.012*\"推定\" + 0.012*\"方式\" + 0.012*\"應徵\" + 0.011*\"小時\" + 0.010*\"砍除\" + 0.010*\"聯絡\" + 0.010*\"情形\"\n",
      "2025-04-19 00:10:51,197 : INFO : topic #7 (0.111): 0.026*\"工作\" + 0.014*\"空白\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"砍除\" + 0.011*\"內容\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.010*\"情形\" + 0.010*\"方式\"\n",
      "2025-04-19 00:10:51,197 : INFO : topic #0 (0.111): 0.030*\"工作\" + 0.015*\"方式\" + 0.014*\"應徵\" + 0.012*\"砍除\" + 0.012*\"推定\" + 0.012*\"單位\" + 0.012*\"空白\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.009*\"第一項\"\n",
      "2025-04-19 00:10:51,198 : INFO : topic diff=8.691695, rho=1.000000\n",
      "2025-04-19 00:10:51,199 : INFO : PROGRESS: pass 0, at document #4000/16310\n",
      "2025-04-19 00:10:51,853 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:51,857 : INFO : topic #5 (0.111): 0.019*\"工作\" + 0.017*\"方式\" + 0.009*\"應徵\" + 0.009*\"依法\" + 0.009*\"通知\" + 0.009*\"聯絡\" + 0.009*\"時間\" + 0.008*\"標題\" + 0.008*\"內容\" + 0.007*\"小時\"\n",
      "2025-04-19 00:10:51,858 : INFO : topic #7 (0.111): 0.026*\"工作\" + 0.013*\"空白\" + 0.011*\"推定\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"砍除\" + 0.010*\"資訊\" + 0.010*\"第一項\" + 0.010*\"情形\" + 0.009*\"方式\"\n",
      "2025-04-19 00:10:51,858 : INFO : topic #1 (0.111): 0.030*\"工作\" + 0.016*\"方式\" + 0.012*\"砍除\" + 0.012*\"推定\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 00:10:51,859 : INFO : topic #8 (0.111): 0.032*\"工作\" + 0.015*\"推定\" + 0.013*\"國定假日\" + 0.013*\"情形\" + 0.013*\"空白\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.011*\"第一項\" + 0.010*\"單位\" + 0.010*\"砍除\"\n",
      "2025-04-19 00:10:51,860 : INFO : topic #6 (0.111): 0.027*\"報名\" + 0.024*\"活動\" + 0.022*\"電話\" + 0.015*\"台北市\" + 0.014*\"車馬費\" + 0.014*\"人數\" + 0.012*\"資料\" + 0.011*\"舉辦\" + 0.011*\"訪問\" + 0.011*\"時間\"\n",
      "2025-04-19 00:10:51,860 : INFO : topic diff=0.773415, rho=0.707107\n",
      "2025-04-19 00:10:51,861 : INFO : PROGRESS: pass 0, at document #6000/16310\n",
      "2025-04-19 00:10:52,403 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:52,407 : INFO : topic #0 (0.111): 0.030*\"工作\" + 0.014*\"砍除\" + 0.013*\"應徵\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.011*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:52,408 : INFO : topic #3 (0.111): 0.013*\"工作\" + 0.012*\"方式\" + 0.009*\"時間\" + 0.009*\"公司\" + 0.009*\"聯絡人\" + 0.009*\"資訊\" + 0.008*\"連結\" + 0.008*\"內容\" + 0.008*\"分類\" + 0.008*\"文字\"\n",
      "2025-04-19 00:10:52,408 : INFO : topic #6 (0.111): 0.028*\"報名\" + 0.025*\"活動\" + 0.020*\"電話\" + 0.016*\"台北市\" + 0.013*\"車馬費\" + 0.013*\"資料\" + 0.012*\"人數\" + 0.012*\"訪問\" + 0.011*\"舉辦\" + 0.011*\"時間\"\n",
      "2025-04-19 00:10:52,409 : INFO : topic #7 (0.111): 0.024*\"工作\" + 0.012*\"空白\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"聯絡\" + 0.010*\"砍除\" + 0.009*\"第一項\" + 0.009*\"情形\" + 0.009*\"方式\"\n",
      "2025-04-19 00:10:52,409 : INFO : topic #5 (0.111): 0.020*\"工作\" + 0.017*\"方式\" + 0.010*\"時間\" + 0.009*\"依法\" + 0.009*\"通知\" + 0.007*\"應徵\" + 0.007*\"聯絡\" + 0.007*\"面試\" + 0.007*\"每日\" + 0.007*\"電話\"\n",
      "2025-04-19 00:10:52,410 : INFO : topic diff=0.557848, rho=0.577350\n",
      "2025-04-19 00:10:52,410 : INFO : PROGRESS: pass 0, at document #8000/16310\n",
      "2025-04-19 00:10:52,789 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:52,793 : INFO : topic #2 (0.111): 0.034*\"工作\" + 0.014*\"公司\" + 0.010*\"方式\" + 0.009*\"面試\" + 0.009*\"時間\" + 0.008*\"內容\" + 0.008*\"小時\" + 0.006*\"覺得\" + 0.006*\"開發\" + 0.006*\"推定\"\n",
      "2025-04-19 00:10:52,794 : INFO : topic #4 (0.111): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.012*\"聯絡\" + 0.012*\"第一項\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.010*\"承攬\"\n",
      "2025-04-19 00:10:52,794 : INFO : topic #0 (0.111): 0.030*\"工作\" + 0.014*\"砍除\" + 0.013*\"應徵\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:52,795 : INFO : topic #6 (0.111): 0.018*\"報名\" + 0.017*\"活動\" + 0.016*\"產品\" + 0.014*\"資料\" + 0.013*\"電話\" + 0.013*\"使用\" + 0.011*\"台北市\" + 0.010*\"目前\" + 0.010*\"進行\" + 0.010*\"介紹\"\n",
      "2025-04-19 00:10:52,795 : INFO : topic #7 (0.111): 0.023*\"工作\" + 0.011*\"空白\" + 0.010*\"內容\" + 0.009*\"資訊\" + 0.009*\"推定\" + 0.009*\"聯絡\" + 0.009*\"砍除\" + 0.008*\"第一項\" + 0.008*\"情形\" + 0.008*\"方式\"\n",
      "2025-04-19 00:10:52,796 : INFO : topic diff=0.651236, rho=0.500000\n",
      "2025-04-19 00:10:52,796 : INFO : PROGRESS: pass 0, at document #10000/16310\n",
      "2025-04-19 00:10:53,069 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:53,073 : INFO : topic #0 (0.111): 0.030*\"工作\" + 0.014*\"砍除\" + 0.013*\"應徵\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:53,073 : INFO : topic #3 (0.111): 0.016*\"研發\" + 0.013*\"公司\" + 0.012*\"資工\" + 0.011*\"工作\" + 0.010*\"數學\" + 0.010*\"職場\" + 0.009*\"方式\" + 0.008*\"時間\" + 0.008*\"資訊\" + 0.007*\"連結\"\n",
      "2025-04-19 00:10:53,074 : INFO : topic #4 (0.111): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.012*\"聯絡\" + 0.011*\"第一項\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.010*\"承攬\"\n",
      "2025-04-19 00:10:53,074 : INFO : topic #6 (0.111): 0.020*\"產品\" + 0.016*\"資料\" + 0.016*\"報名\" + 0.015*\"活動\" + 0.014*\"使用\" + 0.011*\"進行\" + 0.010*\"目前\" + 0.010*\"介紹\" + 0.010*\"電話\" + 0.010*\"公司\"\n",
      "2025-04-19 00:10:53,075 : INFO : topic #5 (0.111): 0.017*\"公司\" + 0.014*\"工作\" + 0.012*\"面試\" + 0.011*\"問題\" + 0.010*\"工程師\" + 0.009*\"經驗\" + 0.008*\"技術\" + 0.008*\"團隊\" + 0.008*\"時間\" + 0.008*\"目前\"\n",
      "2025-04-19 00:10:53,075 : INFO : topic diff=0.540137, rho=0.447214\n",
      "2025-04-19 00:10:53,076 : INFO : PROGRESS: pass 0, at document #12000/16310\n",
      "2025-04-19 00:10:53,344 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:53,347 : INFO : topic #1 (0.111): 0.029*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.011*\"第一項\" + 0.011*\"推定\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 00:10:53,348 : INFO : topic #2 (0.111): 0.025*\"工作\" + 0.015*\"公司\" + 0.008*\"覺得\" + 0.008*\"面試\" + 0.007*\"程式\" + 0.007*\"時間\" + 0.007*\"比較\" + 0.007*\"開發\" + 0.006*\"真的\" + 0.006*\"應該\"\n",
      "2025-04-19 00:10:53,348 : INFO : topic #0 (0.111): 0.030*\"工作\" + 0.014*\"砍除\" + 0.012*\"應徵\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:53,349 : INFO : topic #4 (0.111): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"聯絡\" + 0.011*\"第一項\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.010*\"承攬\"\n",
      "2025-04-19 00:10:53,350 : INFO : topic #5 (0.111): 0.016*\"公司\" + 0.011*\"工作\" + 0.009*\"問題\" + 0.009*\"面試\" + 0.008*\"工程師\" + 0.008*\"技術\" + 0.007*\"經驗\" + 0.007*\"目前\" + 0.006*\"台灣\" + 0.006*\"時間\"\n",
      "2025-04-19 00:10:53,350 : INFO : topic diff=0.489343, rho=0.408248\n",
      "2025-04-19 00:10:53,351 : INFO : PROGRESS: pass 0, at document #14000/16310\n",
      "2025-04-19 00:10:53,666 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:53,677 : INFO : topic #3 (0.111): 0.096*\"半導體\" + 0.043*\"研發\" + 0.042*\"製程\" + 0.031*\"中國\" + 0.026*\"表示\" + 0.014*\"英特爾\" + 0.012*\"職場\" + 0.009*\"公司\" + 0.008*\"資工\" + 0.007*\"數學\"\n",
      "2025-04-19 00:10:53,679 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.009*\"台灣\" + 0.008*\"工作\" + 0.007*\"技術\" + 0.007*\"工程師\" + 0.007*\"問題\" + 0.006*\"員工\" + 0.006*\"美國\" + 0.006*\"面試\" + 0.005*\"目前\"\n",
      "2025-04-19 00:10:53,680 : INFO : topic #4 (0.111): 0.037*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.011*\"聯絡\" + 0.011*\"第一項\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.010*\"承攬\"\n",
      "2025-04-19 00:10:53,682 : INFO : topic #6 (0.111): 0.017*\"產品\" + 0.012*\"進行\" + 0.012*\"使用\" + 0.012*\"資料\" + 0.012*\"活動\" + 0.011*\"研究\" + 0.010*\"影響\" + 0.010*\"報名\" + 0.009*\"模型\" + 0.008*\"目前\"\n",
      "2025-04-19 00:10:53,683 : INFO : topic #2 (0.111): 0.022*\"工作\" + 0.014*\"公司\" + 0.007*\"覺得\" + 0.006*\"面試\" + 0.006*\"比較\" + 0.006*\"時間\" + 0.006*\"程式\" + 0.006*\"應該\" + 0.005*\"真的\" + 0.005*\"開發\"\n",
      "2025-04-19 00:10:53,684 : INFO : topic diff=0.463579, rho=0.377964\n",
      "2025-04-19 00:10:53,686 : INFO : PROGRESS: pass 0, at document #16000/16310\n",
      "2025-04-19 00:10:53,956 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:53,960 : INFO : topic #2 (0.111): 0.019*\"工作\" + 0.014*\"公司\" + 0.007*\"覺得\" + 0.006*\"應該\" + 0.005*\"真的\" + 0.005*\"面試\" + 0.005*\"比較\" + 0.005*\"時間\" + 0.004*\"程式\" + 0.004*\"開發\"\n",
      "2025-04-19 00:10:53,961 : INFO : topic #7 (0.111): 0.046*\"退休\" + 0.016*\"工作\" + 0.008*\"終止\" + 0.007*\"表示\" + 0.007*\"日本\" + 0.006*\"空白\" + 0.006*\"資訊\" + 0.006*\"內容\" + 0.006*\"商用\" + 0.006*\"東京\"\n",
      "2025-04-19 00:10:53,961 : INFO : topic #4 (0.111): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.011*\"聯絡\" + 0.011*\"第一項\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.010*\"承攬\"\n",
      "2025-04-19 00:10:53,962 : INFO : topic #5 (0.111): 0.013*\"公司\" + 0.010*\"台灣\" + 0.008*\"美國\" + 0.007*\"技術\" + 0.007*\"員工\" + 0.007*\"晶片\" + 0.007*\"工作\" + 0.006*\"工程師\" + 0.006*\"台積電\" + 0.005*\"問題\"\n",
      "2025-04-19 00:10:53,963 : INFO : topic #1 (0.111): 0.029*\"工作\" + 0.015*\"方式\" + 0.012*\"砍除\" + 0.011*\"第一項\" + 0.011*\"推定\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:53,963 : INFO : topic diff=0.441207, rho=0.353553\n",
      "2025-04-19 00:10:54,038 : INFO : -9.361 per-word bound, 657.5 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:10:54,038 : INFO : PROGRESS: pass 0, at document #16310/16310\n",
      "2025-04-19 00:10:54,075 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:54,079 : INFO : topic #8 (0.111): 0.031*\"工作\" + 0.013*\"推定\" + 0.013*\"國定假日\" + 0.012*\"情形\" + 0.012*\"空白\" + 0.011*\"應徵\" + 0.010*\"第一項\" + 0.010*\"方式\" + 0.010*\"砍除\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:54,080 : INFO : topic #1 (0.111): 0.028*\"工作\" + 0.015*\"方式\" + 0.012*\"砍除\" + 0.011*\"第一項\" + 0.011*\"推定\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:54,080 : INFO : topic #7 (0.111): 0.048*\"退休\" + 0.014*\"工作\" + 0.009*\"東京\" + 0.007*\"終止\" + 0.007*\"商用\" + 0.007*\"表示\" + 0.006*\"三家\" + 0.006*\"日本\" + 0.006*\"空白\" + 0.006*\"資訊\"\n",
      "2025-04-19 00:10:54,081 : INFO : topic #6 (0.111): 0.016*\"產品\" + 0.012*\"研究\" + 0.012*\"進行\" + 0.011*\"影響\" + 0.011*\"模型\" + 0.011*\"蘋果\" + 0.010*\"資料\" + 0.010*\"機器人\" + 0.009*\"活動\" + 0.009*\"使用\"\n",
      "2025-04-19 00:10:54,081 : INFO : topic #4 (0.111): 0.035*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.011*\"聯絡\" + 0.011*\"第一項\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"資訊\" + 0.010*\"情形\" + 0.010*\"承攬\"\n",
      "2025-04-19 00:10:54,082 : INFO : topic diff=0.406976, rho=0.333333\n",
      "2025-04-19 00:10:54,082 : INFO : PROGRESS: pass 1, at document #2000/16310\n",
      "2025-04-19 00:10:54,747 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:54,751 : INFO : topic #8 (0.111): 0.034*\"工作\" + 0.014*\"推定\" + 0.013*\"情形\" + 0.013*\"國定假日\" + 0.012*\"空白\" + 0.012*\"應徵\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.011*\"第一項\" + 0.010*\"單位\"\n",
      "2025-04-19 00:10:54,751 : INFO : topic #0 (0.111): 0.032*\"工作\" + 0.012*\"方式\" + 0.012*\"應徵\" + 0.011*\"砍除\" + 0.011*\"內容\" + 0.010*\"空白\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"單位\" + 0.010*\"第一項\"\n",
      "2025-04-19 00:10:54,752 : INFO : topic #4 (0.111): 0.037*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"方式\" + 0.012*\"砍除\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:10:54,752 : INFO : topic #2 (0.111): 0.019*\"工作\" + 0.014*\"公司\" + 0.006*\"真的\" + 0.006*\"覺得\" + 0.006*\"時間\" + 0.006*\"應該\" + 0.005*\"面試\" + 0.005*\"比較\" + 0.004*\"台積\" + 0.004*\"東西\"\n",
      "2025-04-19 00:10:54,753 : INFO : topic #6 (0.111): 0.019*\"報名\" + 0.018*\"活動\" + 0.012*\"進行\" + 0.011*\"電話\" + 0.011*\"資料\" + 0.011*\"研究\" + 0.010*\"台北市\" + 0.010*\"產品\" + 0.010*\"舉辦\" + 0.009*\"參與\"\n",
      "2025-04-19 00:10:54,753 : INFO : topic diff=1.092497, rho=0.313805\n",
      "2025-04-19 00:10:54,754 : INFO : PROGRESS: pass 1, at document #4000/16310\n",
      "2025-04-19 00:10:55,372 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:55,376 : INFO : topic #5 (0.111): 0.013*\"公司\" + 0.010*\"美國\" + 0.010*\"台灣\" + 0.008*\"技術\" + 0.007*\"員工\" + 0.006*\"晶片\" + 0.006*\"台積電\" + 0.006*\"工作\" + 0.006*\"科技\" + 0.005*\"問題\"\n",
      "2025-04-19 00:10:55,376 : INFO : topic #7 (0.111): 0.019*\"工作\" + 0.016*\"退休\" + 0.013*\"訪員\" + 0.010*\"時間\" + 0.009*\"內容\" + 0.008*\"台北市\" + 0.007*\"南港\" + 0.007*\"南港區\" + 0.007*\"規定\" + 0.007*\"店家\"\n",
      "2025-04-19 00:10:55,377 : INFO : topic #2 (0.111): 0.019*\"工作\" + 0.013*\"公司\" + 0.006*\"時間\" + 0.006*\"面試\" + 0.006*\"真的\" + 0.005*\"覺得\" + 0.005*\"應該\" + 0.005*\"比較\" + 0.004*\"內容\" + 0.004*\"需要\"\n",
      "2025-04-19 00:10:55,378 : INFO : topic #4 (0.111): 0.036*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\" + 0.011*\"單位\" + 0.011*\"第一項\"\n",
      "2025-04-19 00:10:55,378 : INFO : topic #3 (0.111): 0.044*\"半導體\" + 0.028*\"中國\" + 0.024*\"英特爾\" + 0.024*\"研發\" + 0.024*\"製程\" + 0.023*\"表示\" + 0.018*\"川普\" + 0.018*\"投資\" + 0.012*\"先進\" + 0.009*\"魏哲家\"\n",
      "2025-04-19 00:10:55,379 : INFO : topic diff=0.414560, rho=0.313805\n",
      "2025-04-19 00:10:55,379 : INFO : PROGRESS: pass 1, at document #6000/16310\n",
      "2025-04-19 00:10:55,911 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:55,916 : INFO : topic #1 (0.111): 0.037*\"工作\" + 0.021*\"方式\" + 0.013*\"推定\" + 0.012*\"內容\" + 0.012*\"聯絡\" + 0.012*\"單位\" + 0.012*\"小時\" + 0.011*\"未註明\" + 0.010*\"工資\" + 0.010*\"時間\"\n",
      "2025-04-19 00:10:55,917 : INFO : topic #3 (0.111): 0.039*\"半導體\" + 0.026*\"中國\" + 0.022*\"研發\" + 0.021*\"英特爾\" + 0.021*\"表示\" + 0.021*\"製程\" + 0.016*\"川普\" + 0.016*\"投資\" + 0.011*\"先進\" + 0.009*\"職場\"\n",
      "2025-04-19 00:10:55,917 : INFO : topic #6 (0.111): 0.028*\"報名\" + 0.025*\"活動\" + 0.018*\"電話\" + 0.015*\"台北市\" + 0.013*\"資料\" + 0.012*\"車馬費\" + 0.012*\"舉辦\" + 0.012*\"人數\" + 0.011*\"進行\" + 0.011*\"訪問\"\n",
      "2025-04-19 00:10:55,918 : INFO : topic #0 (0.111): 0.032*\"工作\" + 0.012*\"方式\" + 0.011*\"砍除\" + 0.011*\"應徵\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.010*\"第一項\" + 0.010*\"空白\" + 0.010*\"情形\" + 0.010*\"文字\"\n",
      "2025-04-19 00:10:55,918 : INFO : topic #4 (0.111): 0.035*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.012*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\" + 0.011*\"第一項\" + 0.011*\"單位\"\n",
      "2025-04-19 00:10:55,919 : INFO : topic diff=0.226413, rho=0.313805\n",
      "2025-04-19 00:10:55,919 : INFO : PROGRESS: pass 1, at document #8000/16310\n",
      "2025-04-19 00:10:56,202 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:56,206 : INFO : topic #0 (0.111): 0.033*\"工作\" + 0.012*\"方式\" + 0.011*\"應徵\" + 0.011*\"砍除\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.010*\"第一項\" + 0.010*\"空白\" + 0.010*\"情形\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:10:56,207 : INFO : topic #8 (0.111): 0.033*\"工作\" + 0.013*\"情形\" + 0.013*\"推定\" + 0.013*\"空白\" + 0.012*\"國定假日\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"文字\"\n",
      "2025-04-19 00:10:56,207 : INFO : topic #3 (0.111): 0.039*\"半導體\" + 0.032*\"研發\" + 0.025*\"中國\" + 0.020*\"表示\" + 0.019*\"製程\" + 0.019*\"英特爾\" + 0.015*\"職場\" + 0.015*\"投資\" + 0.015*\"川普\" + 0.010*\"資工\"\n",
      "2025-04-19 00:10:56,208 : INFO : topic #6 (0.111): 0.026*\"報名\" + 0.024*\"活動\" + 0.017*\"電話\" + 0.015*\"台北市\" + 0.013*\"資料\" + 0.011*\"舉辦\" + 0.011*\"進行\" + 0.011*\"車馬費\" + 0.011*\"人數\" + 0.010*\"參加\"\n",
      "2025-04-19 00:10:56,208 : INFO : topic #5 (0.111): 0.016*\"公司\" + 0.008*\"工程師\" + 0.008*\"技術\" + 0.007*\"工作\" + 0.007*\"問題\" + 0.006*\"台灣\" + 0.006*\"團隊\" + 0.006*\"目前\" + 0.006*\"面試\" + 0.005*\"經驗\"\n",
      "2025-04-19 00:10:56,209 : INFO : topic diff=0.331599, rho=0.313805\n",
      "2025-04-19 00:10:56,209 : INFO : PROGRESS: pass 1, at document #10000/16310\n",
      "2025-04-19 00:10:56,466 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:56,470 : INFO : topic #2 (0.111): 0.024*\"工作\" + 0.016*\"公司\" + 0.012*\"面試\" + 0.010*\"覺得\" + 0.009*\"比較\" + 0.008*\"程式\" + 0.008*\"時間\" + 0.007*\"應該\" + 0.006*\"真的\" + 0.006*\"一些\"\n",
      "2025-04-19 00:10:56,470 : INFO : topic #5 (0.111): 0.016*\"公司\" + 0.008*\"工程師\" + 0.008*\"工作\" + 0.007*\"技術\" + 0.007*\"問題\" + 0.007*\"目前\" + 0.006*\"台灣\" + 0.006*\"團隊\" + 0.006*\"經驗\" + 0.006*\"面試\"\n",
      "2025-04-19 00:10:56,471 : INFO : topic #0 (0.111): 0.033*\"工作\" + 0.012*\"方式\" + 0.011*\"應徵\" + 0.011*\"資訊\" + 0.011*\"砍除\" + 0.010*\"內容\" + 0.010*\"第一項\" + 0.010*\"空白\" + 0.010*\"聯絡\" + 0.010*\"情形\"\n",
      "2025-04-19 00:10:56,472 : INFO : topic #3 (0.111): 0.038*\"半導體\" + 0.036*\"研發\" + 0.024*\"中國\" + 0.019*\"表示\" + 0.018*\"製程\" + 0.018*\"職場\" + 0.017*\"資工\" + 0.017*\"英特爾\" + 0.014*\"投資\" + 0.013*\"川普\"\n",
      "2025-04-19 00:10:56,472 : INFO : topic #1 (0.111): 0.038*\"工作\" + 0.022*\"方式\" + 0.013*\"推定\" + 0.013*\"小時\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.012*\"單位\" + 0.011*\"時間\" + 0.011*\"未註明\" + 0.010*\"工資\"\n",
      "2025-04-19 00:10:56,473 : INFO : topic diff=0.281504, rho=0.313805\n",
      "2025-04-19 00:10:56,473 : INFO : PROGRESS: pass 1, at document #12000/16310\n",
      "2025-04-19 00:10:56,684 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:56,688 : INFO : topic #1 (0.111): 0.038*\"工作\" + 0.022*\"方式\" + 0.013*\"小時\" + 0.012*\"推定\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.012*\"單位\" + 0.011*\"時間\" + 0.011*\"未註明\" + 0.010*\"工資\"\n",
      "2025-04-19 00:10:56,689 : INFO : topic #5 (0.111): 0.015*\"公司\" + 0.007*\"技術\" + 0.007*\"工程師\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.006*\"問題\" + 0.006*\"目前\" + 0.005*\"經驗\" + 0.005*\"相關\" + 0.005*\"員工\"\n",
      "2025-04-19 00:10:56,689 : INFO : topic #7 (0.111): 0.045*\"日本\" + 0.023*\"退休\" + 0.019*\"工作\" + 0.015*\"東京\" + 0.012*\"時間\" + 0.009*\"勞基法\" + 0.008*\"南港\" + 0.008*\"時數\" + 0.008*\"篇幅\" + 0.007*\"接案\"\n",
      "2025-04-19 00:10:56,690 : INFO : topic #6 (0.111): 0.024*\"活動\" + 0.024*\"報名\" + 0.014*\"電話\" + 0.013*\"研究\" + 0.012*\"資料\" + 0.012*\"進行\" + 0.012*\"台北市\" + 0.011*\"使用\" + 0.011*\"參加\" + 0.010*\"舉辦\"\n",
      "2025-04-19 00:10:56,690 : INFO : topic #3 (0.111): 0.039*\"半導體\" + 0.023*\"表示\" + 0.021*\"中國\" + 0.020*\"製程\" + 0.019*\"研發\" + 0.016*\"晶片\" + 0.016*\"英特爾\" + 0.013*\"投資\" + 0.011*\"全球\" + 0.011*\"先進\"\n",
      "2025-04-19 00:10:56,691 : INFO : topic diff=0.348719, rho=0.313805\n",
      "2025-04-19 00:10:56,691 : INFO : PROGRESS: pass 1, at document #14000/16310\n",
      "2025-04-19 00:10:56,968 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:56,978 : INFO : topic #1 (0.111): 0.038*\"工作\" + 0.022*\"方式\" + 0.013*\"小時\" + 0.012*\"推定\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.012*\"單位\" + 0.011*\"時間\" + 0.010*\"未註明\" + 0.010*\"工資\"\n",
      "2025-04-19 00:10:56,979 : INFO : topic #2 (0.111): 0.020*\"工作\" + 0.015*\"公司\" + 0.010*\"面試\" + 0.008*\"覺得\" + 0.008*\"比較\" + 0.007*\"時間\" + 0.006*\"應該\" + 0.006*\"真的\" + 0.006*\"程式\" + 0.005*\"一些\"\n",
      "2025-04-19 00:10:56,980 : INFO : topic #4 (0.111): 0.034*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\" + 0.011*\"單位\" + 0.011*\"第一項\"\n",
      "2025-04-19 00:10:56,983 : INFO : topic #3 (0.111): 0.030*\"半導體\" + 0.024*\"表示\" + 0.024*\"中國\" + 0.022*\"晶片\" + 0.017*\"英特爾\" + 0.014*\"研發\" + 0.014*\"製程\" + 0.013*\"全球\" + 0.012*\"投資\" + 0.011*\"先進\"\n",
      "2025-04-19 00:10:56,984 : INFO : topic #8 (0.111): 0.033*\"工作\" + 0.013*\"情形\" + 0.013*\"推定\" + 0.013*\"空白\" + 0.012*\"國定假日\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"文字\"\n",
      "2025-04-19 00:10:56,984 : INFO : topic diff=0.333906, rho=0.313805\n",
      "2025-04-19 00:10:56,985 : INFO : PROGRESS: pass 1, at document #16000/16310\n",
      "2025-04-19 00:10:57,233 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:57,237 : INFO : topic #1 (0.111): 0.038*\"工作\" + 0.022*\"方式\" + 0.013*\"小時\" + 0.012*\"推定\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.012*\"單位\" + 0.011*\"時間\" + 0.010*\"工資\" + 0.010*\"未註明\"\n",
      "2025-04-19 00:10:57,238 : INFO : topic #5 (0.111): 0.013*\"公司\" + 0.008*\"台灣\" + 0.007*\"技術\" + 0.006*\"員工\" + 0.006*\"美國\" + 0.005*\"工程師\" + 0.005*\"科技\" + 0.005*\"目前\" + 0.005*\"工作\" + 0.005*\"報導\"\n",
      "2025-04-19 00:10:57,238 : INFO : topic #2 (0.111): 0.019*\"工作\" + 0.015*\"公司\" + 0.009*\"面試\" + 0.007*\"覺得\" + 0.007*\"比較\" + 0.006*\"真的\" + 0.006*\"應該\" + 0.006*\"時間\" + 0.005*\"主管\" + 0.005*\"程式\"\n",
      "2025-04-19 00:10:57,239 : INFO : topic #0 (0.111): 0.032*\"工作\" + 0.012*\"方式\" + 0.011*\"應徵\" + 0.010*\"資訊\" + 0.010*\"內容\" + 0.010*\"砍除\" + 0.010*\"第一項\" + 0.009*\"空白\" + 0.009*\"聯絡\" + 0.009*\"情形\"\n",
      "2025-04-19 00:10:57,240 : INFO : topic #8 (0.111): 0.033*\"工作\" + 0.013*\"情形\" + 0.013*\"推定\" + 0.013*\"空白\" + 0.012*\"國定假日\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"文字\"\n",
      "2025-04-19 00:10:57,240 : INFO : topic diff=0.297242, rho=0.313805\n",
      "2025-04-19 00:10:57,320 : INFO : -8.773 per-word bound, 437.6 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:10:57,321 : INFO : PROGRESS: pass 1, at document #16310/16310\n",
      "2025-04-19 00:10:57,356 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:57,360 : INFO : topic #8 (0.111): 0.032*\"工作\" + 0.013*\"情形\" + 0.013*\"國定假日\" + 0.013*\"推定\" + 0.012*\"空白\" + 0.012*\"第一項\" + 0.011*\"砍除\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"文字\"\n",
      "2025-04-19 00:10:57,360 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.008*\"技術\" + 0.008*\"台灣\" + 0.007*\"員工\" + 0.007*\"美國\" + 0.006*\"科技\" + 0.005*\"報導\" + 0.005*\"工程師\" + 0.005*\"台積\" + 0.005*\"台積電\"\n",
      "2025-04-19 00:10:57,361 : INFO : topic #6 (0.111): 0.019*\"活動\" + 0.017*\"研究\" + 0.017*\"報名\" + 0.015*\"蘋果\" + 0.013*\"機器人\" + 0.013*\"問卷\" + 0.011*\"進行\" + 0.010*\"資料\" + 0.010*\"女性\" + 0.009*\"參與\"\n",
