{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Stopword-TfIdf.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"cells": [
{
"metadata": {
"id": "QYrPzfy_2rlN",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"
\n",
"# **Stop Words**\n",
"> **nltk**"
]
},
{
"metadata": {
"id": "jbYb-8vw2rlP",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 199
},
"outputId": "a45c3b29-99a2-4981-9ebd-f58ab32c8e3e"
},
"cell_type": "code",
"source": [
"# # 분석에 필요한 모듈설치\n",
"! pip3 install sklearn nltk\n",
"\n",
"import nltk\n",
"nltk.download('punkt')\n",
"nltk.download('stopwords')\n",
"news_texts = \"https://raw.githubusercontent.com/YongBeomKim/nltk_basic/master/data/News.txt\""
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: sklearn in /usr/local/lib/python3.6/dist-packages (0.0)\n",
"Requirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (3.2.5)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sklearn) (0.20.3)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk) (1.11.0)\n",
"Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.1.0)\n",
"Requirement already satisfied: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.14.6)\n",
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n",
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "xB3iast92rlW",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 399
},
"outputId": "e2a1a897-04c1-4e8b-ae10-c7abcb8207f9"
},
"cell_type": "code",
"source": [
"# Stopwords 사용가능한 언어목록 \n",
"from nltk.corpus import stopwords\n",
"stopwords.ensure_loaded\n",
"stopwords.__dict__.get('_fileids')"
],
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['arabic',\n",
" 'azerbaijani',\n",
" 'danish',\n",
" 'dutch',\n",
" 'english',\n",
" 'finnish',\n",
" 'french',\n",
" 'german',\n",
" 'greek',\n",
" 'hungarian',\n",
" 'indonesian',\n",
" 'italian',\n",
" 'kazakh',\n",
" 'nepali',\n",
" 'norwegian',\n",
" 'portuguese',\n",
" 'romanian',\n",
" 'russian',\n",
" 'spanish',\n",
" 'swedish',\n",
" 'turkish']"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"metadata": {
"id": "dmrwljjW2rla",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "8254f173-3907-4974-f89c-13f51b34ce80"
},
"cell_type": "code",
"source": [
"from nltk.corpus import stopwords\n",
"stop_eng = stopwords.words(\"english\")\n",
"stop_eng[:8]"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves']"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"metadata": {
"id": "ljzmOev_2rld",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "50ffcd53-639a-4a37-a150-a54ccabe8d87"
},
"cell_type": "code",
"source": [
"# 영문 내용을 소문자로 전처리\n",
"texts = 'I like such a Wonderful Snow Ice Cream'\n",
"texts = texts.lower()\n",
"texts"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'i like such a wonderful snow ice cream'"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"metadata": {
"id": "yVksIyqn2rlg",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "7ebf4bb3-510e-4f17-d80c-3c7ee27c498c"
},
"cell_type": "code",
"source": [
"from nltk import word_tokenize\n",
"tokens = word_tokenize(texts)\n",
"tokens"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['i', 'like', 'such', 'a', 'wonderful', 'snow', 'ice', 'cream']"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"metadata": {
"id": "oSCLRCcC2rlj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "9c085901-4a9d-48ce-8cf1-b06d2b59b558"
},
"cell_type": "code",
"source": [
"tokens = [word for word in tokens \n",
" if word not in stop_eng]\n",
"print(tokens)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"['like', 'wonderful', 'snow', 'ice', 'cream']\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "qVnpZfqQ2rll",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"
\n",
"# **Tf-idf**\n",
"> **scikit learn**"
]
},
{
"metadata": {
"id": "96upZtAh2rlm",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 55
},
"outputId": "95b41365-f6de-4ca5-91a8-ec86033488f4"
},
"cell_type": "code",
"source": [
"# with open(news_texts, 'r') as f:\n",
"# texts = f.read()\n",
"# texts = texts.lower()\n",
"\n",
"import requests\n",
"texts = requests.get(news_texts).text.lower()\n",
"texts[:300]"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'samsung electronics posted krw 65.98 trillion in consolidated revenue and krw 15.15 trillion in operating profit for the fourth quarter of 2017.\\n \\noverall, the company reported full-year revenue of krw 239.58 trillion and full-year operating profit of krw 53.65 trillion.\\n \\nfourth quarter earnings we'"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"metadata": {
"id": "fLapw_Da2rlp",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 55
},
"outputId": "0f93e0ca-ae15-4615-fdc4-abe26a6318b5"
},
"cell_type": "code",
"source": [
"# 영문 Token만 추출한다 (숫자와 문장기호를 제거)\n",
"import re\n",
"tokenizer = re.compile('[a-z]\\w+')\n",
"tokens = tokenizer.findall(texts)\n",
"document = \" \".join(tokens)\n",
"document[:300]"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'samsung electronics posted krw trillion in consolidated revenue and krw trillion in operating profit for the fourth quarter of overall the company reported full year revenue of krw trillion and full year operating profit of krw trillion fourth quarter earnings were driven by the components business '"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"metadata": {
"id": "9kfmIR0J2rlr",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1835
},
"outputId": "e1fce611-f0c1-422d-e097-bec81af70888"
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"tfidf_vec = TfidfVectorizer(stop_words='english')\n",
"transformed = tfidf_vec.fit_transform(raw_documents = [document])\n",
"transformed = np.array(transformed.todense())\n",
"transformed"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0.01705916, 0.00852958, 0.00852958, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.01705916, 0.00852958, 0.03411832,\n",
" 0.02558874, 0.01705916, 0.00852958, 0.00852958, 0.00852958,\n",