      "2025-04-19 00:10:57,361 : INFO : topic #0 (0.111): 0.031*\"工作\" + 0.011*\"方式\" + 0.011*\"應徵\" + 0.010*\"資訊\" + 0.010*\"內容\" + 0.010*\"砍除\" + 0.009*\"單位\" + 0.009*\"第一項\" + 0.009*\"空白\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:10:57,362 : INFO : topic #7 (0.111): 0.097*\"日本\" + 0.044*\"退休\" + 0.029*\"勞工\" + 0.018*\"東京\" + 0.016*\"勞動\" + 0.015*\"日圓\" + 0.014*\"持有\" + 0.011*\"工作\" + 0.010*\"契約\" + 0.009*\"南港\"\n",
      "2025-04-19 00:10:57,363 : INFO : topic diff=0.296723, rho=0.313805\n",
      "2025-04-19 00:10:57,363 : INFO : PROGRESS: pass 2, at document #2000/16310\n",
      "2025-04-19 00:10:57,940 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:57,943 : INFO : topic #2 (0.111): 0.018*\"工作\" + 0.015*\"公司\" + 0.008*\"面試\" + 0.007*\"真的\" + 0.007*\"覺得\" + 0.006*\"應該\" + 0.006*\"時間\" + 0.006*\"比較\" + 0.005*\"知道\" + 0.005*\"主管\"\n",
      "2025-04-19 00:10:57,944 : INFO : topic #8 (0.111): 0.032*\"工作\" + 0.013*\"情形\" + 0.013*\"空白\" + 0.013*\"第一項\" + 0.012*\"砍除\" + 0.012*\"推定\" + 0.012*\"國定假日\" + 0.011*\"應徵\" + 0.011*\"文字\" + 0.011*\"方式\"\n",
      "2025-04-19 00:10:57,945 : INFO : topic #0 (0.111): 0.031*\"工作\" + 0.011*\"方式\" + 0.011*\"資訊\" + 0.010*\"應徵\" + 0.010*\"內容\" + 0.010*\"砍除\" + 0.010*\"第一項\" + 0.009*\"情形\" + 0.009*\"空白\" + 0.009*\"文字\"\n",
      "2025-04-19 00:10:57,945 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.008*\"台灣\" + 0.008*\"技術\" + 0.007*\"員工\" + 0.007*\"美國\" + 0.006*\"科技\" + 0.005*\"報導\" + 0.005*\"工程師\" + 0.005*\"台積\" + 0.005*\"台積電\"\n",
      "2025-04-19 00:10:57,946 : INFO : topic #4 (0.111): 0.033*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\" + 0.011*\"第一項\" + 0.011*\"單位\"\n",
      "2025-04-19 00:10:57,946 : INFO : topic diff=0.808031, rho=0.299409\n",
      "2025-04-19 00:10:57,947 : INFO : PROGRESS: pass 2, at document #4000/16310\n",
      "2025-04-19 00:10:58,553 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:58,557 : INFO : topic #3 (0.111): 0.023*\"晶片\" + 0.020*\"中國\" + 0.020*\"半導體\" + 0.019*\"表示\" + 0.017*\"美國\" + 0.017*\"投資\" + 0.016*\"英特爾\" + 0.011*\"研發\" + 0.011*\"製程\" + 0.011*\"台灣\"\n",
      "2025-04-19 00:10:58,557 : INFO : topic #1 (0.111): 0.043*\"工作\" + 0.024*\"方式\" + 0.015*\"小時\" + 0.015*\"推定\" + 0.014*\"時間\" + 0.013*\"工資\" + 0.013*\"單位\" + 0.012*\"依法\" + 0.012*\"內容\" + 0.012*\"未註明\"\n",
      "2025-04-19 00:10:58,558 : INFO : topic #8 (0.111): 0.032*\"工作\" + 0.013*\"情形\" + 0.013*\"第一項\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.012*\"推定\" + 0.011*\"國定假日\" + 0.011*\"文字\" + 0.011*\"方式\" + 0.011*\"應徵\"\n",
      "2025-04-19 00:10:58,559 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.008*\"台灣\" + 0.008*\"技術\" + 0.007*\"員工\" + 0.007*\"美國\" + 0.006*\"科技\" + 0.005*\"報導\" + 0.005*\"工程師\" + 0.005*\"台積\" + 0.004*\"台積電\"\n",
      "2025-04-19 00:10:58,559 : INFO : topic #0 (0.111): 0.031*\"工作\" + 0.011*\"方式\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.010*\"應徵\" + 0.010*\"砍除\" + 0.010*\"情形\" + 0.010*\"第一項\" + 0.009*\"文字\" + 0.009*\"空白\"\n",
      "2025-04-19 00:10:58,560 : INFO : topic diff=0.341741, rho=0.299409\n",
      "2025-04-19 00:10:58,560 : INFO : PROGRESS: pass 2, at document #6000/16310\n",
      "2025-04-19 00:10:59,030 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:59,035 : INFO : topic #3 (0.111): 0.023*\"晶片\" + 0.020*\"中國\" + 0.019*\"半導體\" + 0.019*\"表示\" + 0.017*\"美國\" + 0.016*\"投資\" + 0.015*\"英特爾\" + 0.011*\"研發\" + 0.010*\"台灣\" + 0.010*\"製程\"\n",
      "2025-04-19 00:10:59,035 : INFO : topic #1 (0.111): 0.044*\"工作\" + 0.025*\"方式\" + 0.016*\"小時\" + 0.014*\"推定\" + 0.014*\"時間\" + 0.013*\"工資\" + 0.013*\"依法\" + 0.013*\"單位\" + 0.012*\"內容\" + 0.012*\"每日\"\n",
      "2025-04-19 00:10:59,036 : INFO : topic #0 (0.111): 0.031*\"工作\" + 0.011*\"方式\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.010*\"情形\" + 0.010*\"應徵\" + 0.010*\"文字\" + 0.010*\"砍除\" + 0.009*\"第一項\" + 0.009*\"聯絡\"\n",
      "2025-04-19 00:10:59,036 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.008*\"技術\" + 0.007*\"台灣\" + 0.006*\"員工\" + 0.006*\"美國\" + 0.006*\"科技\" + 0.005*\"工程師\" + 0.005*\"產品\" + 0.005*\"目前\" + 0.004*\"工作\"\n",
      "2025-04-19 00:10:59,037 : INFO : topic #8 (0.111): 0.032*\"工作\" + 0.013*\"第一項\" + 0.013*\"情形\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"國定假日\" + 0.011*\"方式\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:10:59,037 : INFO : topic diff=0.177899, rho=0.299409\n",
      "2025-04-19 00:10:59,038 : INFO : PROGRESS: pass 2, at document #8000/16310\n",
      "2025-04-19 00:10:59,322 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:59,327 : INFO : topic #5 (0.111): 0.016*\"公司\" + 0.008*\"技術\" + 0.008*\"工程師\" + 0.006*\"台灣\" + 0.006*\"團隊\" + 0.006*\"產品\" + 0.006*\"目前\" + 0.005*\"工作\" + 0.005*\"員工\" + 0.005*\"問題\"\n",
      "2025-04-19 00:10:59,327 : INFO : topic #3 (0.111): 0.021*\"晶片\" + 0.020*\"中國\" + 0.020*\"半導體\" + 0.018*\"表示\" + 0.016*\"美國\" + 0.015*\"投資\" + 0.014*\"英特爾\" + 0.014*\"研發\" + 0.011*\"台灣\" + 0.010*\"全球\"\n",
      "2025-04-19 00:10:59,328 : INFO : topic #1 (0.111): 0.046*\"工作\" + 0.026*\"方式\" + 0.017*\"小時\" + 0.015*\"時間\" + 0.014*\"推定\" + 0.013*\"工資\" + 0.013*\"每日\" + 0.012*\"依法\" + 0.012*\"內容\" + 0.012*\"單位\"\n",
      "2025-04-19 00:10:59,329 : INFO : topic #4 (0.111): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\" + 0.011*\"第一項\" + 0.011*\"單位\"\n",
      "2025-04-19 00:10:59,329 : INFO : topic #8 (0.111): 0.032*\"工作\" + 0.013*\"第一項\" + 0.013*\"情形\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"國定假日\" + 0.011*\"方式\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:10:59,330 : INFO : topic diff=0.308188, rho=0.299409\n",
      "2025-04-19 00:10:59,330 : INFO : PROGRESS: pass 2, at document #10000/16310\n",
      "2025-04-19 00:10:59,550 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:59,554 : INFO : topic #7 (0.111): 0.050*\"日本\" + 0.027*\"勞基法\" + 0.021*\"時間\" + 0.021*\"加班費\" + 0.021*\"填寫\" + 0.018*\"工作\" + 0.018*\"超過\" + 0.017*\"時數\" + 0.017*\"雇主\" + 0.016*\"勞工\"\n",
      "2025-04-19 00:10:59,555 : INFO : topic #1 (0.111): 0.048*\"工作\" + 0.027*\"方式\" + 0.017*\"小時\" + 0.015*\"時間\" + 0.013*\"每日\" + 0.013*\"推定\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.012*\"聯絡\" + 0.012*\"單位\"\n",
      "2025-04-19 00:10:59,556 : INFO : topic #0 (0.111): 0.031*\"工作\" + 0.011*\"資訊\" + 0.011*\"方式\" + 0.010*\"內容\" + 0.010*\"應徵\" + 0.009*\"情形\" + 0.009*\"文字\" + 0.009*\"聯絡\" + 0.009*\"砍除\" + 0.009*\"第一項\"\n",
      "2025-04-19 00:10:59,556 : INFO : topic #3 (0.111): 0.021*\"中國\" + 0.020*\"半導體\" + 0.019*\"晶片\" + 0.018*\"表示\" + 0.015*\"美國\" + 0.015*\"投資\" + 0.015*\"研發\" + 0.013*\"英特爾\" + 0.011*\"台灣\" + 0.010*\"全球\"\n",
      "2025-04-19 00:10:59,557 : INFO : topic #6 (0.111): 0.029*\"報名\" + 0.027*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.013*\"資料\" + 0.012*\"舉辦\" + 0.012*\"研究\" + 0.011*\"人數\" + 0.011*\"時間\" + 0.011*\"參加\"\n",
      "2025-04-19 00:10:59,557 : INFO : topic diff=0.259077, rho=0.299409\n",
      "2025-04-19 00:10:59,558 : INFO : PROGRESS: pass 2, at document #12000/16310\n",
      "2025-04-19 00:10:59,769 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:10:59,773 : INFO : topic #0 (0.111): 0.030*\"工作\" + 0.011*\"資訊\" + 0.011*\"方式\" + 0.010*\"內容\" + 0.010*\"應徵\" + 0.009*\"文字\" + 0.009*\"情形\" + 0.009*\"聯絡\" + 0.009*\"徵才\" + 0.009*\"砍除\"\n",
      "2025-04-19 00:10:59,774 : INFO : topic #8 (0.111): 0.032*\"工作\" + 0.013*\"第一項\" + 0.013*\"情形\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"國定假日\" + 0.011*\"方式\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:10:59,774 : INFO : topic #5 (0.111): 0.015*\"公司\" + 0.008*\"技術\" + 0.007*\"工程師\" + 0.006*\"台灣\" + 0.006*\"目前\" + 0.006*\"開發\" + 0.005*\"員工\" + 0.005*\"團隊\" + 0.005*\"產品\" + 0.005*\"工作\"\n",
      "2025-04-19 00:10:59,775 : INFO : topic #1 (0.111): 0.048*\"工作\" + 0.027*\"方式\" + 0.018*\"小時\" + 0.016*\"時間\" + 0.013*\"每日\" + 0.013*\"推定\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.012*\"工資\" + 0.012*\"單位\"\n",
      "2025-04-19 00:10:59,776 : INFO : topic #3 (0.111): 0.023*\"晶片\" + 0.022*\"半導體\" + 0.017*\"表示\" + 0.016*\"中國\" + 0.014*\"美國\" + 0.013*\"台灣\" + 0.012*\"台積電\" + 0.011*\"投資\" + 0.011*\"製程\" + 0.011*\"全球\"\n",
      "2025-04-19 00:10:59,776 : INFO : topic diff=0.304038, rho=0.299409\n",
      "2025-04-19 00:10:59,777 : INFO : PROGRESS: pass 2, at document #14000/16310\n",
      "2025-04-19 00:11:00,107 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:00,127 : INFO : topic #8 (0.111): 0.032*\"工作\" + 0.013*\"第一項\" + 0.013*\"情形\" + 0.013*\"空白\" + 0.012*\"砍除\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"國定假日\" + 0.011*\"方式\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:11:00,128 : INFO : topic #1 (0.111): 0.048*\"工作\" + 0.027*\"方式\" + 0.017*\"小時\" + 0.016*\"時間\" + 0.013*\"每日\" + 0.013*\"推定\" + 0.012*\"聯絡\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.012*\"單位\"\n",
      "2025-04-19 00:11:00,129 : INFO : topic #3 (0.111): 0.022*\"晶片\" + 0.019*\"半導體\" + 0.018*\"表示\" + 0.017*\"中國\" + 0.016*\"台灣\" + 0.015*\"美國\" + 0.014*\"台積電\" + 0.011*\"英特爾\" + 0.011*\"全球\" + 0.010*\"投資\"\n",
      "2025-04-19 00:11:00,130 : INFO : topic #0 (0.111): 0.030*\"工作\" + 0.011*\"資訊\" + 0.010*\"方式\" + 0.010*\"內容\" + 0.010*\"應徵\" + 0.009*\"文字\" + 0.009*\"情形\" + 0.009*\"徵才\" + 0.009*\"聯絡\" + 0.009*\"砍除\"\n",
      "2025-04-19 00:11:00,133 : INFO : topic #2 (0.111): 0.020*\"工作\" + 0.016*\"公司\" + 0.012*\"面試\" + 0.008*\"比較\" + 0.008*\"覺得\" + 0.007*\"問題\" + 0.007*\"時間\" + 0.006*\"真的\" + 0.006*\"應該\" + 0.006*\"程式\"\n",
      "2025-04-19 00:11:00,134 : INFO : topic diff=0.285355, rho=0.299409\n",
      "2025-04-19 00:11:00,135 : INFO : PROGRESS: pass 2, at document #16000/16310\n",
      "2025-04-19 00:11:00,343 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:00,347 : INFO : topic #7 (0.111): 0.121*\"日本\" + 0.029*\"勞工\" + 0.019*\"退休\" + 0.019*\"日圓\" + 0.018*\"超過\" + 0.016*\"時間\" + 0.015*\"勞基法\" + 0.015*\"勞動\" + 0.015*\"雇主\" + 0.015*\"加班費\"\n",
      "2025-04-19 00:11:00,348 : INFO : topic #0 (0.111): 0.029*\"工作\" + 0.010*\"資訊\" + 0.010*\"方式\" + 0.010*\"內容\" + 0.009*\"應徵\" + 0.009*\"情形\" + 0.009*\"文字\" + 0.009*\"徵才\" + 0.009*\"聯絡\" + 0.009*\"砍除\"\n",
      "2025-04-19 00:11:00,349 : INFO : topic #3 (0.111): 0.023*\"晶片\" + 0.019*\"美國\" + 0.019*\"半導體\" + 0.018*\"表示\" + 0.017*\"中國\" + 0.015*\"台灣\" + 0.015*\"台積電\" + 0.013*\"英特爾\" + 0.010*\"全球\" + 0.010*\"積電\"\n",
      "2025-04-19 00:11:00,349 : INFO : topic #8 (0.111): 0.031*\"工作\" + 0.013*\"情形\" + 0.013*\"第一項\" + 0.013*\"空白\" + 0.012*\"砍除\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"方式\" + 0.011*\"國定假日\" + 0.010*\"應徵\"\n",
      "2025-04-19 00:11:00,350 : INFO : topic #1 (0.111): 0.048*\"工作\" + 0.027*\"方式\" + 0.018*\"小時\" + 0.015*\"時間\" + 0.013*\"每日\" + 0.012*\"工資\" + 0.012*\"推定\" + 0.012*\"內容\" + 0.012*\"單位\" + 0.012*\"聯絡\"\n",
      "2025-04-19 00:11:00,351 : INFO : topic diff=0.245612, rho=0.299409\n",
      "2025-04-19 00:11:00,452 : INFO : -8.597 per-word bound, 387.2 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:11:00,452 : INFO : PROGRESS: pass 2, at document #16310/16310\n",
      "2025-04-19 00:11:00,483 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:00,487 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.009*\"技術\" + 0.008*\"員工\" + 0.007*\"科技\" + 0.006*\"台灣\" + 0.006*\"台積\" + 0.006*\"報導\" + 0.005*\"工程師\" + 0.004*\"目前\" + 0.004*\"產品\"\n",
      "2025-04-19 00:11:00,488 : INFO : topic #2 (0.111): 0.017*\"工作\" + 0.017*\"公司\" + 0.010*\"面試\" + 0.007*\"真的\" + 0.007*\"知道\" + 0.007*\"覺得\" + 0.007*\"問題\" + 0.007*\"比較\" + 0.007*\"應該\" + 0.006*\"時間\"\n",
      "2025-04-19 00:11:00,488 : INFO : topic #3 (0.111): 0.025*\"美國\" + 0.023*\"晶片\" + 0.017*\"台積電\" + 0.017*\"表示\" + 0.017*\"台灣\" + 0.016*\"中國\" + 0.016*\"半導體\" + 0.014*\"投資\" + 0.013*\"英特爾\" + 0.010*\"積電\"\n",
      "2025-04-19 00:11:00,489 : INFO : topic #6 (0.111): 0.024*\"活動\" + 0.021*\"報名\" + 0.018*\"研究\" + 0.015*\"問卷\" + 0.011*\"進行\" + 0.011*\"電話\" + 0.011*\"參與\" + 0.010*\"女性\" + 0.010*\"時間\" + 0.010*\"台北市\"\n",
      "2025-04-19 00:11:00,489 : INFO : topic #8 (0.111): 0.031*\"工作\" + 0.013*\"情形\" + 0.013*\"第一項\" + 0.013*\"空白\" + 0.012*\"砍除\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"方式\" + 0.011*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 00:11:00,490 : INFO : topic diff=0.253693, rho=0.299409\n",
      "2025-04-19 00:11:00,490 : INFO : PROGRESS: pass 3, at document #2000/16310\n",
      "2025-04-19 00:11:01,067 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:01,071 : INFO : topic #0 (0.111): 0.028*\"工作\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.009*\"方式\" + 0.009*\"應徵\" + 0.009*\"徵才\" + 0.009*\"文字\" + 0.009*\"情形\" + 0.008*\"國定假日\" + 0.008*\"聯絡\"\n",
      "2025-04-19 00:11:01,072 : INFO : topic #3 (0.111): 0.025*\"美國\" + 0.023*\"晶片\" + 0.017*\"台積電\" + 0.017*\"表示\" + 0.017*\"台灣\" + 0.017*\"中國\" + 0.016*\"半導體\" + 0.014*\"投資\" + 0.013*\"英特爾\" + 0.010*\"積電\"\n",
      "2025-04-19 00:11:01,072 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.009*\"技術\" + 0.008*\"員工\" + 0.007*\"科技\" + 0.006*\"台灣\" + 0.006*\"台積\" + 0.006*\"報導\" + 0.005*\"工程師\" + 0.004*\"目前\" + 0.004*\"產品\"\n",
      "2025-04-19 00:11:01,073 : INFO : topic #7 (0.111): 0.086*\"日本\" + 0.037*\"勞工\" + 0.015*\"勞動\" + 0.014*\"勞基法\" + 0.014*\"超過\" + 0.014*\"時間\" + 0.013*\"退休\" + 0.013*\"雇主\" + 0.013*\"東京\" + 0.012*\"時數\"\n",
      "2025-04-19 00:11:01,073 : INFO : topic #2 (0.111): 0.017*\"工作\" + 0.016*\"公司\" + 0.010*\"面試\" + 0.007*\"真的\" + 0.007*\"問題\" + 0.007*\"知道\" + 0.007*\"比較\" + 0.006*\"覺得\" + 0.006*\"應該\" + 0.006*\"時間\"\n",
      "2025-04-19 00:11:01,074 : INFO : topic diff=0.707778, rho=0.286829\n",
      "2025-04-19 00:11:01,074 : INFO : PROGRESS: pass 3, at document #4000/16310\n",
      "2025-04-19 00:11:01,679 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:01,683 : INFO : topic #6 (0.111): 0.030*\"報名\" + 0.027*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.014*\"舉辦\" + 0.013*\"車馬費\" + 0.012*\"人數\" + 0.012*\"參與\" + 0.012*\"資料\" + 0.011*\"訪問\"\n",
      "2025-04-19 00:11:01,684 : INFO : topic #8 (0.111): 0.031*\"工作\" + 0.013*\"第一項\" + 0.013*\"空白\" + 0.013*\"情形\" + 0.013*\"砍除\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"資訊\" + 0.011*\"方式\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:11:01,685 : INFO : topic #0 (0.111): 0.028*\"工作\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.009*\"方式\" + 0.009*\"應徵\" + 0.009*\"情形\" + 0.009*\"文字\" + 0.009*\"徵才\" + 0.008*\"聯絡\" + 0.008*\"分類\"\n",
      "2025-04-19 00:11:01,685 : INFO : topic #3 (0.111): 0.025*\"美國\" + 0.022*\"晶片\" + 0.017*\"中國\" + 0.016*\"台積電\" + 0.016*\"表示\" + 0.016*\"台灣\" + 0.015*\"半導體\" + 0.014*\"投資\" + 0.013*\"英特爾\" + 0.010*\"積電\"\n",
      "2025-04-19 00:11:01,686 : INFO : topic #2 (0.111): 0.017*\"工作\" + 0.016*\"公司\" + 0.010*\"面試\" + 0.007*\"問題\" + 0.007*\"真的\" + 0.006*\"比較\" + 0.006*\"知道\" + 0.006*\"時間\" + 0.006*\"覺得\" + 0.006*\"應該\"\n",
      "2025-04-19 00:11:01,686 : INFO : topic diff=0.317537, rho=0.286829\n",
      "2025-04-19 00:11:01,687 : INFO : PROGRESS: pass 3, at document #6000/16310\n",
      "2025-04-19 00:11:02,149 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:02,153 : INFO : topic #1 (0.111): 0.048*\"工作\" + 0.027*\"方式\" + 0.017*\"小時\" + 0.016*\"時間\" + 0.014*\"推定\" + 0.014*\"工資\" + 0.014*\"依法\" + 0.013*\"每日\" + 0.013*\"單位\" + 0.012*\"內容\"\n",
      "2025-04-19 00:11:02,153 : INFO : topic #8 (0.111): 0.031*\"工作\" + 0.013*\"第一項\" + 0.013*\"空白\" + 0.013*\"情形\" + 0.013*\"砍除\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.011*\"資訊\" + 0.011*\"方式\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:11:02,154 : INFO : topic #3 (0.111): 0.024*\"美國\" + 0.022*\"晶片\" + 0.017*\"中國\" + 0.016*\"表示\" + 0.016*\"台灣\" + 0.016*\"台積電\" + 0.015*\"半導體\" + 0.014*\"投資\" + 0.012*\"英特爾\" + 0.010*\"積電\"\n",
      "2025-04-19 00:11:02,155 : INFO : topic #2 (0.111): 0.018*\"工作\" + 0.016*\"公司\" + 0.010*\"面試\" + 0.007*\"問題\" + 0.007*\"比較\" + 0.006*\"知道\" + 0.006*\"時間\" + 0.006*\"覺得\" + 0.006*\"真的\" + 0.006*\"應該\"\n",
      "2025-04-19 00:11:02,155 : INFO : topic #0 (0.111): 0.028*\"工作\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.009*\"方式\" + 0.009*\"文字\" + 0.009*\"情形\" + 0.009*\"應徵\" + 0.009*\"徵才\" + 0.008*\"聯絡\" + 0.008*\"分類\"\n",
      "2025-04-19 00:11:02,156 : INFO : topic diff=0.166437, rho=0.286829\n",
      "2025-04-19 00:11:02,156 : INFO : PROGRESS: pass 3, at document #8000/16310\n",
      "2025-04-19 00:11:02,434 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:02,438 : INFO : topic #4 (0.111): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\" + 0.011*\"情形\" + 0.011*\"第一項\"\n",
      "2025-04-19 00:11:02,439 : INFO : topic #3 (0.111): 0.024*\"美國\" + 0.021*\"晶片\" + 0.017*\"中國\" + 0.017*\"台灣\" + 0.016*\"表示\" + 0.015*\"半導體\" + 0.015*\"台積電\" + 0.013*\"投資\" + 0.011*\"英特爾\" + 0.009*\"全球\"\n",
      "2025-04-19 00:11:02,439 : INFO : topic #5 (0.111): 0.016*\"公司\" + 0.009*\"技術\" + 0.007*\"工程師\" + 0.007*\"團隊\" + 0.007*\"產品\" + 0.006*\"開發\" + 0.006*\"員工\" + 0.006*\"台灣\" + 0.005*\"目前\" + 0.005*\"相關\"\n",
      "2025-04-19 00:11:02,440 : INFO : topic #7 (0.111): 0.048*\"日本\" + 0.035*\"加班費\" + 0.027*\"勞基法\" + 0.025*\"時間\" + 0.022*\"超過\" + 0.019*\"填寫\" + 0.017*\"勞工\" + 0.016*\"工作\" + 0.015*\"小時\" + 0.014*\"勞動\"\n",
      "2025-04-19 00:11:02,440 : INFO : topic #0 (0.111): 0.028*\"工作\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.009*\"方式\" + 0.009*\"徵才\" + 0.009*\"應徵\" + 0.009*\"文字\" + 0.009*\"情形\" + 0.008*\"聯絡\" + 0.008*\"分類\"\n",
      "2025-04-19 00:11:02,441 : INFO : topic diff=0.302470, rho=0.286829\n",
      "2025-04-19 00:11:02,441 : INFO : PROGRESS: pass 3, at document #10000/16310\n",
      "2025-04-19 00:11:02,652 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:02,657 : INFO : topic #7 (0.111): 0.048*\"日本\" + 0.045*\"加班費\" + 0.030*\"勞基法\" + 0.030*\"時間\" + 0.026*\"超過\" + 0.024*\"填寫\" + 0.022*\"小時\" + 0.021*\"工作\" + 0.017*\"勞工\" + 0.017*\"薪資\"\n",
      "2025-04-19 00:11:02,657 : INFO : topic #8 (0.111): 0.031*\"工作\" + 0.013*\"第一項\" + 0.013*\"空白\" + 0.013*\"情形\" + 0.013*\"砍除\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.011*\"資訊\" + 0.011*\"方式\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:11:02,658 : INFO : topic #2 (0.111): 0.020*\"工作\" + 0.018*\"公司\" + 0.016*\"面試\" + 0.010*\"問題\" + 0.009*\"比較\" + 0.009*\"覺得\" + 0.007*\"時間\" + 0.007*\"程式\" + 0.007*\"知道\" + 0.007*\"一些\"\n",
      "2025-04-19 00:11:02,658 : INFO : topic #6 (0.111): 0.030*\"報名\" + 0.028*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"資料\" + 0.012*\"研究\" + 0.012*\"人數\" + 0.012*\"車馬費\" + 0.012*\"參加\"\n",
      "2025-04-19 00:11:02,659 : INFO : topic #5 (0.111): 0.016*\"公司\" + 0.009*\"技術\" + 0.007*\"開發\" + 0.007*\"工程師\" + 0.007*\"團隊\" + 0.007*\"產品\" + 0.006*\"員工\" + 0.006*\"台灣\" + 0.006*\"目前\" + 0.005*\"相關\"\n",
      "2025-04-19 00:11:02,659 : INFO : topic diff=0.247877, rho=0.286829\n",
      "2025-04-19 00:11:02,659 : INFO : PROGRESS: pass 3, at document #12000/16310\n",
      "2025-04-19 00:11:02,862 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:02,866 : INFO : topic #3 (0.111): 0.021*\"晶片\" + 0.018*\"半導體\" + 0.018*\"美國\" + 0.017*\"台灣\" + 0.015*\"表示\" + 0.015*\"台積電\" + 0.014*\"中國\" + 0.010*\"投資\" + 0.010*\"全球\" + 0.009*\"積電\"\n",
      "2025-04-19 00:11:02,866 : INFO : topic #0 (0.111): 0.027*\"工作\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.009*\"徵才\" + 0.009*\"文字\" + 0.009*\"方式\" + 0.009*\"應徵\" + 0.009*\"情形\" + 0.008*\"聯絡\" + 0.008*\"分類\"\n",
      "2025-04-19 00:11:02,867 : INFO : topic #4 (0.111): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\" + 0.011*\"情形\" + 0.011*\"單位\"\n",
      "2025-04-19 00:11:02,867 : INFO : topic #8 (0.111): 0.031*\"工作\" + 0.013*\"第一項\" + 0.013*\"情形\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.011*\"資訊\" + 0.011*\"方式\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:11:02,868 : INFO : topic #7 (0.111): 0.078*\"日本\" + 0.042*\"加班費\" + 0.029*\"時間\" + 0.027*\"超過\" + 0.026*\"勞基法\" + 0.023*\"填寫\" + 0.022*\"小時\" + 0.022*\"工作\" + 0.018*\"薪資\" + 0.016*\"勞工\"\n",
      "2025-04-19 00:11:02,868 : INFO : topic diff=0.264252, rho=0.286829\n",
      "2025-04-19 00:11:02,869 : INFO : PROGRESS: pass 3, at document #14000/16310\n",
      "2025-04-19 00:11:03,166 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:03,177 : INFO : topic #6 (0.111): 0.028*\"活動\" + 0.027*\"報名\" + 0.016*\"電話\" + 0.015*\"研究\" + 0.012*\"問卷\" + 0.012*\"台北市\" + 0.011*\"舉辦\" + 0.011*\"參加\" + 0.011*\"進行\" + 0.011*\"參與\"\n",
      "2025-04-19 00:11:03,178 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.008*\"技術\" + 0.007*\"員工\" + 0.006*\"工程師\" + 0.006*\"台灣\" + 0.005*\"科技\" + 0.005*\"產品\" + 0.005*\"開發\" + 0.005*\"目前\" + 0.005*\"團隊\"\n",
      "2025-04-19 00:11:03,179 : INFO : topic #0 (0.111): 0.027*\"工作\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.009*\"徵才\" + 0.009*\"文字\" + 0.009*\"方式\" + 0.008*\"應徵\" + 0.008*\"情形\" + 0.008*\"聯絡\" + 0.008*\"分類\"\n",
      "2025-04-19 00:11:03,180 : INFO : topic #8 (0.111): 0.031*\"工作\" + 0.013*\"第一項\" + 0.013*\"情形\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.011*\"資訊\" + 0.011*\"方式\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:11:03,182 : INFO : topic #2 (0.111): 0.019*\"工作\" + 0.017*\"公司\" + 0.013*\"面試\" + 0.009*\"問題\" + 0.008*\"比較\" + 0.007*\"覺得\" + 0.007*\"知道\" + 0.007*\"時間\" + 0.006*\"真的\" + 0.006*\"應該\"\n",
      "2025-04-19 00:11:03,183 : INFO : topic diff=0.261292, rho=0.286829\n",
      "2025-04-19 00:11:03,183 : INFO : PROGRESS: pass 3, at document #16000/16310\n",
      "2025-04-19 00:11:03,410 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:03,414 : INFO : topic #0 (0.111): 0.026*\"工作\" + 0.010*\"資訊\" + 0.009*\"內容\" + 0.009*\"徵才\" + 0.009*\"方式\" + 0.008*\"文字\" + 0.008*\"情形\" + 0.008*\"應徵\" + 0.008*\"聯絡\" + 0.007*\"分類\"\n",
      "2025-04-19 00:11:03,415 : INFO : topic #3 (0.111): 0.021*\"晶片\" + 0.021*\"美國\" + 0.018*\"台灣\" + 0.017*\"表示\" + 0.017*\"半導體\" + 0.016*\"中國\" + 0.016*\"台積電\" + 0.012*\"英特爾\" + 0.009*\"全球\" + 0.009*\"積電\"\n",
      "2025-04-19 00:11:03,415 : INFO : topic #2 (0.111): 0.018*\"工作\" + 0.017*\"公司\" + 0.012*\"面試\" + 0.008*\"問題\" + 0.008*\"比較\" + 0.007*\"覺得\" + 0.007*\"主管\" + 0.006*\"知道\" + 0.006*\"真的\" + 0.006*\"時間\"\n",
      "2025-04-19 00:11:03,416 : INFO : topic #7 (0.111): 0.101*\"日本\" + 0.032*\"加班費\" + 0.028*\"勞工\" + 0.024*\"超過\" + 0.024*\"時間\" + 0.019*\"勞基法\" + 0.018*\"工作\" + 0.018*\"小時\" + 0.018*\"薪資\" + 0.015*\"日圓\"\n",
      "2025-04-19 00:11:03,417 : INFO : topic #4 (0.111): 0.032*\"工作\" + 0.015*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"資訊\" + 0.011*\"情形\" + 0.011*\"單位\"\n",
      "2025-04-19 00:11:03,417 : INFO : topic diff=0.224013, rho=0.286829\n",
      "2025-04-19 00:11:03,515 : INFO : -8.521 per-word bound, 367.3 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:11:03,515 : INFO : PROGRESS: pass 3, at document #16310/16310\n",
      "2025-04-19 00:11:03,548 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:03,552 : INFO : topic #0 (0.111): 0.024*\"工作\" + 0.010*\"資訊\" + 0.010*\"國定假日\" + 0.010*\"徵才\" + 0.010*\"內容\" + 0.009*\"應徵\" + 0.008*\"水桶\" + 0.008*\"方式\" + 0.008*\"文字\" + 0.008*\"單位\"\n",
      "2025-04-19 00:11:03,553 : INFO : topic #3 (0.111): 0.026*\"美國\" + 0.022*\"晶片\" + 0.018*\"台灣\" + 0.017*\"台積電\" + 0.016*\"表示\" + 0.015*\"中國\" + 0.015*\"半導體\" + 0.013*\"投資\" + 0.012*\"英特爾\" + 0.010*\"積電\"\n",
      "2025-04-19 00:11:03,553 : INFO : topic #2 (0.111): 0.017*\"公司\" + 0.017*\"工作\" + 0.010*\"面試\" + 0.008*\"問題\" + 0.008*\"知道\" + 0.007*\"真的\" + 0.007*\"比較\" + 0.007*\"覺得\" + 0.006*\"應該\" + 0.006*\"現在\"\n",
      "2025-04-19 00:11:03,554 : INFO : topic #1 (0.111): 0.054*\"工作\" + 0.027*\"方式\" + 0.019*\"小時\" + 0.016*\"時間\" + 0.014*\"每日\" + 0.012*\"聯絡\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.012*\"單位\" + 0.011*\"依法\"\n",
      "2025-04-19 00:11:03,555 : INFO : topic #6 (0.111): 0.025*\"活動\" + 0.023*\"報名\" + 0.018*\"研究\" + 0.016*\"問卷\" + 0.012*\"電話\" + 0.011*\"台北市\" + 0.011*\"時間\" + 0.011*\"參與\" + 0.011*\"進行\" + 0.010*\"舉辦\"\n",
      "2025-04-19 00:11:03,555 : INFO : topic diff=0.227837, rho=0.286829\n",
      "2025-04-19 00:11:03,555 : INFO : PROGRESS: pass 4, at document #2000/16310\n",
      "2025-04-19 00:11:04,164 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:04,169 : INFO : topic #1 (0.111): 0.050*\"工作\" + 0.027*\"方式\" + 0.017*\"小時\" + 0.016*\"時間\" + 0.014*\"工資\" + 0.013*\"推定\" + 0.013*\"每日\" + 0.013*\"依法\" + 0.012*\"單位\" + 0.012*\"內容\"\n",
      "2025-04-19 00:11:04,169 : INFO : topic #7 (0.111): 0.075*\"日本\" + 0.036*\"勞工\" + 0.028*\"加班費\" + 0.021*\"時間\" + 0.019*\"小時\" + 0.019*\"超過\" + 0.018*\"工時\" + 0.015*\"勞基法\" + 0.015*\"工作\" + 0.015*\"薪資\"\n",
      "2025-04-19 00:11:04,170 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.009*\"技術\" + 0.008*\"員工\" + 0.007*\"科技\" + 0.006*\"台積\" + 0.006*\"報導\" + 0.005*\"台灣\" + 0.005*\"產品\" + 0.005*\"工程師\" + 0.004*\"目前\"\n",
      "2025-04-19 00:11:04,170 : INFO : topic #4 (0.111): 0.032*\"工作\" + 0.016*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"單位\"\n",
      "2025-04-19 00:11:04,171 : INFO : topic #6 (0.111): 0.028*\"報名\" + 0.027*\"活動\" + 0.018*\"電話\" + 0.015*\"台北市\" + 0.014*\"舉辦\" + 0.013*\"研究\" + 0.013*\"參與\" + 0.011*\"車馬費\" + 0.011*\"人數\" + 0.011*\"時間\"\n",
      "2025-04-19 00:11:04,171 : INFO : topic diff=0.657799, rho=0.275711\n",
      "2025-04-19 00:11:04,172 : INFO : PROGRESS: pass 4, at document #4000/16310\n",
      "2025-04-19 00:11:04,799 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:04,803 : INFO : topic #6 (0.111): 0.030*\"報名\" + 0.028*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.014*\"舉辦\" + 0.013*\"車馬費\" + 0.013*\"人數\" + 0.012*\"參與\" + 0.012*\"資料\" + 0.011*\"訪問\"\n",
      "2025-04-19 00:11:04,803 : INFO : topic #7 (0.111): 0.054*\"日本\" + 0.025*\"勞工\" + 0.022*\"時間\" + 0.021*\"加班費\" + 0.016*\"超過\" + 0.016*\"小時\" + 0.015*\"薪資\" + 0.014*\"工作\" + 0.014*\"勞基法\" + 0.014*\"工時\"\n",
      "2025-04-19 00:11:04,804 : INFO : topic #4 (0.111): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"單位\"\n",
      "2025-04-19 00:11:04,805 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.009*\"技術\" + 0.008*\"員工\" + 0.007*\"科技\" + 0.006*\"台積\" + 0.006*\"報導\" + 0.005*\"台灣\" + 0.005*\"產品\" + 0.004*\"工程師\" + 0.004*\"相關\"\n",
      "2025-04-19 00:11:04,805 : INFO : topic #3 (0.111): 0.026*\"美國\" + 0.021*\"晶片\" + 0.018*\"台灣\" + 0.017*\"台積電\" + 0.016*\"表示\" + 0.016*\"中國\" + 0.014*\"半導體\" + 0.013*\"投資\" + 0.012*\"英特爾\" + 0.010*\"積電\"\n",
      "2025-04-19 00:11:04,806 : INFO : topic diff=0.302198, rho=0.275711\n",
      "2025-04-19 00:11:04,806 : INFO : PROGRESS: pass 4, at document #6000/16310\n",
      "2025-04-19 00:11:05,320 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:05,324 : INFO : topic #1 (0.111): 0.049*\"工作\" + 0.027*\"方式\" + 0.017*\"小時\" + 0.016*\"時間\" + 0.014*\"工資\" + 0.014*\"推定\" + 0.014*\"每日\" + 0.014*\"依法\" + 0.013*\"單位\" + 0.012*\"內容\"\n",
      "2025-04-19 00:11:05,324 : INFO : topic #0 (0.111): 0.025*\"工作\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.010*\"徵才\" + 0.008*\"文字\" + 0.008*\"國定假日\" + 0.008*\"分類\" + 0.008*\"應徵\" + 0.008*\"情形\" + 0.008*\"方式\"\n",
      "2025-04-19 00:11:05,325 : INFO : topic #2 (0.111): 0.017*\"公司\" + 0.017*\"工作\" + 0.010*\"面試\" + 0.008*\"問題\" + 0.007*\"知道\" + 0.007*\"比較\" + 0.006*\"時間\" + 0.006*\"真的\" + 0.006*\"覺得\" + 0.006*\"應該\"\n",
      "2025-04-19 00:11:05,325 : INFO : topic #8 (0.111): 0.031*\"工作\" + 0.013*\"第一項\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.013*\"情形\" + 0.011*\"文字\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.011*\"方式\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:11:05,326 : INFO : topic #6 (0.111): 0.032*\"報名\" + 0.028*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.014*\"車馬費\" + 0.014*\"舉辦\" + 0.013*\"人數\" + 0.013*\"訪問\" + 0.012*\"資料\" + 0.011*\"參加\"\n",
      "2025-04-19 00:11:05,326 : INFO : topic diff=0.161728, rho=0.275711\n",
      "2025-04-19 00:11:05,327 : INFO : PROGRESS: pass 4, at document #8000/16310\n",
      "2025-04-19 00:11:05,570 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:05,574 : INFO : topic #8 (0.111): 0.031*\"工作\" + 0.013*\"第一項\" + 0.013*\"砍除\" + 0.013*\"情形\" + 0.013*\"空白\" + 0.011*\"文字\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.011*\"方式\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:11:05,575 : INFO : topic #5 (0.111): 0.016*\"公司\" + 0.009*\"技術\" + 0.007*\"產品\" + 0.007*\"團隊\" + 0.007*\"開發\" + 0.007*\"員工\" + 0.006*\"工程師\" + 0.005*\"台灣\" + 0.005*\"科技\" + 0.005*\"能力\"\n",
      "2025-04-19 00:11:05,575 : INFO : topic #0 (0.111): 0.025*\"工作\" + 0.011*\"資訊\" + 0.010*\"內容\" + 0.010*\"徵才\" + 0.008*\"文字\" + 0.008*\"應徵\" + 0.008*\"分類\" + 0.008*\"水桶\" + 0.008*\"國定假日\" + 0.008*\"方式\"\n",
      "2025-04-19 00:11:05,576 : INFO : topic #6 (0.111): 0.031*\"報名\" + 0.028*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.014*\"舉辦\" + 0.013*\"車馬費\" + 0.013*\"人數\" + 0.012*\"資料\" + 0.012*\"訪問\" + 0.012*\"參加\"\n",
      "2025-04-19 00:11:05,577 : INFO : topic #4 (0.111): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"單位\"\n",
      "2025-04-19 00:11:05,577 : INFO : topic diff=0.301738, rho=0.275711\n",
      "2025-04-19 00:11:05,578 : INFO : PROGRESS: pass 4, at document #10000/16310\n",
      "2025-04-19 00:11:05,807 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:05,812 : INFO : topic #4 (0.111): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"砍除\" + 0.013*\"空白\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"資訊\" + 0.011*\"單位\"\n",
      "2025-04-19 00:11:05,813 : INFO : topic #1 (0.111): 0.053*\"工作\" + 0.029*\"方式\" + 0.018*\"小時\" + 0.017*\"時間\" + 0.015*\"每日\" + 0.013*\"工資\" + 0.013*\"內容\" + 0.013*\"推定\" + 0.012*\"依法\" + 0.012*\"休息\"\n",
      "2025-04-19 00:11:05,816 : INFO : topic #5 (0.111): 0.016*\"公司\" + 0.009*\"技術\" + 0.008*\"開發\" + 0.007*\"團隊\" + 0.007*\"產品\" + 0.007*\"工程師\" + 0.006*\"員工\" + 0.005*\"台灣\" + 0.005*\"使用\" + 0.005*\"相關\"\n",
      "2025-04-19 00:11:05,817 : INFO : topic #6 (0.111): 0.031*\"報名\" + 0.028*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"資料\" + 0.012*\"人數\" + 0.012*\"研究\" + 0.012*\"車馬費\" + 0.012*\"參加\"\n",
      "2025-04-19 00:11:05,817 : INFO : topic #3 (0.111): 0.026*\"美國\" + 0.020*\"台灣\" + 0.018*\"晶片\" + 0.016*\"中國\" + 0.015*\"表示\" + 0.014*\"台積電\" + 0.014*\"半導體\" + 0.012*\"投資\" + 0.010*\"英特爾\" + 0.009*\"全球\"\n",
      "2025-04-19 00:11:05,818 : INFO : topic diff=0.237890, rho=0.275711\n",
      "2025-04-19 00:11:05,818 : INFO : PROGRESS: pass 4, at document #12000/16310\n",
      "2025-04-19 00:11:06,031 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:06,035 : INFO : topic #8 (0.111): 0.031*\"工作\" + 0.013*\"第一項\" + 0.013*\"砍除\" + 0.013*\"情形\" + 0.013*\"空白\" + 0.011*\"文字\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.011*\"方式\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:11:06,036 : INFO : topic #0 (0.111): 0.023*\"工作\" + 0.011*\"資訊\" + 0.010*\"徵才\" + 0.010*\"內容\" + 0.009*\"文字\" + 0.008*\"水桶\" + 0.008*\"分類\" + 0.008*\"應徵\" + 0.007*\"方式\" + 0.007*\"情形\"\n",
      "2025-04-19 00:11:06,036 : INFO : topic #3 (0.111): 0.020*\"晶片\" + 0.019*\"美國\" + 0.018*\"台灣\" + 0.017*\"半導體\" + 0.015*\"台積電\" + 0.015*\"表示\" + 0.013*\"中國\" + 0.010*\"投資\" + 0.009*\"全球\" + 0.009*\"積電\"\n",
      "2025-04-19 00:11:06,037 : INFO : topic #2 (0.111): 0.019*\"工作\" + 0.017*\"公司\" + 0.015*\"面試\" + 0.010*\"問題\" + 0.009*\"比較\" + 0.008*\"覺得\" + 0.007*\"知道\" + 0.007*\"時間\" + 0.007*\"程式\" + 0.006*\"一些\"\n",
      "2025-04-19 00:11:06,037 : INFO : topic #6 (0.111): 0.030*\"報名\" + 0.030*\"活動\" + 0.018*\"電話\" + 0.014*\"台北市\" + 0.013*\"研究\" + 0.013*\"舉辦\" + 0.012*\"參加\" + 0.012*\"資料\" + 0.012*\"人數\" + 0.011*\"時間\"\n",
      "2025-04-19 00:11:06,037 : INFO : topic diff=0.242141, rho=0.275711\n",
      "2025-04-19 00:11:06,038 : INFO : PROGRESS: pass 4, at document #14000/16310\n",
      "2025-04-19 00:11:06,329 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:06,333 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.008*\"技術\" + 0.007*\"員工\" + 0.006*\"開發\" + 0.006*\"產品\" + 0.005*\"科技\" + 0.005*\"工程師\" + 0.005*\"台灣\" + 0.005*\"團隊\" + 0.004*\"報導\"\n",
      "2025-04-19 00:11:06,335 : INFO : topic #0 (0.111): 0.023*\"工作\" + 0.011*\"資訊\" + 0.010*\"徵才\" + 0.010*\"內容\" + 0.008*\"文字\" + 0.008*\"水桶\" + 0.007*\"應徵\" + 0.007*\"分類\" + 0.007*\"方式\" + 0.007*\"情形\"\n",
      "2025-04-19 00:11:06,336 : INFO : topic #7 (0.111): 0.069*\"日本\" + 0.043*\"加班費\" + 0.032*\"時間\" + 0.030*\"工作\" + 0.029*\"薪資\" + 0.026*\"小時\" + 0.024*\"工時\" + 0.023*\"勞基法\" + 0.022*\"超過\" + 0.018*\"填寫\"\n",
      "2025-04-19 00:11:06,337 : INFO : topic #1 (0.111): 0.054*\"工作\" + 0.029*\"方式\" + 0.017*\"小時\" + 0.017*\"時間\" + 0.014*\"每日\" + 0.013*\"工資\" + 0.012*\"內容\" + 0.012*\"依法\" + 0.012*\"推定\" + 0.012*\"單位\"\n",
      "2025-04-19 00:11:06,341 : INFO : topic #3 (0.111): 0.020*\"晶片\" + 0.020*\"台灣\" + 0.018*\"美國\" + 0.016*\"半導體\" + 0.016*\"表示\" + 0.015*\"台積電\" + 0.015*\"中國\" + 0.010*\"英特爾\" + 0.009*\"全球\" + 0.009*\"產業\"\n",
      "2025-04-19 00:11:06,342 : INFO : topic diff=0.247436, rho=0.275711\n",
      "2025-04-19 00:11:06,343 : INFO : PROGRESS: pass 4, at document #16000/16310\n",
      "2025-04-19 00:11:06,558 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:06,561 : INFO : topic #7 (0.111): 0.074*\"日本\" + 0.037*\"加班費\" + 0.029*\"薪資\" + 0.029*\"時間\" + 0.028*\"工作\" + 0.026*\"工時\" + 0.025*\"小時\" + 0.024*\"勞工\" + 0.021*\"超過\" + 0.021*\"勞基法\"\n",
      "2025-04-19 00:11:06,562 : INFO : topic #2 (0.111): 0.018*\"工作\" + 0.017*\"公司\" + 0.012*\"面試\" + 0.009*\"問題\" + 0.008*\"比較\" + 0.007*\"知道\" + 0.007*\"主管\" + 0.007*\"覺得\" + 0.006*\"真的\" + 0.006*\"時間\"\n",
      "2025-04-19 00:11:06,562 : INFO : topic #5 (0.111): 0.014*\"公司\" + 0.009*\"技術\" + 0.008*\"員工\" + 0.006*\"科技\" + 0.006*\"報導\" + 0.005*\"產品\" + 0.005*\"台積\" + 0.005*\"台灣\" + 0.005*\"開發\" + 0.005*\"工程師\"\n",
      "2025-04-19 00:11:06,563 : INFO : topic #6 (0.111): 0.028*\"活動\" + 0.026*\"報名\" + 0.016*\"研究\" + 0.014*\"電話\" + 0.012*\"問卷\" + 0.011*\"舉辦\" + 0.011*\"參加\" + 0.011*\"台北市\" + 0.011*\"進行\" + 0.010*\"參與\"\n",
      "2025-04-19 00:11:06,564 : INFO : topic #3 (0.111): 0.021*\"美國\" + 0.021*\"晶片\" + 0.018*\"台灣\" + 0.016*\"表示\" + 0.016*\"半導體\" + 0.015*\"台積電\" + 0.015*\"中國\" + 0.011*\"英特爾\" + 0.009*\"全球\" + 0.009*\"產業\"\n",
      "2025-04-19 00:11:06,564 : INFO : topic diff=0.211250, rho=0.275711\n",
      "2025-04-19 00:11:06,631 : INFO : -8.481 per-word bound, 357.3 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 00:11:06,631 : INFO : PROGRESS: pass 4, at document #16310/16310\n",
      "2025-04-19 00:11:06,690 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 00:11:06,694 : INFO : topic #3 (0.111): 0.026*\"美國\" + 0.021*\"晶片\" + 0.019*\"台灣\" + 0.017*\"台積電\" + 0.016*\"表示\" + 0.015*\"中國\" + 0.014*\"半導體\" + 0.013*\"投資\" + 0.011*\"英特爾\" + 0.010*\"積電\"\n",
      "2025-04-19 00:11:06,694 : INFO : topic #4 (0.111): 0.032*\"工作\" + 0.015*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"資訊\"\n",
      "2025-04-19 00:11:06,695 : INFO : topic #8 (0.111): 0.030*\"工作\" + 0.013*\"第一項\" + 0.013*\"砍除\" + 0.013*\"情形\" + 0.013*\"空白\" + 0.011*\"文字\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.011*\"方式\" + 0.010*\"聯絡\"\n",
      "2025-04-19 00:11:06,695 : INFO : topic #2 (0.111): 0.017*\"公司\" + 0.017*\"工作\" + 0.010*\"面試\" + 0.009*\"問題\" + 0.008*\"知道\" + 0.007*\"真的\" + 0.007*\"比較\" + 0.007*\"現在\" + 0.006*\"覺得\" + 0.006*\"應該\"\n",
      "2025-04-19 00:11:06,696 : INFO : topic #7 (0.111): 0.068*\"日本\" + 0.040*\"加班費\" + 0.036*\"勞工\" + 0.035*\"工時\" + 0.031*\"小時\" + 0.028*\"薪資\" + 0.026*\"工作\" + 0.026*\"時間\" + 0.019*\"超過\" + 0.017*\"勞基法\"\n",
      "2025-04-19 00:11:06,696 : INFO : topic diff=0.210967, rho=0.275711\n",
      "2025-04-19 00:11:06,697 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel<num_terms=23261, num_topics=9, decay=0.5, chunksize=2000> in 16.17s', 'datetime': '2025-04-19T00:11:06.697129', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n",
      "2025-04-19 00:11:11,840 : INFO : -7.098 per-word bound, 137.0 perplexity estimate based on a held-out corpus of 16310 documents with 3460358 words\n",
      "2025-04-19 00:11:11,843 : INFO : using ParallelWordOccurrenceAccumulator<processes=7, batch_size=64> to estimate probabilities from sliding windows\n",
      "2025-04-19 00:11:15,516 : INFO : 1 batches submitted to accumulate stats from 64 documents (22660 virtual)\n",
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      "2025-04-19 00:11:20,311 : INFO : 7 accumulators retrieved from output queue\n",
      "2025-04-19 00:11:20,320 : INFO : accumulated word occurrence stats for 3451622 virtual documents\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "花費時間: 213.75661492347717 sec\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "\n",
    "topic_num_list = np.arange(2, 10)\n",
    "result = {\"topic_num\":[], \"perplexity\":[], \"pmi\":[]}\n",
    "model_set = dict()\n",
    "\n",
    "\n",
    "for topic_num in topic_num_list:\n",
    "    # perplexity\n",
    "    model = LdaModel(\n",
    "        corpus = corpus,\n",
    "        num_topics = topic_num ,\n",
    "        id2word=dictionary,\n",
    "        random_state = 1500,\n",
    "        passes=5 # 訓練次數\n",
    "        )\n",
    "    \n",
    "    loss = model.log_perplexity(corpus)\n",
    "    pmi = CoherenceModel(model=model, texts=docs, coherence='c_npmi').get_coherence()\n",
    "    perplexity = np.exp(-1. * loss)\n",
    "    \n",
    "    # model_set[f'k_{topic_num}'] = model\n",
    "    \n",
    "    result['topic_num'].append(topic_num)\n",
    "    result['perplexity'].append(perplexity)\n",
    "    result['pmi'].append(pmi)\n",
    "    \n",
    "print(f\"花費時間: {time.time() - t0} sec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic_num</th>\n",
       "      <th>perplexity</th>\n",
       "      <th>pmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1284.941121</td>\n",
       "      <td>-0.054689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1176.735561</td>\n",
       "      <td>-0.048405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1142.876082</td>\n",
       "      <td>-0.024995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1081.005299</td>\n",
       "      <td>-0.040911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1074.750669</td>\n",
       "      <td>0.000634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>1120.856056</td>\n",
       "      <td>-0.038652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>1135.193597</td>\n",
       "      <td>-0.021426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>1209.470612</td>\n",
       "      <td>-0.021101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   topic_num   perplexity       pmi\n",
       "0          2  1284.941121 -0.054689\n",
       "1          3  1176.735561 -0.048405\n",
       "2          4  1142.876082 -0.024995\n",
       "3          5  1081.005299 -0.040911\n",
       "4          6  1074.750669  0.000634\n",
       "5          7  1120.856056 -0.038652\n",
       "6          8  1135.193597 -0.021426\n",
       "7          9  1209.470612 -0.021101"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = pd.DataFrame(result)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wengwulin/Desktop/社群媒體分析/SMAenv/lib/python3.11/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
      "  warnings.warn(\n",
      "/Users/wengwulin/Desktop/社群媒體分析/SMAenv/lib/python3.11/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
      "  warnings.warn(\n",
      "/Users/wengwulin/Desktop/社群媒體分析/SMAenv/lib/python3.11/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='topic_num'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot.line(x='topic_num', y='perplexity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='topic_num'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot.line(x='topic_num', y='pmi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "就訓練結果來看，perplexity 在 6 不錯，coherence 最高則是 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 視覺化呈現結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-04-19 16:03:13,927 : INFO : using symmetric alpha at 0.16666666666666666\n",
      "2025-04-19 16:03:13,928 : INFO : using symmetric eta at 0.16666666666666666\n",
      "2025-04-19 16:03:13,931 : INFO : using serial LDA version on this node\n",
      "2025-04-19 16:03:13,942 : INFO : running online (multi-pass) LDA training, 6 topics, 5 passes over the supplied corpus of 16310 documents, updating model once every 2000 documents, evaluating perplexity every 16310 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2025-04-19 16:03:13,943 : INFO : PROGRESS: pass 0, at document #2000/16310\n",
      "2025-04-19 16:03:14,590 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:14,594 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.014*\"方式\" + 0.013*\"應徵\" + 0.012*\"推定\" + 0.012*\"單位\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.009*\"聯絡\"\n",
      "2025-04-19 16:03:14,595 : INFO : topic #1 (0.167): 0.030*\"工作\" + 0.016*\"方式\" + 0.012*\"推定\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"砍除\" + 0.011*\"第一項\" + 0.010*\"國定假日\" + 0.010*\"情形\" + 0.010*\"內容\"\n",
      "2025-04-19 16:03:14,596 : INFO : topic #4 (0.167): 0.038*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"方式\" + 0.011*\"聯絡\" + 0.010*\"第一項\" + 0.010*\"單位\" + 0.010*\"聯絡人\" + 0.009*\"情形\" + 0.009*\"內容\"\n",
      "2025-04-19 16:03:14,596 : INFO : topic #2 (0.167): 0.040*\"工作\" + 0.013*\"內容\" + 0.012*\"推定\" + 0.012*\"工資\" + 0.011*\"方式\" + 0.011*\"應徵\" + 0.010*\"情形\" + 0.010*\"聯絡\" + 0.010*\"砍除\" + 0.010*\"小時\"\n",
      "2025-04-19 16:03:14,597 : INFO : topic #5 (0.167): 0.017*\"工作\" + 0.012*\"方式\" + 0.011*\"空白\" + 0.010*\"聯絡\" + 0.010*\"應徵\" + 0.009*\"內容\" + 0.008*\"分類\" + 0.008*\"聯絡人\" + 0.008*\"資訊\" + 0.008*\"小時\"\n",
      "2025-04-19 16:03:14,597 : INFO : topic diff=6.809205, rho=1.000000\n",
      "2025-04-19 16:03:14,598 : INFO : PROGRESS: pass 0, at document #4000/16310\n",
      "2025-04-19 16:03:15,227 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:15,230 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.014*\"應徵\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.012*\"空白\" + 0.011*\"推定\" + 0.011*\"單位\" + 0.011*\"資訊\" + 0.010*\"第一項\" + 0.010*\"內容\"\n",
      "2025-04-19 16:03:15,230 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.015*\"方式\" + 0.012*\"砍除\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"單位\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:15,231 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.010*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"聯絡人\"\n",
      "2025-04-19 16:03:15,231 : INFO : topic #3 (0.167): 0.013*\"工作\" + 0.013*\"報名\" + 0.012*\"方式\" + 0.010*\"電話\" + 0.009*\"時間\" + 0.009*\"聯絡人\" + 0.009*\"活動\" + 0.008*\"聯絡\" + 0.008*\"內容\" + 0.008*\"資訊\"\n",
      "2025-04-19 16:03:15,232 : INFO : topic #5 (0.167): 0.013*\"工作\" + 0.013*\"方式\" + 0.012*\"報名\" + 0.012*\"活動\" + 0.012*\"電話\" + 0.010*\"聯絡\" + 0.010*\"時間\" + 0.010*\"台北市\" + 0.010*\"通知\" + 0.009*\"內容\"\n",
      "2025-04-19 16:03:15,232 : INFO : topic diff=0.648008, rho=0.707107\n",
      "2025-04-19 16:03:15,233 : INFO : PROGRESS: pass 0, at document #6000/16310\n",
      "2025-04-19 16:03:15,770 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:15,773 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.013*\"應徵\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 16:03:15,773 : INFO : topic #3 (0.167): 0.013*\"報名\" + 0.012*\"電話\" + 0.011*\"方式\" + 0.010*\"時間\" + 0.009*\"工作\" + 0.009*\"活動\" + 0.009*\"聯絡人\" + 0.009*\"公司\" + 0.009*\"聯絡\" + 0.008*\"內容\"\n",
      "2025-04-19 16:03:15,774 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 16:03:15,774 : INFO : topic #2 (0.167): 0.043*\"工作\" + 0.016*\"方式\" + 0.013*\"推定\" + 0.012*\"小時\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.011*\"單位\" + 0.010*\"未註明\" + 0.010*\"依法\" + 0.010*\"應徵\"\n",
      "2025-04-19 16:03:15,775 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:15,775 : INFO : topic diff=0.664007, rho=0.577350\n",
      "2025-04-19 16:03:15,776 : INFO : PROGRESS: pass 0, at document #8000/16310\n",
      "2025-04-19 16:03:16,066 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:16,069 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.013*\"應徵\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"第一項\" + 0.011*\"資訊\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 16:03:16,069 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 16:03:16,070 : INFO : topic #2 (0.167): 0.044*\"工作\" + 0.013*\"方式\" + 0.010*\"小時\" + 0.010*\"內容\" + 0.009*\"推定\" + 0.009*\"公司\" + 0.009*\"工資\" + 0.009*\"時間\" + 0.008*\"單位\" + 0.008*\"面試\"\n",
      "2025-04-19 16:03:16,070 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:16,071 : INFO : topic #3 (0.167): 0.013*\"報名\" + 0.011*\"電話\" + 0.011*\"方式\" + 0.009*\"時間\" + 0.009*\"工作\" + 0.009*\"公司\" + 0.009*\"活動\" + 0.009*\"聯絡人\" + 0.008*\"聯絡\" + 0.008*\"內容\"\n",
      "2025-04-19 16:03:16,071 : INFO : topic diff=0.848046, rho=0.500000\n",
      "2025-04-19 16:03:16,071 : INFO : PROGRESS: pass 0, at document #10000/16310\n",
      "2025-04-19 16:03:16,329 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:16,332 : INFO : topic #3 (0.167): 0.013*\"報名\" + 0.011*\"電話\" + 0.010*\"方式\" + 0.009*\"時間\" + 0.009*\"工作\" + 0.009*\"活動\" + 0.008*\"公司\" + 0.008*\"聯絡人\" + 0.008*\"聯絡\" + 0.008*\"內容\"\n",
      "2025-04-19 16:03:16,332 : INFO : topic #5 (0.167): 0.016*\"公司\" + 0.011*\"工作\" + 0.008*\"面試\" + 0.007*\"問題\" + 0.007*\"時間\" + 0.006*\"工程師\" + 0.006*\"開發\" + 0.006*\"經驗\" + 0.006*\"目前\" + 0.005*\"技術\"\n",
      "2025-04-19 16:03:16,333 : INFO : topic #2 (0.167): 0.044*\"工作\" + 0.012*\"方式\" + 0.010*\"小時\" + 0.009*\"內容\" + 0.009*\"公司\" + 0.009*\"覺得\" + 0.008*\"時間\" + 0.008*\"面試\" + 0.008*\"單位\" + 0.008*\"推定\"\n",
      "2025-04-19 16:03:16,333 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.012*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:16,334 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.012*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 16:03:16,334 : INFO : topic diff=0.569089, rho=0.447214\n",
      "2025-04-19 16:03:16,335 : INFO : PROGRESS: pass 0, at document #12000/16310\n",
      "2025-04-19 16:03:16,567 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:16,570 : INFO : topic #5 (0.167): 0.014*\"公司\" + 0.009*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.005*\"時間\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.005*\"目前\" + 0.005*\"技術\" + 0.005*\"經驗\"\n",
      "2025-04-19 16:03:16,571 : INFO : topic #3 (0.167): 0.014*\"報名\" + 0.013*\"活動\" + 0.011*\"方式\" + 0.010*\"電話\" + 0.009*\"時間\" + 0.009*\"聯絡\" + 0.008*\"工作\" + 0.008*\"連結\" + 0.008*\"公司\" + 0.007*\"聯絡人\"\n",
      "2025-04-19 16:03:16,571 : INFO : topic #2 (0.167): 0.042*\"工作\" + 0.011*\"方式\" + 0.009*\"公司\" + 0.009*\"小時\" + 0.008*\"覺得\" + 0.008*\"內容\" + 0.008*\"時間\" + 0.008*\"單位\" + 0.008*\"面試\" + 0.007*\"推定\"\n",
      "2025-04-19 16:03:16,572 : INFO : topic #0 (0.167): 0.028*\"工作\" + 0.013*\"應徵\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"空白\" + 0.010*\"第一項\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 16:03:16,572 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.016*\"推定\" + 0.015*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 16:03:16,573 : INFO : topic diff=0.534139, rho=0.408248\n",
      "2025-04-19 16:03:16,573 : INFO : PROGRESS: pass 0, at document #14000/16310\n",
      "2025-04-19 16:03:16,800 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:16,803 : INFO : topic #1 (0.167): 0.028*\"工作\" + 0.014*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.011*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.011*\"文字\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:16,803 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.008*\"工作\" + 0.006*\"台灣\" + 0.005*\"問題\" + 0.004*\"工程師\" + 0.004*\"面試\" + 0.004*\"技術\" + 0.004*\"時間\" + 0.004*\"目前\" + 0.004*\"員工\"\n",
      "2025-04-19 16:03:16,804 : INFO : topic #2 (0.167): 0.039*\"工作\" + 0.009*\"方式\" + 0.009*\"公司\" + 0.008*\"小時\" + 0.008*\"覺得\" + 0.008*\"內容\" + 0.008*\"單位\" + 0.007*\"時間\" + 0.007*\"面試\" + 0.006*\"工資\"\n",
      "2025-04-19 16:03:16,804 : INFO : topic #0 (0.167): 0.027*\"工作\" + 0.012*\"應徵\" + 0.012*\"砍除\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"第一項\" + 0.010*\"資訊\" + 0.010*\"單位\" + 0.010*\"推定\" + 0.010*\"內容\"\n",
      "2025-04-19 16:03:16,805 : INFO : topic #3 (0.167): 0.014*\"報名\" + 0.012*\"量子\" + 0.012*\"活動\" + 0.011*\"問卷\" + 0.011*\"研究\" + 0.010*\"方式\" + 0.008*\"時間\" + 0.008*\"連結\" + 0.008*\"電話\" + 0.008*\"聯絡\"\n",
      "2025-04-19 16:03:16,805 : INFO : topic diff=0.474830, rho=0.377964\n",
      "2025-04-19 16:03:16,806 : INFO : PROGRESS: pass 0, at document #16000/16310\n",
      "2025-04-19 16:03:17,052 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:17,055 : INFO : topic #4 (0.167): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.010*\"方式\" + 0.010*\"砍除\" + 0.010*\"國定假日\" + 0.010*\"單位\"\n",
      "2025-04-19 16:03:17,055 : INFO : topic #3 (0.167): 0.021*\"量子\" + 0.017*\"研究\" + 0.017*\"報名\" + 0.014*\"問卷\" + 0.012*\"活動\" + 0.010*\"眼鏡\" + 0.009*\"方式\" + 0.009*\"連結\" + 0.008*\"聯絡\" + 0.008*\"時間\"\n",
      "2025-04-19 16:03:17,056 : INFO : topic #0 (0.167): 0.026*\"工作\" + 0.012*\"應徵\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"第一項\" + 0.010*\"資訊\" + 0.009*\"單位\" + 0.009*\"推定\" + 0.009*\"內容\"\n",
      "2025-04-19 16:03:17,057 : INFO : topic #1 (0.167): 0.028*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"國定假日\" + 0.011*\"聯絡\" + 0.010*\"文字\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:17,057 : INFO : topic #2 (0.167): 0.037*\"工作\" + 0.009*\"公司\" + 0.008*\"方式\" + 0.008*\"小時\" + 0.007*\"內容\" + 0.007*\"覺得\" + 0.007*\"預期\" + 0.007*\"單位\" + 0.007*\"時間\" + 0.007*\"製程\"\n",
      "2025-04-19 16:03:17,058 : INFO : topic diff=0.378381, rho=0.353553\n",
      "2025-04-19 16:03:17,127 : INFO : -8.638 per-word bound, 398.4 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 16:03:17,127 : INFO : PROGRESS: pass 0, at document #16310/16310\n",
      "2025-04-19 16:03:17,164 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:17,167 : INFO : topic #3 (0.167): 0.028*\"問卷\" + 0.027*\"研究\" + 0.017*\"報名\" + 0.017*\"量子\" + 0.011*\"時間\" + 0.011*\"活動\" + 0.010*\"工作\" + 0.010*\"眼鏡\" + 0.010*\"連結\" + 0.009*\"填寫\"\n",
      "2025-04-19 16:03:17,167 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.007*\"美國\" + 0.007*\"台灣\" + 0.005*\"工作\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"台積電\" + 0.004*\"問題\"\n",
      "2025-04-19 16:03:17,168 : INFO : topic #4 (0.167): 0.035*\"工作\" + 0.015*\"推定\" + 0.014*\"空白\" + 0.011*\"第一項\" + 0.010*\"聯絡\" + 0.010*\"情形\" + 0.010*\"方式\" + 0.010*\"砍除\" + 0.010*\"國定假日\" + 0.009*\"單位\"\n",
      "2025-04-19 16:03:17,168 : INFO : topic #1 (0.167): 0.027*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"推定\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.010*\"連結\" + 0.010*\"文字\"\n",
      "2025-04-19 16:03:17,169 : INFO : topic #2 (0.167): 0.036*\"工作\" + 0.009*\"公司\" + 0.008*\"小時\" + 0.008*\"覺得\" + 0.008*\"預期\" + 0.007*\"方式\" + 0.007*\"單位\" + 0.007*\"時間\" + 0.007*\"內容\" + 0.006*\"製程\"\n",
      "2025-04-19 16:03:17,169 : INFO : topic diff=0.331835, rho=0.333333\n",
      "2025-04-19 16:03:17,169 : INFO : PROGRESS: pass 1, at document #2000/16310\n",
      "2025-04-19 16:03:17,774 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:17,777 : INFO : topic #1 (0.167): 0.030*\"工作\" + 0.015*\"方式\" + 0.012*\"砍除\" + 0.012*\"推定\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"文字\"\n",
      "2025-04-19 16:03:17,778 : INFO : topic #4 (0.167): 0.038*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.011*\"方式\" + 0.011*\"砍除\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"第一項\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 16:03:17,778 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.013*\"方式\" + 0.012*\"應徵\" + 0.010*\"內容\" + 0.010*\"推定\" + 0.010*\"單位\" + 0.009*\"資訊\" + 0.009*\"工資\" + 0.009*\"砍除\" + 0.009*\"聯絡\"\n",
      "2025-04-19 16:03:17,779 : INFO : topic #2 (0.167): 0.042*\"工作\" + 0.012*\"方式\" + 0.011*\"小時\" + 0.011*\"時間\" + 0.008*\"內容\" + 0.007*\"單位\" + 0.007*\"面試\" + 0.007*\"每日\" + 0.006*\"工資\" + 0.006*\"休息\"\n",
      "2025-04-19 16:03:17,779 : INFO : topic #3 (0.167): 0.025*\"報名\" + 0.021*\"活動\" + 0.017*\"電話\" + 0.013*\"台北市\" + 0.013*\"時間\" + 0.012*\"舉辦\" + 0.011*\"參與\" + 0.011*\"車馬費\" + 0.010*\"研究\" + 0.010*\"通知\"\n",
      "2025-04-19 16:03:17,780 : INFO : topic diff=1.299584, rho=0.313805\n",
      "2025-04-19 16:03:17,780 : INFO : PROGRESS: pass 1, at document #4000/16310\n",
      "2025-04-19 16:03:18,373 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:18,376 : INFO : topic #3 (0.167): 0.026*\"報名\" + 0.023*\"活動\" + 0.020*\"電話\" + 0.014*\"台北市\" + 0.013*\"時間\" + 0.012*\"車馬費\" + 0.012*\"舉辦\" + 0.012*\"人數\" + 0.011*\"通知\" + 0.010*\"資料\"\n",
      "2025-04-19 16:03:18,376 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.012*\"砍除\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:18,377 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.013*\"方式\" + 0.011*\"應徵\" + 0.010*\"推定\" + 0.010*\"內容\" + 0.009*\"單位\" + 0.009*\"工資\" + 0.009*\"聯絡\" + 0.009*\"文字\" + 0.009*\"資訊\"\n",
      "2025-04-19 16:03:18,377 : INFO : topic #2 (0.167): 0.045*\"工作\" + 0.016*\"方式\" + 0.014*\"小時\" + 0.014*\"時間\" + 0.010*\"每日\" + 0.009*\"內容\" + 0.009*\"工資\" + 0.009*\"面試\" + 0.008*\"休息\" + 0.008*\"單位\"\n",
      "2025-04-19 16:03:18,378 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.007*\"台灣\" + 0.007*\"美國\" + 0.005*\"工作\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"台積電\" + 0.004*\"問題\"\n",
      "2025-04-19 16:03:18,378 : INFO : topic diff=0.486948, rho=0.313805\n",
      "2025-04-19 16:03:18,379 : INFO : PROGRESS: pass 1, at document #6000/16310\n",
      "2025-04-19 16:03:18,879 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:18,882 : INFO : topic #3 (0.167): 0.029*\"報名\" + 0.025*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.013*\"車馬費\" + 0.013*\"時間\" + 0.012*\"舉辦\" + 0.012*\"人數\" + 0.012*\"通知\" + 0.011*\"訪問\"\n",
      "2025-04-19 16:03:18,883 : INFO : topic #1 (0.167): 0.030*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 16:03:18,883 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.013*\"方式\" + 0.011*\"應徵\" + 0.010*\"內容\" + 0.010*\"推定\" + 0.009*\"單位\" + 0.009*\"文字\" + 0.009*\"聯絡\" + 0.009*\"工資\" + 0.009*\"資訊\"\n",
      "2025-04-19 16:03:18,884 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:18,884 : INFO : topic #2 (0.167): 0.048*\"工作\" + 0.020*\"方式\" + 0.015*\"時間\" + 0.015*\"小時\" + 0.012*\"每日\" + 0.010*\"內容\" + 0.010*\"休息\" + 0.010*\"工資\" + 0.010*\"依法\" + 0.009*\"面試\"\n",
      "2025-04-19 16:03:18,885 : INFO : topic diff=0.306307, rho=0.313805\n",
      "2025-04-19 16:03:18,885 : INFO : PROGRESS: pass 1, at document #8000/16310\n",
      "2025-04-19 16:03:19,159 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:19,162 : INFO : topic #3 (0.167): 0.028*\"報名\" + 0.025*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.013*\"時間\" + 0.012*\"車馬費\" + 0.012*\"舉辦\" + 0.012*\"人數\" + 0.011*\"資料\" + 0.011*\"通知\"\n",
      "2025-04-19 16:03:19,163 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.013*\"方式\" + 0.011*\"應徵\" + 0.010*\"內容\" + 0.010*\"推定\" + 0.009*\"聯絡\" + 0.009*\"單位\" + 0.009*\"文字\" + 0.009*\"資訊\" + 0.009*\"工資\"\n",
      "2025-04-19 16:03:19,163 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.017*\"推定\" + 0.015*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:19,164 : INFO : topic #2 (0.167): 0.054*\"工作\" + 0.021*\"方式\" + 0.017*\"時間\" + 0.016*\"小時\" + 0.011*\"每日\" + 0.011*\"內容\" + 0.010*\"面試\" + 0.009*\"休息\" + 0.008*\"工時\" + 0.008*\"依法\"\n",
      "2025-04-19 16:03:19,165 : INFO : topic #5 (0.167): 0.015*\"公司\" + 0.008*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.005*\"技術\" + 0.005*\"開發\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"經驗\"\n",
      "2025-04-19 16:03:19,165 : INFO : topic diff=0.342322, rho=0.313805\n",
      "2025-04-19 16:03:19,165 : INFO : PROGRESS: pass 1, at document #10000/16310\n",
      "2025-04-19 16:03:19,389 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:19,392 : INFO : topic #3 (0.167): 0.029*\"報名\" + 0.025*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.013*\"時間\" + 0.012*\"舉辦\" + 0.012*\"車馬費\" + 0.011*\"人數\" + 0.011*\"研究\" + 0.011*\"資料\"\n",
      "2025-04-19 16:03:19,392 : INFO : topic #2 (0.167): 0.056*\"工作\" + 0.021*\"方式\" + 0.019*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"每日\" + 0.010*\"面試\" + 0.009*\"工時\" + 0.008*\"休息\" + 0.008*\"聯絡\"\n",
      "2025-04-19 16:03:19,393 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:19,393 : INFO : topic #1 (0.167): 0.030*\"工作\" + 0.015*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 16:03:19,394 : INFO : topic #0 (0.167): 0.029*\"工作\" + 0.013*\"方式\" + 0.011*\"應徵\" + 0.010*\"內容\" + 0.010*\"推定\" + 0.009*\"資訊\" + 0.009*\"聯絡\" + 0.009*\"單位\" + 0.009*\"文字\" + 0.009*\"工資\"\n",
      "2025-04-19 16:03:19,394 : INFO : topic diff=0.295443, rho=0.313805\n",
      "2025-04-19 16:03:19,394 : INFO : PROGRESS: pass 1, at document #12000/16310\n",
      "2025-04-19 16:03:19,599 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:19,601 : INFO : topic #4 (0.167): 0.037*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:19,602 : INFO : topic #5 (0.167): 0.014*\"公司\" + 0.008*\"工作\" + 0.006*\"面試\" + 0.006*\"問題\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.004*\"技術\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 16:03:19,602 : INFO : topic #2 (0.167): 0.055*\"工作\" + 0.020*\"方式\" + 0.019*\"時間\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.010*\"每日\" + 0.010*\"工時\" + 0.009*\"面試\" + 0.008*\"休息\" + 0.007*\"聯絡\"\n",
      "2025-04-19 16:03:19,603 : INFO : topic #1 (0.167): 0.030*\"工作\" + 0.014*\"方式\" + 0.013*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.011*\"文字\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 16:03:19,603 : INFO : topic #3 (0.167): 0.030*\"報名\" + 0.027*\"活動\" + 0.017*\"電話\" + 0.013*\"台北市\" + 0.013*\"研究\" + 0.012*\"時間\" + 0.012*\"舉辦\" + 0.012*\"問卷\" + 0.011*\"人數\" + 0.011*\"資料\"\n",
      "2025-04-19 16:03:19,604 : INFO : topic diff=0.310465, rho=0.313805\n",
      "2025-04-19 16:03:19,604 : INFO : PROGRESS: pass 1, at document #14000/16310\n",
      "2025-04-19 16:03:19,799 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:19,802 : INFO : topic #0 (0.167): 0.028*\"工作\" + 0.013*\"方式\" + 0.011*\"應徵\" + 0.010*\"內容\" + 0.009*\"推定\" + 0.009*\"文字\" + 0.009*\"單位\" + 0.009*\"資訊\" + 0.009*\"聯絡\" + 0.008*\"情形\"\n",
      "2025-04-19 16:03:19,802 : INFO : topic #4 (0.167): 0.036*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:19,803 : INFO : topic #3 (0.167): 0.028*\"報名\" + 0.026*\"活動\" + 0.016*\"電話\" + 0.016*\"研究\" + 0.014*\"問卷\" + 0.012*\"台北市\" + 0.012*\"時間\" + 0.011*\"舉辦\" + 0.011*\"人數\" + 0.010*\"參與\"\n",
      "2025-04-19 16:03:19,803 : INFO : topic #2 (0.167): 0.053*\"工作\" + 0.018*\"方式\" + 0.018*\"時間\" + 0.016*\"小時\" + 0.010*\"內容\" + 0.009*\"工時\" + 0.009*\"每日\" + 0.009*\"面試\" + 0.007*\"地點\" + 0.007*\"單位\"\n",
      "2025-04-19 16:03:19,804 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.007*\"工作\" + 0.006*\"台灣\" + 0.005*\"問題\" + 0.004*\"面試\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"目前\" + 0.004*\"員工\" + 0.003*\"美國\"\n",
      "2025-04-19 16:03:19,804 : INFO : topic diff=0.305308, rho=0.313805\n",
      "2025-04-19 16:03:19,804 : INFO : PROGRESS: pass 1, at document #16000/16310\n",
      "2025-04-19 16:03:19,985 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:19,988 : INFO : topic #0 (0.167): 0.027*\"工作\" + 0.012*\"方式\" + 0.011*\"應徵\" + 0.009*\"內容\" + 0.009*\"推定\" + 0.008*\"資訊\" + 0.008*\"文字\" + 0.008*\"單位\" + 0.008*\"聯絡\" + 0.008*\"工資\"\n",
      "2025-04-19 16:03:19,989 : INFO : topic #4 (0.167): 0.036*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:19,989 : INFO : topic #2 (0.167): 0.051*\"工作\" + 0.017*\"時間\" + 0.016*\"方式\" + 0.015*\"小時\" + 0.010*\"內容\" + 0.010*\"工時\" + 0.009*\"面試\" + 0.008*\"地點\" + 0.008*\"每日\" + 0.007*\"單位\"\n",
      "2025-04-19 16:03:19,990 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.011*\"文字\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 16:03:19,990 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"工作\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"表示\"\n",
      "2025-04-19 16:03:19,991 : INFO : topic diff=0.280011, rho=0.313805\n",
      "2025-04-19 16:03:20,054 : INFO : -8.469 per-word bound, 354.3 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 16:03:20,054 : INFO : PROGRESS: pass 1, at document #16310/16310\n",
      "2025-04-19 16:03:20,097 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:20,100 : INFO : topic #2 (0.167): 0.050*\"工作\" + 0.016*\"小時\" + 0.016*\"時間\" + 0.014*\"方式\" + 0.011*\"工時\" + 0.009*\"內容\" + 0.007*\"面試\" + 0.007*\"地點\" + 0.007*\"單位\" + 0.006*\"每日\"\n",
      "2025-04-19 16:03:20,101 : INFO : topic #4 (0.167): 0.036*\"工作\" + 0.016*\"推定\" + 0.014*\"空白\" + 0.011*\"砍除\" + 0.011*\"方式\" + 0.011*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:20,101 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.007*\"美國\" + 0.006*\"台灣\" + 0.005*\"工作\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"台積電\"\n",
      "2025-04-19 16:03:20,102 : INFO : topic #0 (0.167): 0.026*\"工作\" + 0.012*\"方式\" + 0.010*\"應徵\" + 0.009*\"單位\" + 0.009*\"內容\" + 0.009*\"推定\" + 0.008*\"國定假日\" + 0.008*\"聯絡\" + 0.008*\"資訊\" + 0.008*\"工資\"\n",
      "2025-04-19 16:03:20,102 : INFO : topic #1 (0.167): 0.029*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"單位\"\n",
      "2025-04-19 16:03:20,103 : INFO : topic diff=0.285298, rho=0.313805\n",
      "2025-04-19 16:03:20,103 : INFO : PROGRESS: pass 2, at document #2000/16310\n",
      "2025-04-19 16:03:20,738 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:20,741 : INFO : topic #3 (0.167): 0.028*\"報名\" + 0.025*\"活動\" + 0.018*\"電話\" + 0.014*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"研究\" + 0.012*\"時間\" + 0.012*\"參與\" + 0.012*\"人數\" + 0.011*\"車馬費\"\n",
      "2025-04-19 16:03:20,741 : INFO : topic #4 (0.167): 0.035*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"第一項\"\n",
      "2025-04-19 16:03:20,742 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.007*\"台灣\" + 0.006*\"美國\" + 0.005*\"工作\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"台積電\"\n",
      "2025-04-19 16:03:20,743 : INFO : topic #0 (0.167): 0.027*\"工作\" + 0.014*\"方式\" + 0.011*\"推定\" + 0.011*\"工資\" + 0.010*\"依法\" + 0.010*\"應徵\" + 0.010*\"發薪日\" + 0.010*\"內容\" + 0.010*\"單位\" + 0.009*\"聯絡\"\n",
      "2025-04-19 16:03:20,743 : INFO : topic #1 (0.167): 0.030*\"工作\" + 0.014*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"推定\" + 0.011*\"第一項\" + 0.011*\"文字\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.010*\"單位\"\n",
      "2025-04-19 16:03:20,744 : INFO : topic diff=0.967354, rho=0.299409\n",
      "2025-04-19 16:03:20,744 : INFO : PROGRESS: pass 2, at document #4000/16310\n",
      "2025-04-19 16:03:21,330 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:21,333 : INFO : topic #2 (0.167): 0.052*\"工作\" + 0.022*\"方式\" + 0.019*\"時間\" + 0.018*\"小時\" + 0.012*\"每日\" + 0.011*\"內容\" + 0.010*\"休息\" + 0.009*\"面試\" + 0.009*\"工時\" + 0.009*\"工資\"\n",
      "2025-04-19 16:03:21,333 : INFO : topic #4 (0.167): 0.035*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\"\n",
      "2025-04-19 16:03:21,334 : INFO : topic #3 (0.167): 0.029*\"報名\" + 0.026*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.013*\"人數\" + 0.012*\"時間\" + 0.011*\"資料\" + 0.011*\"通知\"\n",
      "2025-04-19 16:03:21,335 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"推定\" + 0.011*\"文字\" + 0.011*\"空白\" + 0.011*\"聯絡\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:21,335 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"工作\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 16:03:21,335 : INFO : topic diff=0.401616, rho=0.299409\n",
      "2025-04-19 16:03:21,336 : INFO : PROGRESS: pass 2, at document #6000/16310\n",
      "2025-04-19 16:03:21,827 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:21,830 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\"\n",
      "2025-04-19 16:03:21,831 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.011*\"空白\" + 0.011*\"聯絡\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:21,831 : INFO : topic #2 (0.167): 0.051*\"工作\" + 0.024*\"方式\" + 0.019*\"時間\" + 0.017*\"小時\" + 0.013*\"每日\" + 0.011*\"內容\" + 0.010*\"休息\" + 0.010*\"依法\" + 0.009*\"工資\" + 0.009*\"面試\"\n",
      "2025-04-19 16:03:21,832 : INFO : topic #0 (0.167): 0.027*\"工作\" + 0.014*\"方式\" + 0.014*\"推定\" + 0.011*\"工資\" + 0.011*\"依法\" + 0.011*\"未註明\" + 0.011*\"發薪日\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.010*\"內容\"\n",
      "2025-04-19 16:03:21,832 : INFO : topic #3 (0.167): 0.031*\"報名\" + 0.027*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.013*\"車馬費\" + 0.013*\"舉辦\" + 0.013*\"人數\" + 0.012*\"訪問\" + 0.012*\"資料\" + 0.012*\"時間\"\n",
      "2025-04-19 16:03:21,833 : INFO : topic diff=0.239542, rho=0.299409\n",
      "2025-04-19 16:03:21,833 : INFO : PROGRESS: pass 2, at document #8000/16310\n",
      "2025-04-19 16:03:22,084 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:22,087 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\"\n",
      "2025-04-19 16:03:22,088 : INFO : topic #2 (0.167): 0.056*\"工作\" + 0.023*\"方式\" + 0.021*\"時間\" + 0.018*\"小時\" + 0.012*\"內容\" + 0.011*\"每日\" + 0.011*\"面試\" + 0.010*\"工時\" + 0.009*\"休息\" + 0.008*\"聯絡\"\n",
      "2025-04-19 16:03:22,088 : INFO : topic #3 (0.167): 0.030*\"報名\" + 0.027*\"活動\" + 0.020*\"電話\" + 0.016*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.012*\"人數\" + 0.012*\"資料\" + 0.012*\"時間\" + 0.011*\"訪問\"\n",
      "2025-04-19 16:03:22,089 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.011*\"空白\" + 0.011*\"聯絡\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:22,089 : INFO : topic #0 (0.167): 0.027*\"工作\" + 0.014*\"方式\" + 0.014*\"推定\" + 0.011*\"工資\" + 0.011*\"依法\" + 0.011*\"應徵\" + 0.011*\"未註明\" + 0.010*\"發薪日\" + 0.010*\"單位\" + 0.010*\"內容\"\n",
      "2025-04-19 16:03:22,090 : INFO : topic diff=0.310660, rho=0.299409\n",
      "2025-04-19 16:03:22,090 : INFO : PROGRESS: pass 2, at document #10000/16310\n",
      "2025-04-19 16:03:22,299 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:22,301 : INFO : topic #0 (0.167): 0.027*\"工作\" + 0.014*\"方式\" + 0.014*\"推定\" + 0.011*\"依法\" + 0.011*\"工資\" + 0.010*\"應徵\" + 0.010*\"未註明\" + 0.010*\"單位\" + 0.010*\"發薪日\" + 0.010*\"內容\"\n",
      "2025-04-19 16:03:22,302 : INFO : topic #3 (0.167): 0.031*\"報名\" + 0.027*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.012*\"舉辦\" + 0.012*\"研究\" + 0.012*\"資料\" + 0.012*\"車馬費\" + 0.012*\"人數\" + 0.012*\"時間\"\n",
      "2025-04-19 16:03:22,302 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\"\n",
      "2025-04-19 16:03:22,303 : INFO : topic #5 (0.167): 0.016*\"公司\" + 0.008*\"工作\" + 0.007*\"面試\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.005*\"目前\" + 0.004*\"比較\" + 0.004*\"覺得\"\n",
      "2025-04-19 16:03:22,303 : INFO : topic #2 (0.167): 0.056*\"工作\" + 0.022*\"方式\" + 0.022*\"時間\" + 0.018*\"小時\" + 0.012*\"內容\" + 0.011*\"每日\" + 0.011*\"工時\" + 0.010*\"面試\" + 0.009*\"經驗\" + 0.008*\"聯絡\"\n",
      "2025-04-19 16:03:22,304 : INFO : topic diff=0.276710, rho=0.299409\n",
      "2025-04-19 16:03:22,304 : INFO : PROGRESS: pass 2, at document #12000/16310\n",
      "2025-04-19 16:03:22,501 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:22,504 : INFO : topic #0 (0.167): 0.026*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"依法\" + 0.010*\"工資\" + 0.010*\"應徵\" + 0.010*\"單位\" + 0.010*\"未註明\" + 0.010*\"發薪日\" + 0.009*\"內容\"\n",
      "2025-04-19 16:03:22,504 : INFO : topic #3 (0.167): 0.031*\"報名\" + 0.029*\"活動\" + 0.018*\"電話\" + 0.014*\"研究\" + 0.013*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"問卷\" + 0.011*\"資料\" + 0.011*\"人數\" + 0.011*\"時間\"\n",
      "2025-04-19 16:03:22,505 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.017*\"推定\" + 0.014*\"空白\" + 0.013*\"砍除\" + 0.012*\"方式\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\"\n",
      "2025-04-19 16:03:22,505 : INFO : topic #2 (0.167): 0.056*\"工作\" + 0.022*\"方式\" + 0.021*\"時間\" + 0.018*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"每日\" + 0.010*\"面試\" + 0.009*\"經驗\" + 0.008*\"地點\"\n",
      "2025-04-19 16:03:22,505 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.011*\"空白\" + 0.011*\"聯絡\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:22,506 : INFO : topic diff=0.284897, rho=0.299409\n",
      "2025-04-19 16:03:22,506 : INFO : PROGRESS: pass 2, at document #14000/16310\n",
      "2025-04-19 16:03:22,698 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:22,701 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"第一項\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.011*\"空白\" + 0.010*\"聯絡\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:22,701 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"問題\" + 0.004*\"面試\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"目前\" + 0.004*\"美國\" + 0.004*\"開發\"\n",
      "2025-04-19 16:03:22,702 : INFO : topic #2 (0.167): 0.054*\"工作\" + 0.020*\"時間\" + 0.020*\"方式\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.009*\"面試\" + 0.009*\"每日\" + 0.009*\"地點\" + 0.008*\"經驗\"\n",
      "2025-04-19 16:03:22,702 : INFO : topic #0 (0.167): 0.025*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.011*\"工資\" + 0.010*\"依法\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.010*\"未註明\" + 0.010*\"發薪日\" + 0.009*\"內容\"\n",
      "2025-04-19 16:03:22,703 : INFO : topic #3 (0.167): 0.030*\"報名\" + 0.029*\"活動\" + 0.016*\"研究\" + 0.016*\"電話\" + 0.014*\"問卷\" + 0.012*\"舉辦\" + 0.012*\"台北市\" + 0.011*\"人數\" + 0.011*\"參與\" + 0.011*\"參加\"\n",
      "2025-04-19 16:03:22,703 : INFO : topic diff=0.283423, rho=0.299409\n",
      "2025-04-19 16:03:22,704 : INFO : PROGRESS: pass 2, at document #16000/16310\n",
      "2025-04-19 16:03:22,880 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:22,883 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.013*\"方式\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.012*\"第一項\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.011*\"空白\" + 0.010*\"聯絡\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:22,884 : INFO : topic #3 (0.167): 0.029*\"報名\" + 0.028*\"活動\" + 0.019*\"研究\" + 0.015*\"電話\" + 0.014*\"問卷\" + 0.012*\"舉辦\" + 0.011*\"台北市\" + 0.011*\"參加\" + 0.011*\"人數\" + 0.011*\"參與\"\n",
      "2025-04-19 16:03:22,884 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.006*\"台灣\" + 0.005*\"工作\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"表示\"\n",
      "2025-04-19 16:03:22,885 : INFO : topic #4 (0.167): 0.034*\"工作\" + 0.017*\"推定\" + 0.013*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"第一項\"\n",
      "2025-04-19 16:03:22,885 : INFO : topic #0 (0.167): 0.025*\"工作\" + 0.013*\"方式\" + 0.012*\"推定\" + 0.010*\"工資\" + 0.010*\"依法\" + 0.010*\"單位\" + 0.009*\"應徵\" + 0.009*\"未註明\" + 0.009*\"發薪日\" + 0.009*\"內容\"\n",
      "2025-04-19 16:03:22,885 : INFO : topic diff=0.262100, rho=0.299409\n",
      "2025-04-19 16:03:22,950 : INFO : -8.448 per-word bound, 349.2 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 16:03:22,950 : INFO : PROGRESS: pass 2, at document #16310/16310\n",
      "2025-04-19 16:03:22,980 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:22,983 : INFO : topic #0 (0.167): 0.023*\"工作\" + 0.013*\"方式\" + 0.012*\"推定\" + 0.010*\"單位\" + 0.010*\"工資\" + 0.009*\"依法\" + 0.009*\"應徵\" + 0.009*\"未註明\" + 0.009*\"發薪日\" + 0.008*\"內容\"\n",
      "2025-04-19 16:03:22,984 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.005*\"工作\" + 0.005*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 16:03:22,984 : INFO : topic #3 (0.167): 0.027*\"活動\" + 0.027*\"報名\" + 0.022*\"研究\" + 0.019*\"問卷\" + 0.012*\"電話\" + 0.011*\"時間\" + 0.011*\"舉辦\" + 0.011*\"參與\" + 0.010*\"人數\" + 0.010*\"台北市\"\n",
      "2025-04-19 16:03:22,985 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 16:03:22,985 : INFO : topic #2 (0.167): 0.052*\"工作\" + 0.018*\"時間\" + 0.017*\"小時\" + 0.016*\"方式\" + 0.012*\"工時\" + 0.010*\"內容\" + 0.008*\"地點\" + 0.008*\"面試\" + 0.007*\"經驗\" + 0.007*\"以上\"\n",
      "2025-04-19 16:03:22,986 : INFO : topic diff=0.270630, rho=0.299409\n",
      "2025-04-19 16:03:22,986 : INFO : PROGRESS: pass 3, at document #2000/16310\n",
      "2025-04-19 16:03:23,574 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:23,577 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.005*\"工作\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 16:03:23,578 : INFO : topic #2 (0.167): 0.053*\"工作\" + 0.020*\"方式\" + 0.020*\"時間\" + 0.018*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"每日\" + 0.009*\"地點\" + 0.009*\"面試\" + 0.008*\"休息\"\n",
      "2025-04-19 16:03:23,579 : INFO : topic #0 (0.167): 0.025*\"工作\" + 0.017*\"推定\" + 0.017*\"方式\" + 0.015*\"依法\" + 0.015*\"工資\" + 0.014*\"未註明\" + 0.012*\"發薪日\" + 0.012*\"單位\" + 0.010*\"應徵\" + 0.009*\"排班\"\n",
      "2025-04-19 16:03:23,579 : INFO : topic #3 (0.167): 0.029*\"報名\" + 0.027*\"活動\" + 0.018*\"電話\" + 0.014*\"台北市\" + 0.014*\"舉辦\" + 0.013*\"研究\" + 0.012*\"參與\" + 0.012*\"人數\" + 0.012*\"車馬費\" + 0.011*\"時間\"\n",
      "2025-04-19 16:03:23,580 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.017*\"推定\" + 0.013*\"空白\" + 0.013*\"方式\" + 0.013*\"砍除\" + 0.012*\"單位\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"應徵\"\n",
      "2025-04-19 16:03:23,580 : INFO : topic diff=0.904409, rho=0.286829\n",
      "2025-04-19 16:03:23,580 : INFO : PROGRESS: pass 3, at document #4000/16310\n",
      "2025-04-19 16:03:24,160 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:24,163 : INFO : topic #0 (0.167): 0.027*\"工作\" + 0.021*\"推定\" + 0.018*\"方式\" + 0.016*\"未註明\" + 0.016*\"依法\" + 0.016*\"工資\" + 0.014*\"單位\" + 0.013*\"發薪日\" + 0.010*\"應徵\" + 0.010*\"排班\"\n",
      "2025-04-19 16:03:24,164 : INFO : topic #3 (0.167): 0.029*\"報名\" + 0.027*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.013*\"人數\" + 0.012*\"時間\" + 0.011*\"參與\" + 0.011*\"資料\"\n",
      "2025-04-19 16:03:24,164 : INFO : topic #2 (0.167): 0.054*\"工作\" + 0.022*\"方式\" + 0.021*\"時間\" + 0.019*\"小時\" + 0.011*\"內容\" + 0.011*\"每日\" + 0.010*\"工時\" + 0.009*\"面試\" + 0.009*\"地點\" + 0.009*\"休息\"\n",
      "2025-04-19 16:03:24,165 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.010*\"資訊\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:24,166 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"方式\" + 0.013*\"砍除\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"國定假日\"\n",
      "2025-04-19 16:03:24,166 : INFO : topic diff=0.383204, rho=0.286829\n",
      "2025-04-19 16:03:24,166 : INFO : PROGRESS: pass 3, at document #6000/16310\n",
      "2025-04-19 16:03:24,678 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:24,681 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"工作\" + 0.005*\"技術\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"晶片\" + 0.004*\"科技\"\n",
      "2025-04-19 16:03:24,681 : INFO : topic #1 (0.167): 0.032*\"工作\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.011*\"資訊\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:24,682 : INFO : topic #2 (0.167): 0.054*\"工作\" + 0.023*\"方式\" + 0.021*\"時間\" + 0.018*\"小時\" + 0.012*\"每日\" + 0.011*\"內容\" + 0.010*\"工時\" + 0.009*\"面試\" + 0.009*\"地點\" + 0.009*\"休息\"\n",
      "2025-04-19 16:03:24,682 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:24,683 : INFO : topic #3 (0.167): 0.031*\"報名\" + 0.028*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.014*\"車馬費\" + 0.013*\"舉辦\" + 0.013*\"人數\" + 0.012*\"訪問\" + 0.012*\"資料\" + 0.011*\"時間\"\n",
      "2025-04-19 16:03:24,683 : INFO : topic diff=0.225247, rho=0.286829\n",
      "2025-04-19 16:03:24,683 : INFO : PROGRESS: pass 3, at document #8000/16310\n",
      "2025-04-19 16:03:24,923 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:24,926 : INFO : topic #2 (0.167): 0.056*\"工作\" + 0.022*\"時間\" + 0.021*\"方式\" + 0.018*\"小時\" + 0.012*\"內容\" + 0.011*\"面試\" + 0.010*\"每日\" + 0.010*\"工時\" + 0.010*\"經驗\" + 0.008*\"地點\"\n",
      "2025-04-19 16:03:24,926 : INFO : topic #0 (0.167): 0.028*\"工作\" + 0.022*\"推定\" + 0.019*\"方式\" + 0.017*\"未註明\" + 0.016*\"依法\" + 0.016*\"工資\" + 0.015*\"單位\" + 0.014*\"發薪日\" + 0.010*\"應徵\" + 0.010*\"內容\"\n",
      "2025-04-19 16:03:24,927 : INFO : topic #1 (0.167): 0.032*\"工作\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.011*\"資訊\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:24,927 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:24,928 : INFO : topic #3 (0.167): 0.031*\"報名\" + 0.028*\"活動\" + 0.020*\"電話\" + 0.016*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.012*\"人數\" + 0.012*\"資料\" + 0.012*\"訪問\" + 0.011*\"時間\"\n",
      "2025-04-19 16:03:24,928 : INFO : topic diff=0.290583, rho=0.286829\n",
      "2025-04-19 16:03:24,929 : INFO : PROGRESS: pass 3, at document #10000/16310\n",
      "2025-04-19 16:03:25,144 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:25,147 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"文字\" + 0.011*\"資訊\" + 0.011*\"推定\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:25,147 : INFO : topic #0 (0.167): 0.028*\"工作\" + 0.022*\"推定\" + 0.019*\"方式\" + 0.017*\"未註明\" + 0.016*\"依法\" + 0.016*\"工資\" + 0.015*\"單位\" + 0.014*\"發薪日\" + 0.010*\"應徵\" + 0.010*\"內容\"\n",
      "2025-04-19 16:03:25,148 : INFO : topic #3 (0.167): 0.032*\"報名\" + 0.028*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"研究\" + 0.012*\"車馬費\" + 0.012*\"資料\" + 0.012*\"人數\" + 0.011*\"時間\"\n",
      "2025-04-19 16:03:25,148 : INFO : topic #5 (0.167): 0.016*\"公司\" + 0.007*\"工作\" + 0.007*\"面試\" + 0.007*\"問題\" + 0.006*\"工程師\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.005*\"目前\" + 0.004*\"比較\" + 0.004*\"台灣\"\n",
      "2025-04-19 16:03:25,149 : INFO : topic #2 (0.167): 0.056*\"工作\" + 0.022*\"時間\" + 0.020*\"方式\" + 0.017*\"小時\" + 0.012*\"內容\" + 0.011*\"經驗\" + 0.011*\"工時\" + 0.010*\"面試\" + 0.009*\"每日\" + 0.008*\"地點\"\n",
      "2025-04-19 16:03:25,149 : INFO : topic diff=0.260057, rho=0.286829\n",
      "2025-04-19 16:03:25,150 : INFO : PROGRESS: pass 3, at document #12000/16310\n",
      "2025-04-19 16:03:25,349 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:25,352 : INFO : topic #3 (0.167): 0.032*\"報名\" + 0.030*\"活動\" + 0.018*\"電話\" + 0.015*\"研究\" + 0.013*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"問卷\" + 0.012*\"人數\" + 0.012*\"資料\" + 0.012*\"參加\"\n",
      "2025-04-19 16:03:25,352 : INFO : topic #0 (0.167): 0.028*\"工作\" + 0.021*\"推定\" + 0.019*\"方式\" + 0.017*\"未註明\" + 0.016*\"依法\" + 0.015*\"工資\" + 0.015*\"單位\" + 0.013*\"發薪日\" + 0.010*\"應徵\" + 0.010*\"內容\"\n",
      "2025-04-19 16:03:25,353 : INFO : topic #2 (0.167): 0.055*\"工作\" + 0.022*\"時間\" + 0.019*\"方式\" + 0.017*\"小時\" + 0.012*\"內容\" + 0.011*\"工時\" + 0.011*\"經驗\" + 0.010*\"面試\" + 0.008*\"每日\" + 0.008*\"地點\"\n",
      "2025-04-19 16:03:25,353 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:25,354 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"文字\" + 0.011*\"資訊\" + 0.010*\"推定\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:25,354 : INFO : topic diff=0.268807, rho=0.286829\n",
      "2025-04-19 16:03:25,354 : INFO : PROGRESS: pass 3, at document #14000/16310\n",
      "2025-04-19 16:03:25,545 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:25,548 : INFO : topic #2 (0.167): 0.054*\"工作\" + 0.021*\"時間\" + 0.018*\"方式\" + 0.016*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"經驗\" + 0.009*\"面試\" + 0.008*\"地點\" + 0.008*\"每日\"\n",
      "2025-04-19 16:03:25,549 : INFO : topic #3 (0.167): 0.030*\"報名\" + 0.029*\"活動\" + 0.017*\"研究\" + 0.017*\"電話\" + 0.014*\"問卷\" + 0.013*\"舉辦\" + 0.012*\"台北市\" + 0.011*\"人數\" + 0.011*\"參與\" + 0.011*\"參加\"\n",
      "2025-04-19 16:03:25,549 : INFO : topic #0 (0.167): 0.027*\"工作\" + 0.021*\"推定\" + 0.018*\"方式\" + 0.016*\"未註明\" + 0.016*\"工資\" + 0.016*\"單位\" + 0.015*\"依法\" + 0.013*\"發薪日\" + 0.009*\"應徵\" + 0.009*\"內容\"\n",
      "2025-04-19 16:03:25,550 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:25,550 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.006*\"工作\" + 0.006*\"台灣\" + 0.005*\"問題\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"面試\" + 0.004*\"目前\" + 0.004*\"美國\" + 0.004*\"開發\"\n",
      "2025-04-19 16:03:25,550 : INFO : topic diff=0.269076, rho=0.286829\n",
      "2025-04-19 16:03:25,551 : INFO : PROGRESS: pass 3, at document #16000/16310\n",
      "2025-04-19 16:03:25,731 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:25,734 : INFO : topic #2 (0.167): 0.053*\"工作\" + 0.020*\"時間\" + 0.016*\"方式\" + 0.016*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"經驗\" + 0.009*\"面試\" + 0.009*\"地點\" + 0.007*\"以上\"\n",
      "2025-04-19 16:03:25,734 : INFO : topic #3 (0.167): 0.030*\"報名\" + 0.029*\"活動\" + 0.019*\"研究\" + 0.015*\"電話\" + 0.014*\"問卷\" + 0.013*\"舉辦\" + 0.011*\"參加\" + 0.011*\"台北市\" + 0.011*\"人數\" + 0.011*\"參與\"\n",
      "2025-04-19 16:03:25,735 : INFO : topic #0 (0.167): 0.026*\"工作\" + 0.020*\"推定\" + 0.018*\"方式\" + 0.016*\"單位\" + 0.016*\"未註明\" + 0.015*\"工資\" + 0.015*\"依法\" + 0.012*\"發薪日\" + 0.009*\"內容\" + 0.009*\"應徵\"\n",
      "2025-04-19 16:03:25,735 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.006*\"台灣\" + 0.005*\"工作\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"晶片\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"表示\"\n",
      "2025-04-19 16:03:25,736 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.012*\"空白\" + 0.011*\"文字\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:25,736 : INFO : topic diff=0.248752, rho=0.286829\n",
      "2025-04-19 16:03:25,800 : INFO : -8.440 per-word bound, 347.4 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 16:03:25,800 : INFO : PROGRESS: pass 3, at document #16310/16310\n",
      "2025-04-19 16:03:25,830 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:25,833 : INFO : topic #0 (0.167): 0.025*\"工作\" + 0.019*\"推定\" + 0.017*\"方式\" + 0.015*\"單位\" + 0.015*\"未註明\" + 0.015*\"工資\" + 0.014*\"依法\" + 0.012*\"發薪日\" + 0.009*\"內容\" + 0.009*\"通知\"\n",
      "2025-04-19 16:03:25,834 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"方式\" + 0.013*\"砍除\" + 0.011*\"內容\" + 0.011*\"單位\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:25,834 : INFO : topic #2 (0.167): 0.052*\"工作\" + 0.019*\"時間\" + 0.017*\"小時\" + 0.015*\"方式\" + 0.012*\"工時\" + 0.011*\"內容\" + 0.009*\"經驗\" + 0.008*\"面試\" + 0.008*\"地點\" + 0.007*\"以上\"\n",
      "2025-04-19 16:03:25,835 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.012*\"砍除\" + 0.012*\"方式\" + 0.011*\"空白\" + 0.011*\"文字\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:25,835 : INFO : topic #3 (0.167): 0.028*\"活動\" + 0.027*\"報名\" + 0.022*\"研究\" + 0.019*\"問卷\" + 0.013*\"電話\" + 0.012*\"舉辦\" + 0.011*\"參與\" + 0.011*\"時間\" + 0.010*\"人數\" + 0.010*\"台北市\"\n",
      "2025-04-19 16:03:25,835 : INFO : topic diff=0.259452, rho=0.286829\n",
      "2025-04-19 16:03:25,836 : INFO : PROGRESS: pass 4, at document #2000/16310\n",
      "2025-04-19 16:03:26,407 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:26,409 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"方式\" + 0.013*\"砍除\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"應徵\"\n",
      "2025-04-19 16:03:26,410 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"文字\" + 0.011*\"推定\" + 0.010*\"資訊\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:26,410 : INFO : topic #3 (0.167): 0.029*\"報名\" + 0.027*\"活動\" + 0.018*\"電話\" + 0.014*\"台北市\" + 0.014*\"舉辦\" + 0.013*\"研究\" + 0.013*\"參與\" + 0.012*\"人數\" + 0.012*\"車馬費\" + 0.011*\"時間\"\n",
      "2025-04-19 16:03:26,411 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.005*\"工作\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"問題\" + 0.004*\"表示\"\n",
      "2025-04-19 16:03:26,411 : INFO : topic #0 (0.167): 0.030*\"工作\" + 0.023*\"方式\" + 0.023*\"推定\" + 0.019*\"工資\" + 0.018*\"依法\" + 0.018*\"未註明\" + 0.017*\"單位\" + 0.014*\"發薪日\" + 0.012*\"休息\" + 0.011*\"每日\"\n",
      "2025-04-19 16:03:26,412 : INFO : topic diff=0.857019, rho=0.275711\n",
      "2025-04-19 16:03:26,412 : INFO : PROGRESS: pass 4, at document #4000/16310\n",
      "2025-04-19 16:03:26,977 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:26,981 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:26,982 : INFO : topic #3 (0.167): 0.030*\"報名\" + 0.028*\"活動\" + 0.020*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.013*\"人數\" + 0.012*\"參與\" + 0.011*\"時間\" + 0.011*\"資料\"\n",
      "2025-04-19 16:03:26,982 : INFO : topic #0 (0.167): 0.033*\"工作\" + 0.024*\"方式\" + 0.023*\"推定\" + 0.019*\"工資\" + 0.018*\"未註明\" + 0.018*\"依法\" + 0.018*\"單位\" + 0.015*\"發薪日\" + 0.012*\"休息\" + 0.012*\"每日\"\n",
      "2025-04-19 16:03:26,983 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.013*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"空白\" + 0.011*\"方式\" + 0.011*\"文字\" + 0.010*\"資訊\" + 0.010*\"推定\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:26,983 : INFO : topic #2 (0.167): 0.054*\"工作\" + 0.022*\"時間\" + 0.018*\"小時\" + 0.018*\"方式\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.009*\"面試\" + 0.009*\"地點\" + 0.008*\"每日\" + 0.008*\"經驗\"\n",
      "2025-04-19 16:03:26,983 : INFO : topic diff=0.351575, rho=0.275711\n",
      "2025-04-19 16:03:26,984 : INFO : PROGRESS: pass 4, at document #6000/16310\n",
      "2025-04-19 16:03:27,483 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:27,486 : INFO : topic #2 (0.167): 0.053*\"工作\" + 0.022*\"時間\" + 0.018*\"方式\" + 0.017*\"小時\" + 0.011*\"內容\" + 0.010*\"工時\" + 0.009*\"面試\" + 0.009*\"地點\" + 0.009*\"經驗\" + 0.008*\"每日\"\n",
      "2025-04-19 16:03:27,487 : INFO : topic #3 (0.167): 0.031*\"報名\" + 0.028*\"活動\" + 0.021*\"電話\" + 0.016*\"台北市\" + 0.014*\"車馬費\" + 0.013*\"舉辦\" + 0.013*\"人數\" + 0.012*\"訪問\" + 0.012*\"資料\" + 0.011*\"通知\"\n",
      "2025-04-19 16:03:27,487 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.013*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"空白\" + 0.011*\"方式\" + 0.011*\"文字\" + 0.011*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"推定\"\n",
      "2025-04-19 16:03:27,488 : INFO : topic #0 (0.167): 0.034*\"工作\" + 0.025*\"方式\" + 0.023*\"推定\" + 0.019*\"工資\" + 0.019*\"依法\" + 0.018*\"未註明\" + 0.017*\"單位\" + 0.015*\"發薪日\" + 0.013*\"休息\" + 0.013*\"每日\"\n",
      "2025-04-19 16:03:27,489 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.005*\"工作\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"晶片\" + 0.004*\"員工\" + 0.004*\"科技\"\n",
      "2025-04-19 16:03:27,489 : INFO : topic diff=0.209608, rho=0.275711\n",
      "2025-04-19 16:03:27,489 : INFO : PROGRESS: pass 4, at document #8000/16310\n",
      "2025-04-19 16:03:27,731 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:27,733 : INFO : topic #2 (0.167): 0.054*\"工作\" + 0.022*\"時間\" + 0.017*\"方式\" + 0.016*\"小時\" + 0.012*\"經驗\" + 0.012*\"內容\" + 0.011*\"面試\" + 0.010*\"工時\" + 0.007*\"地點\" + 0.007*\"每日\"\n",
      "2025-04-19 16:03:27,734 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.013*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"空白\" + 0.011*\"方式\" + 0.011*\"文字\" + 0.011*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"推定\"\n",
      "2025-04-19 16:03:27,734 : INFO : topic #3 (0.167): 0.031*\"報名\" + 0.028*\"活動\" + 0.020*\"電話\" + 0.016*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"車馬費\" + 0.013*\"人數\" + 0.012*\"資料\" + 0.012*\"訪問\" + 0.011*\"參與\"\n",
      "2025-04-19 16:03:27,735 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:27,735 : INFO : topic #5 (0.167): 0.016*\"公司\" + 0.006*\"工作\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.005*\"面試\" + 0.005*\"技術\" + 0.005*\"台灣\" + 0.005*\"開發\" + 0.004*\"目前\" + 0.004*\"產品\"\n",
      "2025-04-19 16:03:27,736 : INFO : topic diff=0.287010, rho=0.275711\n",
      "2025-04-19 16:03:27,736 : INFO : PROGRESS: pass 4, at document #10000/16310\n",
      "2025-04-19 16:03:27,949 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:27,952 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.013*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"空白\" + 0.011*\"方式\" + 0.011*\"文字\" + 0.011*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"推定\"\n",
      "2025-04-19 16:03:27,952 : INFO : topic #3 (0.167): 0.032*\"報名\" + 0.028*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.013*\"舉辦\" + 0.013*\"研究\" + 0.012*\"車馬費\" + 0.012*\"人數\" + 0.012*\"資料\" + 0.011*\"參加\"\n",
      "2025-04-19 16:03:27,953 : INFO : topic #2 (0.167): 0.054*\"工作\" + 0.022*\"時間\" + 0.016*\"方式\" + 0.015*\"小時\" + 0.013*\"經驗\" + 0.012*\"內容\" + 0.011*\"面試\" + 0.010*\"工時\" + 0.008*\"職缺\" + 0.007*\"薪資\"\n",
      "2025-04-19 16:03:27,953 : INFO : topic #5 (0.167): 0.016*\"公司\" + 0.007*\"工作\" + 0.007*\"問題\" + 0.006*\"面試\" + 0.006*\"工程師\" + 0.005*\"開發\" + 0.005*\"技術\" + 0.005*\"目前\" + 0.004*\"比較\" + 0.004*\"台灣\"\n",
      "2025-04-19 16:03:27,954 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:27,954 : INFO : topic diff=0.244169, rho=0.275711\n",
      "2025-04-19 16:03:27,955 : INFO : PROGRESS: pass 4, at document #12000/16310\n",
      "2025-04-19 16:03:28,159 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:28,161 : INFO : topic #5 (0.167): 0.014*\"公司\" + 0.006*\"工作\" + 0.006*\"問題\" + 0.006*\"面試\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.005*\"台灣\" + 0.005*\"開發\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 16:03:28,162 : INFO : topic #3 (0.167): 0.032*\"報名\" + 0.030*\"活動\" + 0.018*\"電話\" + 0.015*\"研究\" + 0.014*\"台北市\" + 0.013*\"舉辦\" + 0.012*\"人數\" + 0.012*\"問卷\" + 0.012*\"參加\" + 0.012*\"資料\"\n",
      "2025-04-19 16:03:28,162 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:28,163 : INFO : topic #2 (0.167): 0.053*\"工作\" + 0.021*\"時間\" + 0.015*\"方式\" + 0.015*\"小時\" + 0.013*\"經驗\" + 0.011*\"內容\" + 0.011*\"面試\" + 0.010*\"工時\" + 0.008*\"職缺\" + 0.008*\"薪資\"\n",
      "2025-04-19 16:03:28,163 : INFO : topic #0 (0.167): 0.033*\"工作\" + 0.025*\"方式\" + 0.022*\"推定\" + 0.018*\"工資\" + 0.018*\"依法\" + 0.018*\"單位\" + 0.017*\"未註明\" + 0.014*\"發薪日\" + 0.013*\"休息\" + 0.012*\"每日\"\n",
      "2025-04-19 16:03:28,164 : INFO : topic diff=0.254106, rho=0.275711\n",
      "2025-04-19 16:03:28,164 : INFO : PROGRESS: pass 4, at document #14000/16310\n",
      "2025-04-19 16:03:28,358 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:28,361 : INFO : topic #5 (0.167): 0.013*\"公司\" + 0.006*\"台灣\" + 0.005*\"工作\" + 0.005*\"問題\" + 0.004*\"工程師\" + 0.004*\"技術\" + 0.004*\"面試\" + 0.004*\"目前\" + 0.004*\"美國\" + 0.004*\"開發\"\n",
      "2025-04-19 16:03:28,362 : INFO : topic #3 (0.167): 0.030*\"報名\" + 0.030*\"活動\" + 0.017*\"電話\" + 0.017*\"研究\" + 0.014*\"問卷\" + 0.013*\"舉辦\" + 0.012*\"台北市\" + 0.012*\"人數\" + 0.011*\"參與\" + 0.011*\"參加\"\n",
      "2025-04-19 16:03:28,362 : INFO : topic #4 (0.167): 0.033*\"工作\" + 0.016*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:28,363 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"空白\" + 0.011*\"文字\" + 0.011*\"方式\" + 0.011*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"推定\"\n",
      "2025-04-19 16:03:28,363 : INFO : topic #2 (0.167): 0.052*\"工作\" + 0.020*\"時間\" + 0.014*\"小時\" + 0.014*\"方式\" + 0.012*\"經驗\" + 0.011*\"內容\" + 0.010*\"面試\" + 0.010*\"工時\" + 0.008*\"薪資\" + 0.008*\"職缺\"\n",
      "2025-04-19 16:03:28,363 : INFO : topic diff=0.255894, rho=0.275711\n",
      "2025-04-19 16:03:28,364 : INFO : PROGRESS: pass 4, at document #16000/16310\n",
      "2025-04-19 16:03:28,544 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:28,546 : INFO : topic #5 (0.167): 0.012*\"公司\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.005*\"工作\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"晶片\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"表示\"\n",
      "2025-04-19 16:03:28,547 : INFO : topic #4 (0.167): 0.032*\"工作\" + 0.015*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"情形\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:28,548 : INFO : topic #2 (0.167): 0.051*\"工作\" + 0.019*\"時間\" + 0.014*\"小時\" + 0.013*\"方式\" + 0.011*\"經驗\" + 0.011*\"內容\" + 0.010*\"面試\" + 0.010*\"工時\" + 0.008*\"薪資\" + 0.008*\"地點\"\n",
      "2025-04-19 16:03:28,548 : INFO : topic #0 (0.167): 0.032*\"工作\" + 0.024*\"方式\" + 0.021*\"推定\" + 0.019*\"工資\" + 0.018*\"單位\" + 0.018*\"依法\" + 0.017*\"未註明\" + 0.014*\"發薪日\" + 0.012*\"休息\" + 0.012*\"每日\"\n",
      "2025-04-19 16:03:28,549 : INFO : topic #3 (0.167): 0.030*\"報名\" + 0.030*\"活動\" + 0.019*\"研究\" + 0.015*\"電話\" + 0.014*\"問卷\" + 0.013*\"舉辦\" + 0.011*\"參加\" + 0.011*\"台北市\" + 0.011*\"人數\" + 0.011*\"參與\"\n",
      "2025-04-19 16:03:28,549 : INFO : topic diff=0.237228, rho=0.275711\n",
      "2025-04-19 16:03:28,612 : INFO : -8.434 per-word bound, 345.8 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 16:03:28,613 : INFO : PROGRESS: pass 4, at document #16310/16310\n",
      "2025-04-19 16:03:28,643 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:28,645 : INFO : topic #4 (0.167): 0.032*\"工作\" + 0.015*\"推定\" + 0.013*\"空白\" + 0.013*\"砍除\" + 0.013*\"方式\" + 0.011*\"內容\" + 0.011*\"情形\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:28,646 : INFO : topic #1 (0.167): 0.031*\"工作\" + 0.012*\"第一項\" + 0.012*\"砍除\" + 0.012*\"情形\" + 0.012*\"空白\" + 0.011*\"文字\" + 0.011*\"方式\" + 0.011*\"資訊\" + 0.010*\"聯絡\" + 0.010*\"推定\"\n",
      "2025-04-19 16:03:28,646 : INFO : topic #0 (0.167): 0.031*\"工作\" + 0.023*\"方式\" + 0.020*\"推定\" + 0.018*\"工資\" + 0.018*\"單位\" + 0.017*\"依法\" + 0.016*\"未註明\" + 0.013*\"發薪日\" + 0.012*\"每日\" + 0.012*\"休息\"\n",
      "2025-04-19 16:03:28,647 : INFO : topic #2 (0.167): 0.051*\"工作\" + 0.018*\"時間\" + 0.015*\"小時\" + 0.012*\"方式\" + 0.011*\"工時\" + 0.011*\"經驗\" + 0.011*\"內容\" + 0.009*\"面試\" + 0.008*\"薪資\" + 0.007*\"地點\"\n",
      "2025-04-19 16:03:28,647 : INFO : topic #3 (0.167): 0.029*\"活動\" + 0.028*\"報名\" + 0.022*\"研究\" + 0.018*\"問卷\" + 0.013*\"電話\" + 0.012*\"舉辦\" + 0.011*\"參與\" + 0.011*\"台北市\" + 0.011*\"人數\" + 0.011*\"時間\"\n",
      "2025-04-19 16:03:28,648 : INFO : topic diff=0.248883, rho=0.275711\n",
      "2025-04-19 16:03:28,648 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel<num_terms=23261, num_topics=6, decay=0.5, chunksize=2000> in 14.71s', 'datetime': '2025-04-19T16:03:28.648362', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n"
     ]
    }
   ],
   "source": [
    "best_model = LdaModel(\n",
    "    corpus = corpus,\n",
    "    num_topics = 6,\n",
    "    id2word=dictionary,\n",
    "    random_state = 1500,\n",
    "    passes = 5 # 訓練次數\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el1141014285289312473131027\" style=\"background-color:white;\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el1141014285289312473131027_data = {\"mdsDat\": {\"x\": [-0.2995354008706043, 0.24592003070312043, 0.22710065861777043, -0.09888834136774394, 0.1347712378653305, -0.20936818494787315], \"y\": [0.19387491557480888, 0.018139002811217792, 0.005808504015559244, 0.06344852704882578, -0.0009366181316837525, -0.2803343313187279], \"topics\": [1, 2, 3, 4, 5, 6], \"cluster\": [1, 1, 1, 1, 1, 1], \"Freq\": [37.79374206319901, 23.896788351038214, 20.401931061029455, 8.489952803267657, 5.2492680817173785, 4.168317639748281]}, \"tinfo\": {\"Term\": [\"\\u5de5\\u4f5c\", \"\\u63a8\\u5b9a\", \"\\u65b9\\u5f0f\", \"\\u55ae\\u4f4d\", \"\\u5de5\\u8cc7\", \"\\u672a\\u8a3b\\u660e\", \"\\u4f9d\\u6cd5\", \"\\u6642\\u9593\", \"\\u767c\\u85aa\\u65e5\", \"\\u7a7a\\u767d\", \"\\u780d\\u9664\", \"\\u5c0f\\u6642\", \"\\u7b2c\\u4e00\\u9805\", \"\\u60c5\\u5f62\", \"\\u6d3b\\u52d5\", \"\\u5167\\u5bb9\", \"\\u5831\\u540d\", \"\\u570b\\u5b9a\\u5047\\u65e5\", \"\\u806f\\u7d61\", \"\\u7814\\u7a76\", 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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el1141014285289312473131027\", ldavis_el1141014285289312473131027_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el1141014285289312473131027\", ldavis_el1141014285289312473131027_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el1141014285289312473131027\", ldavis_el1141014285289312473131027_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "5     -0.299535  0.193875       1        1  37.793742\n",
       "4      0.245920  0.018139       2        1  23.896788\n",
       "1      0.227101  0.005809       3        1  20.401931\n",
       "2     -0.098888  0.063449       4        1   8.489953\n",
       "0      0.134771 -0.000937       5        1   5.249268\n",
       "3     -0.209368 -0.280334       6        1   4.168318, topic_info=    Term          Freq         Total Category  logprob  loglift\n",
       "34    工作  74360.000000  74360.000000  Default  30.0000  30.0000\n",
       "321   推定  23439.000000  23439.000000  Default  29.0000  29.0000\n",
       "50    方式  27690.000000  27690.000000  Default  28.0000  28.0000\n",
       "23    單位  20105.000000  20105.000000  Default  27.0000  27.0000\n",
       "36    工資  15885.000000  15885.000000  Default  26.0000  26.0000\n",
       "..   ...           ...           ...      ...      ...      ...\n",
       "153   提供    998.682697   8272.551621   Topic6  -4.9727   1.0634\n",
       "73    聯絡   1056.003498  20408.887353   Topic6  -4.9169   0.2162\n",
       "12    內容    978.784041  23126.742818   Topic6  -4.9929   0.0152\n",
       "157   條件    919.634782   9214.971541   Topic6  -5.0552   0.8730\n",
       "50    方式    902.400500  27690.074502   Topic6  -5.0741  -0.2461\n",
       "\n",
       "[476 rows x 6 columns], token_table=      Topic      Freq  Term\n",
       "term                       \n",
       "210       2  0.544514    一律\n",
       "210       3  0.409652    一律\n",
       "210       4  0.011905    一律\n",
       "210       5  0.033849    一律\n",
       "213       1  0.003195   一次性\n",
       "...     ...       ...   ...\n",
       "5417      4  0.972331  高速公路\n",
       "5417      5  0.012004  高速公路\n",
       "672       2  0.027680    齊虹\n",
       "672       3  0.941125    齊虹\n",
       "672       5  0.027680    齊虹\n",
       "\n",
       "[1668 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[6, 5, 2, 3, 1, 4])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "p = pyLDAvis.gensim_models.prepare(best_model, corpus, dictionary)\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看(2,3,5) 很接近，試試看跑四個主題"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-04-19 16:03:44,013 : INFO : using symmetric alpha at 0.25\n",
      "2025-04-19 16:03:44,014 : INFO : using symmetric eta at 0.25\n",
      "2025-04-19 16:03:44,038 : INFO : using serial LDA version on this node\n",
      "2025-04-19 16:03:44,066 : INFO : running online (multi-pass) LDA training, 4 topics, 4 passes over the supplied corpus of 16310 documents, updating model once every 2000 documents, evaluating perplexity every 16310 documents, iterating 50x with a convergence threshold of 0.001000\n",
      "2025-04-19 16:03:44,066 : INFO : PROGRESS: pass 0, at document #2000/16310\n",
      "2025-04-19 16:03:44,648 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:44,650 : INFO : topic #0 (0.250): 0.029*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"應徵\" + 0.012*\"空白\" + 0.011*\"單位\" + 0.011*\"砍除\" + 0.010*\"內容\" + 0.010*\"聯絡\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:44,651 : INFO : topic #1 (0.250): 0.030*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"國定假日\" + 0.010*\"第一項\" + 0.010*\"空白\" + 0.010*\"情形\" + 0.010*\"砍除\"\n",
      "2025-04-19 16:03:44,651 : INFO : topic #2 (0.250): 0.040*\"工作\" + 0.013*\"內容\" + 0.013*\"推定\" + 0.012*\"工資\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.010*\"聯絡\" + 0.010*\"情形\" + 0.010*\"砍除\" + 0.010*\"小時\"\n",
      "2025-04-19 16:03:44,652 : INFO : topic #3 (0.250): 0.020*\"工作\" + 0.012*\"方式\" + 0.011*\"砍除\" + 0.010*\"聯絡人\" + 0.010*\"推定\" + 0.010*\"應徵\" + 0.009*\"空白\" + 0.009*\"文字\" + 0.008*\"資訊\" + 0.008*\"情形\"\n",
      "2025-04-19 16:03:44,652 : INFO : topic diff=5.686805, rho=1.000000\n",
      "2025-04-19 16:03:44,652 : INFO : PROGRESS: pass 0, at document #4000/16310\n",
      "2025-04-19 16:03:45,191 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:45,194 : INFO : topic #0 (0.250): 0.030*\"工作\" + 0.013*\"空白\" + 0.013*\"方式\" + 0.013*\"應徵\" + 0.012*\"推定\" + 0.011*\"砍除\" + 0.011*\"單位\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.010*\"第一項\"\n",
      "2025-04-19 16:03:45,194 : INFO : topic #1 (0.250): 0.030*\"工作\" + 0.014*\"方式\" + 0.013*\"推定\" + 0.012*\"第一項\" + 0.012*\"空白\" + 0.012*\"砍除\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"單位\"\n",
      "2025-04-19 16:03:45,195 : INFO : topic #2 (0.250): 0.042*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"工資\" + 0.012*\"內容\" + 0.011*\"小時\" + 0.011*\"應徵\" + 0.010*\"單位\" + 0.010*\"聯絡\" + 0.010*\"情形\"\n",
      "2025-04-19 16:03:45,195 : INFO : topic #3 (0.250): 0.014*\"報名\" + 0.012*\"活動\" + 0.012*\"電話\" + 0.011*\"工作\" + 0.011*\"方式\" + 0.009*\"時間\" + 0.009*\"台北市\" + 0.009*\"聯絡\" + 0.009*\"內容\" + 0.008*\"聯絡人\"\n",
      "2025-04-19 16:03:45,196 : INFO : topic diff=0.569569, rho=0.707107\n",
      "2025-04-19 16:03:45,196 : INFO : PROGRESS: pass 0, at document #6000/16310\n",
      "2025-04-19 16:03:45,667 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:45,670 : INFO : topic #0 (0.250): 0.030*\"工作\" + 0.012*\"應徵\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"推定\" + 0.011*\"砍除\" + 0.010*\"第一項\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.010*\"單位\"\n",
      "2025-04-19 16:03:45,670 : INFO : topic #1 (0.250): 0.031*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:45,671 : INFO : topic #2 (0.250): 0.042*\"工作\" + 0.015*\"方式\" + 0.013*\"推定\" + 0.012*\"內容\" + 0.011*\"工資\" + 0.011*\"小時\" + 0.010*\"依法\" + 0.010*\"單位\" + 0.010*\"應徵\" + 0.009*\"聯絡\"\n",
      "2025-04-19 16:03:45,671 : INFO : topic #3 (0.250): 0.017*\"報名\" + 0.014*\"活動\" + 0.013*\"電話\" + 0.010*\"台北市\" + 0.010*\"時間\" + 0.009*\"方式\" + 0.008*\"聯絡\" + 0.008*\"內容\" + 0.008*\"資料\" + 0.008*\"人數\"\n",
      "2025-04-19 16:03:45,672 : INFO : topic diff=0.765869, rho=0.577350\n",
      "2025-04-19 16:03:45,672 : INFO : PROGRESS: pass 0, at document #8000/16310\n",
      "2025-04-19 16:03:45,972 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:45,974 : INFO : topic #0 (0.250): 0.029*\"工作\" + 0.012*\"應徵\" + 0.012*\"空白\" + 0.012*\"方式\" + 0.011*\"推定\" + 0.011*\"砍除\" + 0.010*\"第一項\" + 0.010*\"內容\" + 0.010*\"資訊\" + 0.010*\"單位\"\n",
      "2025-04-19 16:03:45,975 : INFO : topic #1 (0.250): 0.031*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:45,975 : INFO : topic #2 (0.250): 0.041*\"工作\" + 0.011*\"方式\" + 0.010*\"面試\" + 0.009*\"內容\" + 0.009*\"小時\" + 0.008*\"推定\" + 0.008*\"時間\" + 0.007*\"工資\" + 0.007*\"公司\" + 0.007*\"應徵\"\n",
      "2025-04-19 16:03:45,976 : INFO : topic #3 (0.250): 0.016*\"公司\" + 0.008*\"時間\" + 0.007*\"問題\" + 0.006*\"工作\" + 0.006*\"工程師\" + 0.006*\"目前\" + 0.005*\"產品\" + 0.005*\"資料\" + 0.005*\"使用\" + 0.005*\"報名\"\n",
      "2025-04-19 16:03:45,976 : INFO : topic diff=1.008501, rho=0.500000\n",
      "2025-04-19 16:03:45,977 : INFO : PROGRESS: pass 0, at document #10000/16310\n",
      "2025-04-19 16:03:46,296 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:46,299 : INFO : topic #0 (0.250): 0.029*\"工作\" + 0.012*\"應徵\" + 0.012*\"空白\" + 0.011*\"方式\" + 0.011*\"推定\" + 0.011*\"砍除\" + 0.010*\"單位\" + 0.009*\"第一項\" + 0.009*\"內容\" + 0.009*\"資訊\"\n",
      "2025-04-19 16:03:46,299 : INFO : topic #1 (0.250): 0.031*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.011*\"資訊\"\n",
      "2025-04-19 16:03:46,300 : INFO : topic #2 (0.250): 0.044*\"工作\" + 0.011*\"面試\" + 0.010*\"方式\" + 0.009*\"小時\" + 0.008*\"內容\" + 0.008*\"時間\" + 0.007*\"公司\" + 0.007*\"推定\" + 0.006*\"應徵\" + 0.006*\"工資\"\n",
      "2025-04-19 16:03:46,301 : INFO : topic #3 (0.250): 0.016*\"公司\" + 0.007*\"問題\" + 0.007*\"工作\" + 0.007*\"時間\" + 0.006*\"面試\" + 0.006*\"目前\" + 0.006*\"工程師\" + 0.005*\"開發\" + 0.005*\"經驗\" + 0.005*\"技術\"\n",
      "2025-04-19 16:03:46,301 : INFO : topic diff=0.542565, rho=0.447214\n",
      "2025-04-19 16:03:46,301 : INFO : PROGRESS: pass 0, at document #12000/16310\n",
      "2025-04-19 16:03:46,591 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:46,594 : INFO : topic #0 (0.250): 0.028*\"工作\" + 0.011*\"應徵\" + 0.011*\"空白\" + 0.011*\"方式\" + 0.010*\"推定\" + 0.010*\"砍除\" + 0.009*\"單位\" + 0.009*\"內容\" + 0.009*\"資訊\" + 0.009*\"第一項\"\n",
      "2025-04-19 16:03:46,594 : INFO : topic #1 (0.250): 0.031*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"第一項\" + 0.012*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:46,595 : INFO : topic #2 (0.250): 0.044*\"工作\" + 0.010*\"面試\" + 0.010*\"方式\" + 0.009*\"小時\" + 0.008*\"內容\" + 0.008*\"時間\" + 0.008*\"公司\" + 0.006*\"單位\" + 0.006*\"推定\" + 0.006*\"應徵\"\n",
      "2025-04-19 16:03:46,595 : INFO : topic #3 (0.250): 0.014*\"公司\" + 0.007*\"工作\" + 0.006*\"問題\" + 0.005*\"面試\" + 0.005*\"時間\" + 0.005*\"工程師\" + 0.005*\"目前\" + 0.004*\"開發\" + 0.004*\"技術\" + 0.004*\"台灣\"\n",
      "2025-04-19 16:03:46,596 : INFO : topic diff=0.532430, rho=0.408248\n",
      "2025-04-19 16:03:46,596 : INFO : PROGRESS: pass 0, at document #14000/16310\n",
      "2025-04-19 16:03:46,835 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:46,838 : INFO : topic #0 (0.250): 0.027*\"工作\" + 0.011*\"應徵\" + 0.011*\"方式\" + 0.011*\"空白\" + 0.010*\"推定\" + 0.010*\"砍除\" + 0.009*\"單位\" + 0.009*\"資訊\" + 0.009*\"內容\" + 0.009*\"第一項\"\n",
      "2025-04-19 16:03:46,838 : INFO : topic #1 (0.250): 0.031*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:46,839 : INFO : topic #2 (0.250): 0.045*\"工作\" + 0.010*\"面試\" + 0.009*\"方式\" + 0.008*\"小時\" + 0.008*\"內容\" + 0.008*\"時間\" + 0.007*\"公司\" + 0.006*\"單位\" + 0.006*\"工時\" + 0.005*\"覺得\"\n",
      "2025-04-19 16:03:46,839 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"工作\" + 0.005*\"問題\" + 0.004*\"技術\" + 0.004*\"工程師\" + 0.004*\"目前\" + 0.004*\"時間\" + 0.004*\"面試\" + 0.003*\"員工\"\n",
      "2025-04-19 16:03:46,840 : INFO : topic diff=0.418933, rho=0.377964\n",
      "2025-04-19 16:03:46,840 : INFO : PROGRESS: pass 0, at document #16000/16310\n",
      "2025-04-19 16:03:47,067 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:47,069 : INFO : topic #0 (0.250): 0.025*\"工作\" + 0.010*\"方式\" + 0.010*\"應徵\" + 0.010*\"空白\" + 0.009*\"推定\" + 0.009*\"砍除\" + 0.009*\"單位\" + 0.009*\"資訊\" + 0.008*\"內容\" + 0.008*\"第一項\"\n",
      "2025-04-19 16:03:47,070 : INFO : topic #1 (0.250): 0.030*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:47,070 : INFO : topic #2 (0.250): 0.045*\"工作\" + 0.011*\"面試\" + 0.009*\"方式\" + 0.008*\"內容\" + 0.008*\"小時\" + 0.008*\"公司\" + 0.008*\"時間\" + 0.006*\"工時\" + 0.006*\"單位\" + 0.005*\"覺得\"\n",
      "2025-04-19 16:03:47,071 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.005*\"工作\" + 0.004*\"晶片\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"員工\" + 0.004*\"表示\" + 0.004*\"工程師\"\n",
      "2025-04-19 16:03:47,071 : INFO : topic diff=0.321705, rho=0.353553\n",
      "2025-04-19 16:03:47,143 : INFO : -8.523 per-word bound, 367.9 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 16:03:47,143 : INFO : PROGRESS: pass 0, at document #16310/16310\n",
      "2025-04-19 16:03:47,180 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:47,183 : INFO : topic #0 (0.250): 0.023*\"工作\" + 0.010*\"應徵\" + 0.010*\"方式\" + 0.009*\"空白\" + 0.009*\"單位\" + 0.008*\"推定\" + 0.008*\"砍除\" + 0.008*\"資訊\" + 0.008*\"內容\" + 0.008*\"分類\"\n",
      "2025-04-19 16:03:47,183 : INFO : topic #1 (0.250): 0.030*\"工作\" + 0.013*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.011*\"第一項\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.010*\"國定假日\" + 0.010*\"資訊\"\n",
      "2025-04-19 16:03:47,184 : INFO : topic #2 (0.250): 0.044*\"工作\" + 0.010*\"面試\" + 0.009*\"小時\" + 0.008*\"公司\" + 0.008*\"內容\" + 0.008*\"方式\" + 0.008*\"時間\" + 0.007*\"工時\" + 0.006*\"覺得\" + 0.006*\"單位\"\n",
      "2025-04-19 16:03:47,184 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.007*\"美國\" + 0.007*\"台灣\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"工作\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"台積電\"\n",
      "2025-04-19 16:03:47,185 : INFO : topic diff=0.324760, rho=0.333333\n",
      "2025-04-19 16:03:47,185 : INFO : PROGRESS: pass 1, at document #2000/16310\n",
      "2025-04-19 16:03:47,671 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:47,673 : INFO : topic #0 (0.250): 0.022*\"工作\" + 0.014*\"方式\" + 0.012*\"台北市\" + 0.011*\"內容\" + 0.011*\"聯絡\" + 0.009*\"應徵\" + 0.009*\"通知\" + 0.009*\"電話\" + 0.009*\"地點\" + 0.008*\"工資\"\n",
      "2025-04-19 16:03:47,674 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.011*\"情形\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"第一項\" + 0.011*\"單位\"\n",
      "2025-04-19 16:03:47,674 : INFO : topic #2 (0.250): 0.047*\"工作\" + 0.013*\"方式\" + 0.012*\"時間\" + 0.011*\"小時\" + 0.010*\"面試\" + 0.009*\"內容\" + 0.007*\"工時\" + 0.007*\"每日\" + 0.006*\"工資\" + 0.006*\"休息\"\n",
      "2025-04-19 16:03:47,675 : INFO : topic #3 (0.250): 0.011*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"工作\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"台積電\"\n",
      "2025-04-19 16:03:47,675 : INFO : topic diff=0.963431, rho=0.313805\n",
      "2025-04-19 16:03:47,675 : INFO : PROGRESS: pass 1, at document #4000/16310\n",
      "2025-04-19 16:03:48,160 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:48,163 : INFO : topic #0 (0.250): 0.019*\"工作\" + 0.015*\"方式\" + 0.015*\"電話\" + 0.014*\"台北市\" + 0.012*\"聯絡\" + 0.012*\"內容\" + 0.012*\"通知\" + 0.010*\"地點\" + 0.009*\"單位名稱\" + 0.008*\"單位地址\"\n",
      "2025-04-19 16:03:48,163 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:48,164 : INFO : topic #2 (0.250): 0.048*\"工作\" + 0.016*\"方式\" + 0.014*\"時間\" + 0.013*\"小時\" + 0.010*\"面試\" + 0.009*\"每日\" + 0.009*\"內容\" + 0.008*\"休息\" + 0.008*\"工資\" + 0.008*\"依法\"\n",
      "2025-04-19 16:03:48,165 : INFO : topic #3 (0.250): 0.011*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"工作\" + 0.004*\"員工\" + 0.004*\"表示\" + 0.003*\"科技\" + 0.003*\"時間\"\n",
      "2025-04-19 16:03:48,165 : INFO : topic diff=0.473894, rho=0.313805\n",
      "2025-04-19 16:03:48,165 : INFO : PROGRESS: pass 1, at document #6000/16310\n",
      "2025-04-19 16:03:48,557 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:48,560 : INFO : topic #0 (0.250): 0.019*\"電話\" + 0.017*\"台北市\" + 0.015*\"報名\" + 0.015*\"工作\" + 0.015*\"方式\" + 0.013*\"聯絡\" + 0.013*\"通知\" + 0.013*\"內容\" + 0.012*\"活動\" + 0.011*\"地點\"\n",
      "2025-04-19 16:03:48,560 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 16:03:48,561 : INFO : topic #2 (0.250): 0.049*\"工作\" + 0.019*\"方式\" + 0.015*\"時間\" + 0.014*\"小時\" + 0.011*\"每日\" + 0.010*\"內容\" + 0.010*\"面試\" + 0.009*\"依法\" + 0.009*\"休息\" + 0.009*\"工資\"\n",
      "2025-04-19 16:03:48,561 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.005*\"台灣\" + 0.005*\"美國\" + 0.004*\"技術\" + 0.004*\"資料\" + 0.004*\"工作\" + 0.004*\"目前\" + 0.004*\"問題\" + 0.004*\"產品\" + 0.004*\"時間\"\n",
      "2025-04-19 16:03:48,562 : INFO : topic diff=0.326303, rho=0.313805\n",
      "2025-04-19 16:03:48,562 : INFO : PROGRESS: pass 1, at document #8000/16310\n",
      "2025-04-19 16:03:48,846 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:48,848 : INFO : topic #0 (0.250): 0.019*\"電話\" + 0.017*\"台北市\" + 0.015*\"報名\" + 0.015*\"工作\" + 0.015*\"方式\" + 0.013*\"聯絡\" + 0.013*\"通知\" + 0.012*\"內容\" + 0.012*\"活動\" + 0.011*\"地點\"\n",
      "2025-04-19 16:03:48,849 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 16:03:48,849 : INFO : topic #2 (0.250): 0.054*\"工作\" + 0.018*\"方式\" + 0.016*\"時間\" + 0.014*\"小時\" + 0.013*\"面試\" + 0.010*\"內容\" + 0.009*\"每日\" + 0.008*\"經驗\" + 0.008*\"工時\" + 0.007*\"休息\"\n",
      "2025-04-19 16:03:48,850 : INFO : topic #3 (0.250): 0.015*\"公司\" + 0.006*\"問題\" + 0.005*\"工作\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.005*\"面試\" + 0.004*\"目前\" + 0.004*\"台灣\" + 0.004*\"開發\" + 0.004*\"產品\"\n",
      "2025-04-19 16:03:48,850 : INFO : topic diff=0.345988, rho=0.313805\n",
      "2025-04-19 16:03:48,851 : INFO : PROGRESS: pass 1, at document #10000/16310\n",
      "2025-04-19 16:03:49,099 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:49,101 : INFO : topic #0 (0.250): 0.018*\"電話\" + 0.016*\"台北市\" + 0.016*\"報名\" + 0.015*\"方式\" + 0.014*\"工作\" + 0.013*\"聯絡\" + 0.013*\"通知\" + 0.012*\"內容\" + 0.012*\"活動\" + 0.011*\"地點\"\n",
      "2025-04-19 16:03:49,101 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\" + 0.011*\"單位\"\n",
      "2025-04-19 16:03:49,102 : INFO : topic #2 (0.250): 0.055*\"工作\" + 0.017*\"方式\" + 0.017*\"時間\" + 0.014*\"小時\" + 0.013*\"面試\" + 0.011*\"內容\" + 0.009*\"經驗\" + 0.009*\"工時\" + 0.009*\"每日\" + 0.007*\"職缺\"\n",
      "2025-04-19 16:03:49,102 : INFO : topic #3 (0.250): 0.015*\"公司\" + 0.006*\"問題\" + 0.006*\"工作\" + 0.006*\"面試\" + 0.005*\"工程師\" + 0.005*\"開發\" + 0.005*\"目前\" + 0.005*\"技術\" + 0.004*\"比較\" + 0.004*\"台灣\"\n",
      "2025-04-19 16:03:49,103 : INFO : topic diff=0.277496, rho=0.313805\n",
      "2025-04-19 16:03:49,103 : INFO : PROGRESS: pass 1, at document #12000/16310\n",
      "2025-04-19 16:03:49,322 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:49,324 : INFO : topic #0 (0.250): 0.017*\"電話\" + 0.017*\"報名\" + 0.015*\"台北市\" + 0.015*\"方式\" + 0.014*\"工作\" + 0.014*\"活動\" + 0.013*\"聯絡\" + 0.012*\"通知\" + 0.012*\"內容\" + 0.011*\"地點\"\n",
      "2025-04-19 16:03:49,324 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:49,325 : INFO : topic #2 (0.250): 0.055*\"工作\" + 0.017*\"時間\" + 0.016*\"方式\" + 0.014*\"小時\" + 0.013*\"面試\" + 0.010*\"內容\" + 0.009*\"經驗\" + 0.009*\"工時\" + 0.008*\"每日\" + 0.007*\"職缺\"\n",
      "2025-04-19 16:03:49,325 : INFO : topic #3 (0.250): 0.014*\"公司\" + 0.006*\"工作\" + 0.006*\"問題\" + 0.005*\"面試\" + 0.005*\"工程師\" + 0.004*\"技術\" + 0.004*\"台灣\" + 0.004*\"目前\" + 0.004*\"開發\" + 0.004*\"比較\"\n",
      "2025-04-19 16:03:49,326 : INFO : topic diff=0.287867, rho=0.313805\n",
      "2025-04-19 16:03:49,326 : INFO : PROGRESS: pass 1, at document #14000/16310\n",
      "2025-04-19 16:03:49,527 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:49,529 : INFO : topic #0 (0.250): 0.017*\"報名\" + 0.017*\"電話\" + 0.015*\"台北市\" + 0.014*\"方式\" + 0.013*\"活動\" + 0.013*\"工作\" + 0.013*\"聯絡\" + 0.012*\"通知\" + 0.011*\"內容\" + 0.011*\"地點\"\n",
      "2025-04-19 16:03:49,529 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:49,530 : INFO : topic #2 (0.250): 0.055*\"工作\" + 0.016*\"時間\" + 0.015*\"方式\" + 0.013*\"小時\" + 0.013*\"面試\" + 0.010*\"內容\" + 0.010*\"工時\" + 0.009*\"經驗\" + 0.007*\"每日\" + 0.007*\"職缺\"\n",
      "2025-04-19 16:03:49,530 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.005*\"工作\" + 0.005*\"問題\" + 0.004*\"技術\" + 0.004*\"工程師\" + 0.004*\"面試\" + 0.004*\"目前\" + 0.003*\"美國\" + 0.003*\"員工\"\n",
      "2025-04-19 16:03:49,531 : INFO : topic diff=0.283267, rho=0.313805\n",
      "2025-04-19 16:03:49,531 : INFO : PROGRESS: pass 1, at document #16000/16310\n",
      "2025-04-19 16:03:49,720 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:49,722 : INFO : topic #0 (0.250): 0.019*\"報名\" + 0.015*\"電話\" + 0.014*\"台北市\" + 0.014*\"方式\" + 0.013*\"活動\" + 0.012*\"工作\" + 0.012*\"聯絡\" + 0.012*\"通知\" + 0.010*\"內容\" + 0.010*\"地點\"\n",
      "2025-04-19 16:03:49,723 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"單位\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:49,723 : INFO : topic #2 (0.250): 0.055*\"工作\" + 0.015*\"時間\" + 0.013*\"方式\" + 0.013*\"面試\" + 0.013*\"小時\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.009*\"經驗\" + 0.007*\"職缺\" + 0.007*\"地點\"\n",
      "2025-04-19 16:03:49,724 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.005*\"工作\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.004*\"表示\"\n",
      "2025-04-19 16:03:49,724 : INFO : topic diff=0.252586, rho=0.313805\n",
      "2025-04-19 16:03:49,790 : INFO : -8.446 per-word bound, 348.8 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 16:03:49,791 : INFO : PROGRESS: pass 1, at document #16310/16310\n",
      "2025-04-19 16:03:49,823 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:49,825 : INFO : topic #0 (0.250): 0.019*\"報名\" + 0.015*\"問卷\" + 0.014*\"台北市\" + 0.013*\"電話\" + 0.013*\"工作\" + 0.013*\"活動\" + 0.012*\"方式\" + 0.011*\"通知\" + 0.011*\"聯絡\" + 0.010*\"地點\"\n",
      "2025-04-19 16:03:49,825 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:49,826 : INFO : topic #2 (0.250): 0.053*\"工作\" + 0.015*\"時間\" + 0.014*\"小時\" + 0.012*\"方式\" + 0.012*\"面試\" + 0.011*\"工時\" + 0.010*\"內容\" + 0.009*\"經驗\" + 0.007*\"公司\" + 0.006*\"薪資\"\n",
      "2025-04-19 16:03:49,826 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"美國\" + 0.006*\"台灣\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"工作\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"台積電\"\n",
      "2025-04-19 16:03:49,826 : INFO : topic diff=0.279421, rho=0.313805\n",
      "2025-04-19 16:03:49,827 : INFO : PROGRESS: pass 2, at document #2000/16310\n",
      "2025-04-19 16:03:50,204 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:50,206 : INFO : topic #0 (0.250): 0.021*\"報名\" + 0.019*\"電話\" + 0.018*\"活動\" + 0.017*\"台北市\" + 0.014*\"通知\" + 0.013*\"方式\" + 0.012*\"人數\" + 0.011*\"聯絡\" + 0.011*\"時間\" + 0.011*\"地點\"\n",
      "2025-04-19 16:03:50,207 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:50,207 : INFO : topic #2 (0.250): 0.054*\"工作\" + 0.017*\"時間\" + 0.016*\"方式\" + 0.015*\"小時\" + 0.012*\"面試\" + 0.011*\"工時\" + 0.011*\"內容\" + 0.009*\"每日\" + 0.008*\"休息\" + 0.007*\"經驗\"\n",
      "2025-04-19 16:03:50,208 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"工作\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 16:03:50,208 : INFO : topic diff=0.815427, rho=0.299409\n",
      "2025-04-19 16:03:50,208 : INFO : PROGRESS: pass 2, at document #4000/16310\n",
      "2025-04-19 16:03:50,570 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:50,572 : INFO : topic #0 (0.250): 0.021*\"報名\" + 0.020*\"電話\" + 0.019*\"活動\" + 0.017*\"台北市\" + 0.014*\"通知\" + 0.012*\"人數\" + 0.012*\"方式\" + 0.011*\"時間\" + 0.011*\"聯絡\" + 0.011*\"地點\"\n",
      "2025-04-19 16:03:50,573 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:50,573 : INFO : topic #2 (0.250): 0.053*\"工作\" + 0.019*\"方式\" + 0.017*\"時間\" + 0.016*\"小時\" + 0.012*\"面試\" + 0.011*\"每日\" + 0.011*\"內容\" + 0.010*\"工時\" + 0.009*\"休息\" + 0.008*\"依法\"\n",
      "2025-04-19 16:03:50,574 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"工作\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 16:03:50,574 : INFO : topic diff=0.366523, rho=0.299409\n",
      "2025-04-19 16:03:50,575 : INFO : PROGRESS: pass 2, at document #6000/16310\n",
      "2025-04-19 16:03:50,914 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:50,916 : INFO : topic #0 (0.250): 0.024*\"報名\" + 0.021*\"活動\" + 0.021*\"電話\" + 0.017*\"台北市\" + 0.013*\"通知\" + 0.013*\"人數\" + 0.011*\"聯絡\" + 0.011*\"時間\" + 0.011*\"車馬費\" + 0.010*\"方式\"\n",
      "2025-04-19 16:03:50,917 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:50,917 : INFO : topic #2 (0.250): 0.052*\"工作\" + 0.021*\"方式\" + 0.017*\"時間\" + 0.015*\"小時\" + 0.012*\"每日\" + 0.011*\"內容\" + 0.011*\"面試\" + 0.010*\"休息\" + 0.009*\"依法\" + 0.009*\"工時\"\n",
      "2025-04-19 16:03:50,918 : INFO : topic #3 (0.250): 0.013*\"公司\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.005*\"技術\" + 0.004*\"工作\" + 0.004*\"問題\" + 0.004*\"晶片\" + 0.004*\"工程師\" + 0.004*\"員工\" + 0.003*\"目前\"\n",
      "2025-04-19 16:03:50,918 : INFO : topic diff=0.279284, rho=0.299409\n",
      "2025-04-19 16:03:50,918 : INFO : PROGRESS: pass 2, at document #8000/16310\n",
      "2025-04-19 16:03:51,174 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:51,176 : INFO : topic #0 (0.250): 0.024*\"報名\" + 0.021*\"活動\" + 0.021*\"電話\" + 0.017*\"台北市\" + 0.013*\"通知\" + 0.012*\"人數\" + 0.011*\"時間\" + 0.011*\"聯絡\" + 0.010*\"方式\" + 0.010*\"車馬費\"\n",
      "2025-04-19 16:03:51,177 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:51,177 : INFO : topic #2 (0.250): 0.054*\"工作\" + 0.019*\"方式\" + 0.018*\"時間\" + 0.015*\"小時\" + 0.013*\"面試\" + 0.011*\"內容\" + 0.010*\"每日\" + 0.009*\"經驗\" + 0.009*\"工時\" + 0.008*\"休息\"\n",
      "2025-04-19 16:03:51,178 : INFO : topic #3 (0.250): 0.016*\"公司\" + 0.006*\"問題\" + 0.006*\"工作\" + 0.006*\"工程師\" + 0.005*\"技術\" + 0.005*\"面試\" + 0.005*\"台灣\" + 0.004*\"開發\" + 0.004*\"目前\" + 0.004*\"產品\"\n",
      "2025-04-19 16:03:51,178 : INFO : topic diff=0.315344, rho=0.299409\n",
      "2025-04-19 16:03:51,178 : INFO : PROGRESS: pass 2, at document #10000/16310\n",
      "2025-04-19 16:03:51,404 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:51,406 : INFO : topic #0 (0.250): 0.025*\"報名\" + 0.022*\"活動\" + 0.020*\"電話\" + 0.016*\"台北市\" + 0.013*\"通知\" + 0.012*\"人數\" + 0.011*\"時間\" + 0.011*\"聯絡\" + 0.010*\"方式\" + 0.010*\"內容\"\n",
      "2025-04-19 16:03:51,407 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:51,407 : INFO : topic #2 (0.250): 0.053*\"工作\" + 0.018*\"時間\" + 0.018*\"方式\" + 0.015*\"小時\" + 0.012*\"面試\" + 0.011*\"內容\" + 0.010*\"經驗\" + 0.009*\"工時\" + 0.009*\"每日\" + 0.007*\"職缺\"\n",
      "2025-04-19 16:03:51,408 : INFO : topic #3 (0.250): 0.016*\"公司\" + 0.007*\"工作\" + 0.007*\"問題\" + 0.006*\"面試\" + 0.006*\"工程師\" + 0.005*\"技術\" + 0.005*\"開發\" + 0.005*\"目前\" + 0.004*\"比較\" + 0.004*\"覺得\"\n",
      "2025-04-19 16:03:51,408 : INFO : topic diff=0.258780, rho=0.299409\n",
      "2025-04-19 16:03:51,408 : INFO : PROGRESS: pass 2, at document #12000/16310\n",
      "2025-04-19 16:03:51,620 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:51,622 : INFO : topic #0 (0.250): 0.026*\"報名\" + 0.024*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.012*\"通知\" + 0.012*\"人數\" + 0.011*\"時間\" + 0.011*\"聯絡\" + 0.010*\"方式\" + 0.010*\"舉辦\"\n",
      "2025-04-19 16:03:51,622 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:51,623 : INFO : topic #2 (0.250): 0.052*\"工作\" + 0.018*\"時間\" + 0.017*\"方式\" + 0.014*\"小時\" + 0.012*\"面試\" + 0.011*\"內容\" + 0.010*\"經驗\" + 0.010*\"工時\" + 0.008*\"每日\" + 0.008*\"職缺\"\n",
      "2025-04-19 16:03:51,623 : INFO : topic #3 (0.250): 0.014*\"公司\" + 0.006*\"工作\" + 0.006*\"問題\" + 0.005*\"面試\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.005*\"台灣\" + 0.004*\"開發\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 16:03:51,624 : INFO : topic diff=0.269441, rho=0.299409\n",
      "2025-04-19 16:03:51,624 : INFO : PROGRESS: pass 2, at document #14000/16310\n",
      "2025-04-19 16:03:51,857 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:51,859 : INFO : topic #0 (0.250): 0.025*\"報名\" + 0.023*\"活動\" + 0.018*\"電話\" + 0.014*\"台北市\" + 0.012*\"通知\" + 0.012*\"人數\" + 0.011*\"問卷\" + 0.011*\"研究\" + 0.011*\"時間\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:51,860 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:51,861 : INFO : topic #2 (0.250): 0.053*\"工作\" + 0.017*\"時間\" + 0.016*\"方式\" + 0.014*\"小時\" + 0.011*\"面試\" + 0.011*\"內容\" + 0.010*\"經驗\" + 0.010*\"工時\" + 0.008*\"職缺\" + 0.007*\"每日\"\n",
      "2025-04-19 16:03:51,861 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"工作\" + 0.005*\"問題\" + 0.004*\"技術\" + 0.004*\"工程師\" + 0.004*\"面試\" + 0.004*\"目前\" + 0.004*\"美國\" + 0.003*\"開發\"\n",
      "2025-04-19 16:03:51,862 : INFO : topic diff=0.269494, rho=0.299409\n",
      "2025-04-19 16:03:51,862 : INFO : PROGRESS: pass 2, at document #16000/16310\n",
      "2025-04-19 16:03:52,050 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:52,052 : INFO : topic #0 (0.250): 0.026*\"報名\" + 0.023*\"活動\" + 0.016*\"電話\" + 0.013*\"研究\" + 0.013*\"台北市\" + 0.012*\"問卷\" + 0.012*\"通知\" + 0.011*\"人數\" + 0.010*\"時間\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:52,053 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:52,053 : INFO : topic #2 (0.250): 0.052*\"工作\" + 0.017*\"時間\" + 0.014*\"方式\" + 0.014*\"小時\" + 0.012*\"面試\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.010*\"經驗\" + 0.008*\"薪資\" + 0.007*\"職缺\"\n",
      "2025-04-19 16:03:52,054 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.005*\"工作\" + 0.004*\"技術\" + 0.004*\"晶片\" + 0.004*\"問題\" + 0.004*\"工程師\" + 0.004*\"表示\" + 0.004*\"員工\"\n",
      "2025-04-19 16:03:52,054 : INFO : topic diff=0.240447, rho=0.299409\n",
      "2025-04-19 16:03:52,119 : INFO : -8.429 per-word bound, 344.8 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 16:03:52,120 : INFO : PROGRESS: pass 2, at document #16310/16310\n",
      "2025-04-19 16:03:52,151 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:52,153 : INFO : topic #0 (0.250): 0.024*\"報名\" + 0.023*\"活動\" + 0.017*\"問卷\" + 0.017*\"研究\" + 0.014*\"電話\" + 0.013*\"台北市\" + 0.011*\"時間\" + 0.011*\"人數\" + 0.010*\"通知\" + 0.009*\"舉辦\"\n",
      "2025-04-19 16:03:52,154 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 16:03:52,155 : INFO : topic #2 (0.250): 0.052*\"工作\" + 0.016*\"時間\" + 0.015*\"小時\" + 0.013*\"方式\" + 0.012*\"工時\" + 0.011*\"面試\" + 0.011*\"內容\" + 0.010*\"經驗\" + 0.008*\"薪資\" + 0.007*\"地點\"\n",
      "2025-04-19 16:03:52,155 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"美國\" + 0.006*\"台灣\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"工作\" + 0.004*\"員工\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 16:03:52,155 : INFO : topic diff=0.261425, rho=0.299409\n",
      "2025-04-19 16:03:52,156 : INFO : PROGRESS: pass 3, at document #2000/16310\n",
      "2025-04-19 16:03:52,500 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:52,502 : INFO : topic #0 (0.250): 0.025*\"報名\" + 0.023*\"活動\" + 0.019*\"電話\" + 0.016*\"台北市\" + 0.012*\"通知\" + 0.012*\"人數\" + 0.012*\"時間\" + 0.011*\"舉辦\" + 0.010*\"車馬費\" + 0.010*\"聯絡\"\n",
      "2025-04-19 16:03:52,502 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:52,503 : INFO : topic #2 (0.250): 0.052*\"工作\" + 0.018*\"方式\" + 0.018*\"時間\" + 0.015*\"小時\" + 0.011*\"面試\" + 0.011*\"內容\" + 0.011*\"工時\" + 0.009*\"每日\" + 0.008*\"休息\" + 0.008*\"地點\"\n",
      "2025-04-19 16:03:52,503 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"工作\" + 0.004*\"科技\" + 0.004*\"員工\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 16:03:52,504 : INFO : topic diff=0.697368, rho=0.286829\n",
      "2025-04-19 16:03:52,504 : INFO : PROGRESS: pass 3, at document #4000/16310\n",
      "2025-04-19 16:03:52,839 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:52,841 : INFO : topic #0 (0.250): 0.025*\"報名\" + 0.023*\"活動\" + 0.020*\"電話\" + 0.016*\"台北市\" + 0.013*\"人數\" + 0.012*\"通知\" + 0.011*\"時間\" + 0.011*\"舉辦\" + 0.011*\"車馬費\" + 0.010*\"資料\"\n",
      "2025-04-19 16:03:52,841 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:52,842 : INFO : topic #2 (0.250): 0.051*\"工作\" + 0.020*\"方式\" + 0.018*\"時間\" + 0.016*\"小時\" + 0.011*\"面試\" + 0.011*\"每日\" + 0.011*\"內容\" + 0.010*\"工時\" + 0.009*\"休息\" + 0.008*\"地點\"\n",
      "2025-04-19 16:03:52,842 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"晶片\" + 0.004*\"工作\" + 0.004*\"科技\" + 0.004*\"員工\" + 0.004*\"表示\" + 0.004*\"問題\"\n",
      "2025-04-19 16:03:52,843 : INFO : topic diff=0.335287, rho=0.286829\n",
      "2025-04-19 16:03:52,844 : INFO : PROGRESS: pass 3, at document #6000/16310\n",
      "2025-04-19 16:03:53,158 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:53,160 : INFO : topic #0 (0.250): 0.027*\"報名\" + 0.025*\"活動\" + 0.021*\"電話\" + 0.017*\"台北市\" + 0.013*\"人數\" + 0.012*\"通知\" + 0.012*\"車馬費\" + 0.011*\"舉辦\" + 0.011*\"時間\" + 0.011*\"資料\"\n",
      "2025-04-19 16:03:53,160 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"第一項\" + 0.011*\"單位\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:53,161 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.021*\"方式\" + 0.017*\"時間\" + 0.015*\"小時\" + 0.012*\"每日\" + 0.011*\"內容\" + 0.010*\"面試\" + 0.010*\"休息\" + 0.009*\"依法\" + 0.009*\"工時\"\n",
      "2025-04-19 16:03:53,161 : INFO : topic #3 (0.250): 0.013*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.004*\"工作\" + 0.004*\"問題\" + 0.004*\"晶片\" + 0.004*\"工程師\" + 0.004*\"科技\" + 0.004*\"員工\"\n",
      "2025-04-19 16:03:53,162 : INFO : topic diff=0.259937, rho=0.286829\n",
      "2025-04-19 16:03:53,162 : INFO : PROGRESS: pass 3, at document #8000/16310\n",
      "2025-04-19 16:03:53,408 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:53,410 : INFO : topic #0 (0.250): 0.027*\"報名\" + 0.025*\"活動\" + 0.020*\"電話\" + 0.016*\"台北市\" + 0.012*\"人數\" + 0.012*\"通知\" + 0.011*\"舉辦\" + 0.011*\"車馬費\" + 0.011*\"時間\" + 0.011*\"資料\"\n",
      "2025-04-19 16:03:53,411 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:53,411 : INFO : topic #2 (0.250): 0.051*\"工作\" + 0.019*\"方式\" + 0.018*\"時間\" + 0.015*\"小時\" + 0.012*\"面試\" + 0.011*\"內容\" + 0.010*\"每日\" + 0.009*\"經驗\" + 0.009*\"工時\" + 0.008*\"休息\"\n",
      "2025-04-19 16:03:53,412 : INFO : topic #3 (0.250): 0.015*\"公司\" + 0.006*\"工作\" + 0.006*\"問題\" + 0.006*\"工程師\" + 0.005*\"面試\" + 0.005*\"技術\" + 0.005*\"台灣\" + 0.004*\"開發\" + 0.004*\"目前\" + 0.004*\"覺得\"\n",
      "2025-04-19 16:03:53,412 : INFO : topic diff=0.295338, rho=0.286829\n",
      "2025-04-19 16:03:53,412 : INFO : PROGRESS: pass 3, at document #10000/16310\n",
      "2025-04-19 16:03:53,631 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:53,633 : INFO : topic #0 (0.250): 0.028*\"報名\" + 0.025*\"活動\" + 0.019*\"電話\" + 0.015*\"台北市\" + 0.012*\"人數\" + 0.012*\"通知\" + 0.011*\"時間\" + 0.011*\"舉辦\" + 0.011*\"資料\" + 0.011*\"車馬費\"\n",
      "2025-04-19 16:03:53,633 : INFO : topic #1 (0.250): 0.033*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:53,634 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.018*\"時間\" + 0.018*\"方式\" + 0.014*\"小時\" + 0.011*\"面試\" + 0.011*\"內容\" + 0.010*\"經驗\" + 0.009*\"工時\" + 0.009*\"每日\" + 0.007*\"職缺\"\n",
      "2025-04-19 16:03:53,634 : INFO : topic #3 (0.250): 0.016*\"公司\" + 0.007*\"工作\" + 0.007*\"問題\" + 0.006*\"面試\" + 0.006*\"工程師\" + 0.005*\"技術\" + 0.005*\"開發\" + 0.005*\"目前\" + 0.004*\"比較\" + 0.004*\"覺得\"\n",
      "2025-04-19 16:03:53,635 : INFO : topic diff=0.243741, rho=0.286829\n",
      "2025-04-19 16:03:53,635 : INFO : PROGRESS: pass 3, at document #12000/16310\n",
      "2025-04-19 16:03:53,851 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:53,853 : INFO : topic #0 (0.250): 0.028*\"報名\" + 0.027*\"活動\" + 0.018*\"電話\" + 0.014*\"台北市\" + 0.012*\"研究\" + 0.012*\"人數\" + 0.011*\"舉辦\" + 0.011*\"通知\" + 0.011*\"時間\" + 0.011*\"資料\"\n",
      "2025-04-19 16:03:53,854 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.015*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:53,854 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.018*\"時間\" + 0.017*\"方式\" + 0.014*\"小時\" + 0.011*\"內容\" + 0.011*\"面試\" + 0.010*\"經驗\" + 0.009*\"工時\" + 0.008*\"每日\" + 0.008*\"職缺\"\n",
      "2025-04-19 16:03:53,855 : INFO : topic #3 (0.250): 0.014*\"公司\" + 0.006*\"工作\" + 0.006*\"問題\" + 0.006*\"面試\" + 0.005*\"工程師\" + 0.005*\"技術\" + 0.005*\"台灣\" + 0.004*\"開發\" + 0.004*\"目前\" + 0.004*\"比較\"\n",
      "2025-04-19 16:03:53,855 : INFO : topic diff=0.254071, rho=0.286829\n",
      "2025-04-19 16:03:53,855 : INFO : PROGRESS: pass 3, at document #14000/16310\n",
      "2025-04-19 16:03:54,054 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:54,056 : INFO : topic #0 (0.250): 0.027*\"報名\" + 0.026*\"活動\" + 0.017*\"電話\" + 0.014*\"研究\" + 0.013*\"台北市\" + 0.012*\"問卷\" + 0.011*\"人數\" + 0.011*\"舉辦\" + 0.011*\"通知\" + 0.010*\"時間\"\n",
      "2025-04-19 16:03:54,057 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:54,057 : INFO : topic #2 (0.250): 0.050*\"工作\" + 0.017*\"時間\" + 0.016*\"方式\" + 0.014*\"小時\" + 0.011*\"內容\" + 0.011*\"面試\" + 0.010*\"經驗\" + 0.010*\"工時\" + 0.008*\"薪資\" + 0.008*\"職缺\"\n",
      "2025-04-19 16:03:54,058 : INFO : topic #3 (0.250): 0.013*\"公司\" + 0.006*\"台灣\" + 0.006*\"工作\" + 0.005*\"問題\" + 0.004*\"技術\" + 0.004*\"工程師\" + 0.004*\"面試\" + 0.004*\"目前\" + 0.004*\"美國\" + 0.003*\"開發\"\n",
      "2025-04-19 16:03:54,058 : INFO : topic diff=0.255783, rho=0.286829\n",
      "2025-04-19 16:03:54,058 : INFO : PROGRESS: pass 3, at document #16000/16310\n",
      "2025-04-19 16:03:54,247 : INFO : merging changes from 2000 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:54,249 : INFO : topic #0 (0.250): 0.027*\"報名\" + 0.026*\"活動\" + 0.016*\"研究\" + 0.015*\"電話\" + 0.013*\"問卷\" + 0.012*\"台北市\" + 0.011*\"人數\" + 0.011*\"舉辦\" + 0.010*\"通知\" + 0.010*\"參加\"\n",
      "2025-04-19 16:03:54,249 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"聯絡\" + 0.011*\"內容\"\n",
      "2025-04-19 16:03:54,250 : INFO : topic #2 (0.250): 0.049*\"工作\" + 0.017*\"時間\" + 0.015*\"方式\" + 0.013*\"小時\" + 0.011*\"內容\" + 0.011*\"面試\" + 0.010*\"工時\" + 0.010*\"經驗\" + 0.008*\"薪資\" + 0.007*\"職缺\"\n",
      "2025-04-19 16:03:54,250 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.005*\"美國\" + 0.005*\"工作\" + 0.004*\"技術\" + 0.004*\"問題\" + 0.004*\"晶片\" + 0.004*\"工程師\" + 0.004*\"表示\" + 0.004*\"員工\"\n",
      "2025-04-19 16:03:54,251 : INFO : topic diff=0.228391, rho=0.286829\n",
      "2025-04-19 16:03:54,317 : INFO : -8.423 per-word bound, 343.3 perplexity estimate based on a held-out corpus of 310 documents with 43091 words\n",
      "2025-04-19 16:03:54,317 : INFO : PROGRESS: pass 3, at document #16310/16310\n",
      "2025-04-19 16:03:54,349 : INFO : merging changes from 310 documents into a model of 16310 documents\n",
      "2025-04-19 16:03:54,351 : INFO : topic #0 (0.250): 0.025*\"活動\" + 0.025*\"報名\" + 0.018*\"研究\" + 0.017*\"問卷\" + 0.013*\"電話\" + 0.012*\"台北市\" + 0.011*\"人數\" + 0.011*\"時間\" + 0.010*\"舉辦\" + 0.010*\"參與\"\n",
      "2025-04-19 16:03:54,352 : INFO : topic #1 (0.250): 0.032*\"工作\" + 0.014*\"推定\" + 0.013*\"方式\" + 0.012*\"砍除\" + 0.012*\"空白\" + 0.012*\"情形\" + 0.011*\"單位\" + 0.011*\"第一項\" + 0.011*\"內容\" + 0.011*\"聯絡\"\n",
      "2025-04-19 16:03:54,352 : INFO : topic #2 (0.250): 0.049*\"工作\" + 0.016*\"時間\" + 0.015*\"小時\" + 0.013*\"方式\" + 0.011*\"工時\" + 0.011*\"內容\" + 0.010*\"面試\" + 0.010*\"經驗\" + 0.009*\"薪資\" + 0.007*\"地點\"\n",
      "2025-04-19 16:03:54,353 : INFO : topic #3 (0.250): 0.012*\"公司\" + 0.006*\"台灣\" + 0.006*\"美國\" + 0.005*\"技術\" + 0.005*\"晶片\" + 0.004*\"工作\" + 0.004*\"科技\" + 0.004*\"表示\" + 0.004*\"員工\" + 0.004*\"問題\"\n",
      "2025-04-19 16:03:54,353 : INFO : topic diff=0.247362, rho=0.286829\n",
      "2025-04-19 16:03:54,353 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel<num_terms=23261, num_topics=4, decay=0.5, chunksize=2000> in 10.29s', 'datetime': '2025-04-19T16:03:54.353935', 'gensim': '4.3.3', 'python': '3.11.2 (main, Apr 21 2023, 22:51:21) [Clang 14.0.3 (clang-1403.0.22.14.1)]', 'platform': 'macOS-15.3.2-arm64-arm-64bit', 'event': 'created'}\n"
     ]
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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el11410142852905446863740741\", ldavis_el11410142852905446863740741_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el11410142852905446863740741\", ldavis_el11410142852905446863740741_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el11410142852905446863740741\", ldavis_el11410142852905446863740741_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "1      0.298481 -0.082304       1        1  47.337103\n",
       "3     -0.271188 -0.126857       2        1  37.724702\n",
       "2      0.007553 -0.060090       3        1  10.235505\n",
       "0     -0.034846  0.269251       4        1   4.702689, topic_info=    Term          Freq         Total Category  logprob  loglift\n",
       "34    工作  75783.000000  75783.000000  Default  30.0000  30.0000\n",
       "50    方式  27286.000000  27286.000000  Default  29.0000  29.0000\n",
       "54    時間  17622.000000  17622.000000  Default  28.0000  28.0000\n",
       "33    小時  17064.000000  17064.000000  Default  27.0000  27.0000\n",
       "12    內容  23471.000000  23471.000000  Default  26.0000  26.0000\n",
       "..   ...           ...           ...      ...      ...      ...\n",
       "73    聯絡   1214.473612  20599.332482   Topic4  -4.8977   0.2261\n",
       "12    內容   1123.610761  23471.035546   Topic4  -4.9755   0.0178\n",
       "157   條件    985.592330   9250.548176   Topic4  -5.1066   0.8178\n",
       "50    方式   1096.363666  27286.554240   Topic4  -5.0000  -0.1574\n",
       "108   分鐘    855.902038   5145.829581   Topic4  -5.2476   1.2632\n",
       "\n",
       "[312 rows x 6 columns], token_table=      Topic      Freq  Term\n",
       "term                       \n",
       "210       1  0.984286    一律\n",
       "210       2  0.000088    一律\n",
       "210       3  0.014897    一律\n",
       "210       4  0.000789    一律\n",
       "213       1  0.995324   一次性\n",
       "...     ...       ...   ...\n",
       "207       3  0.003776   額滿將\n",
       "207       4  0.993006   額滿將\n",
       "5417      1  0.009930  高速公路\n",
       "5417      3  0.983038  高速公路\n",
       "5417      4  0.009930  高速公路\n",
       "\n",
       "[977 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 4, 3, 1])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_5 = LdaModel(\n",
    "    corpus = corpus,\n",
    "    num_topics = 4,\n",
    "    id2word=dictionary,\n",
    "    random_state = 1500,\n",
    "    passes = 4 # 訓練次數\n",
    "    )\n",
    "pyLDAvis.enable_notebook()\n",
    "p = pyLDAvis.gensim_models.prepare(model_5, corpus, dictionary)\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在嘗試後不同主題數量後，發現4個主題最為合適。\n",
    "\n",
    "- 第一個主題主要與工作應徵和聯絡方式相關\n",
    "- 第二個主題主要與半導體的產業發展相關\n",
    "- 第三個主題主要與勞權相關\n",
    "- 第四個主體與問卷研究、座談會相關"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 0.97264475), (2, 0.02634671)]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 取得每條新聞的主題分佈\n",
    "topics_doc = model_5.get_document_topics(corpus)\n",
    "topics_doc[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.33038548, 0.66557795, 0.        ],\n",
       "       [0.9956888 , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.99831349, 0.        , 0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , 0.99198657],\n",
       "       [0.        , 0.        , 0.        , 0.9972536 ],\n",
       "       [0.        , 0.        , 0.17628665, 0.82183146]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 把 gensim 的稀疏表示法轉成稀疏矩陣\n",
    "m_theta = corpus2csc(topics_doc).T.toarray() # 倒置讓shape變為(num_docs, num_topics)\n",
    "m_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 將主題的機率分布轉換成主題標籤\n",
    "data['topic_label'] = m_theta.argmax(axis=1) + 1\n",
    "\n",
    "# 儲存分類結果\n",
    "data.to_csv('./raw_data/topicData.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 固定各topic_label 所代表的顏色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tz/hplj27qd26n9qxr1cd83m32c0000gn/T/ipykernel_12814/893968679.py:5: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap = plt.cm.get_cmap('tab20', len(all_topics))\n"
     ]
    }
   ],
   "source": [
    "# 取得所有唯一的 topic_label，並排序保證一致性\n",
    "all_topics = sorted(data['topic_label'].unique())\n",
    "\n",
    "# 使用較深的 colormap，例如 'tab20' \n",
    "cmap = plt.cm.get_cmap('tab20', len(all_topics))\n",
    "colors = {topic: cmap(i) for i, topic in enumerate(all_topics)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 統計個主題數量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv(\"./raw_data/topicData.csv\")\n",
    "# 日期轉換與欄位整理\n",
    "data['artDate'] = pd.to_datetime(data['artDate'])\n",
    "value_counts = data[data['artDate'].dt.year >= 2024]['topic_label'].value_counts()\n",
    "# 畫出圓餅圖\n",
    "plt.figure(figsize=(4, 4))\n",
    "plt.pie(value_counts, labels=value_counts.index, autopct='%1.1f%%', startangle=140, colors=[colors[col] for col in value_counts.index])\n",
    "plt.title('Distribution of Topic Labels')\n",
    "plt.axis('equal')  # 讓圓餅圖是圓形的\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2024 - 2025 的趨勢"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2024 - 2025 topic trend')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date_topic = data[data['artDate'].dt.year >= 2024].groupby(data['artDate'].dt.date)['topic_label'].value_counts().unstack()\n",
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "date_topic.plot.line(ax=ax, stacked=True, color=[colors[col] for col in date_topic.columns])\n",
    "ax.legend(loc='lower right')\n",
    "ax.set_title(\"2024 - 2025 topic trend\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.\t主題 4 主導整體討論量，代表問卷與活動徵才最頻繁出現。\n",
    "2.\t主題 2 & 3 呈現正相關波動，反映出產業發展與勞權待遇常共同被提及。\n",
    "3.\t主題 1 極低，顯示明確求職/聯絡訊息少見，可能散見於其他渠道。\n",
    "4.\t2025 年初多主題出現缺口，可能與過年的長假相關"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 各月主題分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x3597e55d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "month_topic =  data[data['artDate'].dt.year >= 2024].groupby(data['artDate'].dt.month)['topic_label'].value_counts(normalize=True).unstack()\n",
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "month_topic.plot.bar(ax=ax, stacked=True, color=[colors[col] for col in month_topic.columns])\n",
    "ax.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.\t主題4（淺藍）：\n",
    "\t- 幾乎每個月份都佔了最大比例，尤其在第12月更明顯，顯示問卷研究或座談會招募的訊息非常頻繁且穩定。\n",
    "2.\t主題2（紅）：\n",
    "\t- 佔比也相當高，特別是在第4、8、9月等月份，這些月份半導體相關的話題討論明顯增多。\n",
    "\t- 第12月大幅下降，可能表示年底與產業發展相關的討論減少。\n",
    "3.\t主題3（粉紅）：\n",
    "\t- 雖然佔比較小，但在各月份相對穩定，顯示勞權和待遇的議題有一定關注度。\n",
    "\t- 第5和第9月稍微上升，可能與某些事件或新聞相關。\n",
    "4.\t主題1（深藍）：\n",
    "\t- 全年佔比較小，但在第1、3、11月略微上升，可能與年初/年末轉職季節相關，工作機會和聯絡方式的訊息較多。\n",
    "- 第一個主題主要與工作應徵的資訊相關\n",
    "- 第二個主題主要與半導體的產業發展相關\n",
    "- 第三個主題主要與勞權、待遇相關\n",
    "- 第四個主題與問卷研究、座談會招募相關"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同類別版被標記的比例分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x2200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 畫三張圖（固定顏色）\n",
    "fig, ax = plt.subplots(3, 1, figsize=(15, 22))\n",
    "\n",
    "for i, cat in enumerate(data['artCatagory'].unique()):\n",
    "    date_topic = (\n",
    "        data[(data['artCatagory'] == cat) & (data['artDate'].dt.year >= 2024)]\n",
    "        .groupby(data['artDate'].dt.date)['topic_label']\n",
    "        .value_counts()\n",
    "        .unstack()\n",
    "        .reindex(columns=all_topics, fill_value=0)  # 確保所有主題都出現\n",
    "    )\n",
    "    \n",
    "    # 繪圖（固定顏色順序）\n",
    "    date_topic.plot.line(\n",
    "        ax=ax[i],\n",
    "        stacked=True,\n",
    "        color=[colors[col] for col in date_topic.columns]\n",
    "    )\n",
    "    ax[i].set_title(f'{cat}', fontsize=16)\n",
    "    ax[i].legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. GuidedLDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import guidedlda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el11410142719433283079388391\" style=\"background-color:white;\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el11410142719433283079388391_data = {\"mdsDat\": {\"x\": [0.29848124845265434, -0.27118825043372724, 0.007552607059745372, -0.03484560507867222], \"y\": [-0.08230372327983378, -0.12685710493951674, -0.06008986040240897, 0.26925068862175955], \"topics\": [1, 2, 3, 4], \"cluster\": [1, 1, 1, 1], \"Freq\": [47.33710347149185, 37.72470177251394, 10.23550532476676, 4.702689431227458]}, \"tinfo\": {\"Term\": [\"\\u5de5\\u4f5c\", \"\\u65b9\\u5f0f\", \"\\u6642\\u9593\", \"\\u5c0f\\u6642\", \"\\u5167\\u5bb9\", \"\\u806f\\u7d61\", \"\\u63a8\\u5b9a\", \"\\u780d\\u9664\", \"\\u7a7a\\u767d\", \"\\u5de5\\u6642\", \"\\u6d3b\\u52d5\", \"\\u7b2c\\u4e00\\u9805\", \"\\u60c5\\u5f62\", \"\\u5831\\u540d\", \"\\u5730\\u9ede\", \"\\u7814\\u7a76\", \"\\u4eba\\u6578\", \"\\u901a\\u77e5\", \"\\u61c9\\u5fb5\", \"\\u9762\\u8a66\", \"\\u96fb\\u8a71\", \"\\u806f\\u7d61\\u4eba\", \"\\u6587\\u5b57\", \"\\u8cc7\\u8a0a\", \"\\u627f\\u652c\", \"\\u9023\\u7d50\", \"\\u689d\\u4ef6\", 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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el11410142719433283079388391\", ldavis_el11410142719433283079388391_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el11410142719433283079388391\", ldavis_el11410142719433283079388391_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el11410142719433283079388391\", ldavis_el11410142719433283079388391_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "1      0.298481 -0.082304       1        1  47.337103\n",
       "3     -0.271188 -0.126857       2        1  37.724702\n",
       "2      0.007553 -0.060090       3        1  10.235505\n",
       "0     -0.034846  0.269251       4        1   4.702689, topic_info=    Term          Freq         Total Category  logprob  loglift\n",
       "34    工作  75783.000000  75783.000000  Default  30.0000  30.0000\n",
       "50    方式  27286.000000  27286.000000  Default  29.0000  29.0000\n",
       "54    時間  17622.000000  17622.000000  Default  28.0000  28.0000\n",
       "33    小時  17064.000000  17064.000000  Default  27.0000  27.0000\n",
       "12    內容  23471.000000  23471.000000  Default  26.0000  26.0000\n",
       "..   ...           ...           ...      ...      ...      ...\n",
       "73    聯絡   1214.473612  20599.332482   Topic4  -4.8977   0.2261\n",
       "12    內容   1123.610761  23471.035546   Topic4  -4.9755   0.0178\n",
       "157   條件    985.592330   9250.548176   Topic4  -5.1066   0.8178\n",
       "50    方式   1096.363666  27286.554240   Topic4  -5.0000  -0.1574\n",
       "108   分鐘    855.902038   5145.829581   Topic4  -5.2476   1.2632\n",
       "\n",
       "[312 rows x 6 columns], token_table=      Topic      Freq  Term\n",
       "term                       \n",
       "210       1  0.984286    一律\n",
       "210       2  0.000088    一律\n",
       "210       3  0.014897    一律\n",
       "210       4  0.000789    一律\n",
       "213       1  0.995324   一次性\n",
       "...     ...       ...   ...\n",
       "207       3  0.003776   額滿將\n",
       "207       4  0.993006   額滿將\n",
       "5417      1  0.009930  高速公路\n",
       "5417      3  0.983038  高速公路\n",
       "5417      4  0.009930  高速公路\n",
       "\n",
       "[977 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 4, 3, 1])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2id = dictionary.token2id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 嘗試將軟體與硬體分開\n",
    "seed_topic_list = [\n",
    "    [\"受訪者\", \"訪談\", \"訪問\", \"實驗\", \"抽獎\"],\n",
    "    [\"聯絡人\", \"連結\", \"電子郵件\", \"聯絡\"],\n",
    "    [\"晶片\",\"技術\",\"英特爾\",\"台積電\"],\n",
    "    [\"軟體\",\"資訊\",\"網站\",\"系統\", \"開發\",\"專案\"],\n",
    "    [\"面試\", \"加班費\", \"履歷\", \"職缺\"]\n",
    "]\n",
    "seed_topics = {}\n",
    "for t_id, st in enumerate(seed_topic_list):\n",
    "    for word in st:\n",
    "        seed_topics[word2id[word]] = t_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# guidedlda 需要 DTM 格式作為 input，因此這邊利用 corpus2dense() 方法進行轉換\n",
    "X = corpus2dense(corpus, len(dictionary), len(corpus)).T.astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-04-19 15:48:34,425 : INFO : n_documents: 16310\n",
      "2025-04-19 15:48:34,427 : INFO : vocab_size: 23261\n",
      "2025-04-19 15:48:34,427 : INFO : n_words: 3460208\n",
      "2025-04-19 15:48:34,427 : INFO : n_topics: 5\n",
      "2025-04-19 15:48:34,427 : INFO : n_iter: 100\n",
      "2025-04-19 15:48:34,569 : WARNING : all zero row in document-term matrix found\n",
      "2025-04-19 15:48:48,928 : INFO : <0> log likelihood: -32236301\n",
      "2025-04-19 15:48:51,118 : INFO : <20> log likelihood: -24644227\n",
      "2025-04-19 15:48:53,125 : INFO : <40> log likelihood: -24503986\n",
      "2025-04-19 15:48:55,176 : INFO : <60> log likelihood: -24410422\n",
      "2025-04-19 15:48:57,211 : INFO : <80> log likelihood: -24380054\n",
      "2025-04-19 15:48:59,208 : INFO : <99> log likelihood: -24372692\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<guidedlda.guidedlda.GuidedLDA at 0x35be08350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = guidedlda.GuidedLDA(n_topics=5, n_iter=100, random_state=7, refresh=20)\n",
    "model.fit(X, seed_topics=seed_topics, seed_confidence=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 視覺化呈現結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0: 報名 活動 電話 台北市 人數 時間 通知 舉辦 車馬費 聯絡\n",
      "Topic 1: 工作 推定 方式 砍除 空白 情形 單位 內容 聯絡 第一項\n",
      "Topic 2: 台灣 美國 公司 晶片 表示 中國 半導體 台積電 員工 報導\n",
      "Topic 3: 工作 時間 方式 公司 小時 經驗 每日 薪資 工時 職缺\n",
      "Topic 4: 公司 工作 面試 問題 工程師 比較 覺得 知道 目前 時間\n"
     ]
    },
    {
     "data": {
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       "\n",
       "\n",
       "<div id=\"ldavis_el11410143865623202515089397\" style=\"background-color:white;\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el11410143865623202515089397_data = {\"mdsDat\": {\"x\": [0.3201173397220651, -0.19023463187538436, -0.2607463272727446, 0.005578426137548891, 0.12528519328851462], \"y\": [-0.18511431447978874, -0.04844727815680205, -0.04622182543745546, -0.024535462824165254, 0.3043188808982114], \"topics\": [1, 2, 3, 4, 5], \"cluster\": [1, 1, 1, 1, 1], \"Freq\": [48.197511873895586, 22.09306237143594, 16.249835544264528, 8.936207491728391, 4.523382718675541]}, \"tinfo\": {\"Term\": [\"\\u5de5\\u4f5c\", \"\\u806f\\u7d61\", \"\\u65b9\\u5f0f\", \"\\u63a8\\u5b9a\", \"\\u6642\\u9593\", \"\\u5167\\u5bb9\", \"\\u780d\\u9664\", \"\\u806f\\u7d61\\u4eba\", \"\\u5831\\u540d\", \"\\u7a7a\\u767d\", \"\\u96fb\\u8a71\", \"\\u7b2c\\u4e00\\u9805\", \"\\u5730\\u9ede\", \"\\u6d3b\\u52d5\", \"\\u60c5\\u5f62\", \"\\u901a\\u77e5\", \"\\u5c0f\\u6642\", \"\\u4eba\\u6578\", \"\\u516c\\u53f8\", \"\\u672a\\u8a3b\\u660e\", \"\\u767c\\u85aa\\u65e5\", \"\\u7c21\\u4ecb\", \"\\u5831\\u916c\", 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0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 5, 3, 4, 1]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el11410143865623202515089397\", ldavis_el11410143865623202515089397_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el11410143865623202515089397\", ldavis_el11410143865623202515089397_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el11410143865623202515089397\", ldavis_el11410143865623202515089397_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "1      0.320117 -0.185114       1        1  48.197512\n",
       "4     -0.190235 -0.048447       2        1  22.093062\n",
       "2     -0.260746 -0.046222       3        1  16.249836\n",
       "3      0.005578 -0.024535       4        1   8.936207\n",
       "0      0.125285  0.304319       5        1   4.523383, topic_info=    Term          Freq         Total Category  logprob  loglift\n",
       "34    工作  76840.000000  76840.000000  Default  30.0000  30.0000\n",
       "73    聯絡  21299.000000  21299.000000  Default  29.0000  29.0000\n",
       "50    方式  29140.000000  29140.000000  Default  28.0000  28.0000\n",
       "321   推定  24094.000000  24094.000000  Default  27.0000  27.0000\n",
       "54    時間  18625.000000  18625.000000  Default  26.0000  26.0000\n",
       "..   ...           ...           ...      ...      ...      ...\n",
       "12    內容   1557.893632  23920.861124   Topic5  -4.6098   0.3645\n",
       "97    使用   1065.246657   5107.343227   Topic5  -4.9900   1.5284\n",
       "157   條件   1084.233051   9565.435432   Topic5  -4.9723   0.9186\n",
       "50    方式   1188.158579  29140.798115   Topic5  -4.8808  -0.1038\n",
       "153   提供    985.303943   8273.116250   Topic5  -5.0680   0.9681\n",
       "\n",
       "[427 rows x 6 columns], token_table=       Topic      Freq Term\n",
       "term                       \n",
       "430        1  0.011399   一下\n",
       "430        2  0.986899   一下\n",
       "430        3  0.001838   一下\n",
       "431        1  0.009101   一些\n",
       "431        2  0.921909   一些\n",
       "...      ...       ...  ...\n",
       "2604       4  0.999451   願受\n",
       "771        5  0.999883   食用\n",
       "2626       5  1.000050   飲用\n",
       "20908      3  1.000287  馬斯克\n",
       "21549      3  1.000257  魏哲家\n",
       "\n",
       "[896 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 5, 3, 4, 1])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 整理／顯示主題模型結果\n",
    "n_top_words = 10\n",
    "topic_word = model.topic_word_\n",
    "# 取得corpus全部的詞彙表\n",
    "vocab = tuple(dictionary.token2id.keys())\n",
    "\n",
    "for i, topic_dist in enumerate(topic_word):\n",
    "    # 依照詞語機率從小到大排序，找出每個主題的前十個關鍵詞\n",
    "    topic_words = np.array(vocab)[np.argsort(topic_dist)][: -(n_top_words + 1) : -1]\n",
    "    print(\"Topic {}: {}\".format(i, \" \".join(topic_words)))\n",
    "    \n",
    "\n",
    "doc_topic = model.doc_topic_ # 文件-主題 分佈\n",
    "term_freq = tuple(dictionary.cfs.values()) # 每個詞在整個語料中出現的總次數\n",
    "doc_len = [sum(v for k, v in doc) for doc in corpus] # 每篇文章的長度\n",
    "\n",
    "## LDAvis\n",
    "pyLDAvis.enable_notebook()\n",
    "p = pyLDAvis.prepare(topic_word, doc_topic, doc_len, vocab = vocab, term_frequency = term_freq)\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在使用seed list後，成功將軟體與硬體分開\n",
    "- 第一個主題主要與工作應徵和聯絡方式相關\n",
    "- 第二個主題主要與軟體業相關\n",
    "- 第三個主題主要與半導體的產業發展相關\n",
    "- 第四個主題主要與勞權福利相關\n",
    "- 第五個主體與問卷研究、座談會相關"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_coef(logistic_reg_model=model_set['clf_logistic'], feature_names=vectorizer.get_feature_names_out(), top_n=10)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": [
    {
     "file_id": "1uBoWcykWbaq0b73itBFLoTl2k2-k5jmE",
     "timestamp": 1743771873830
    }
   ]
  },
  "kernelspec": {
   "display_name": "SMA2025",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}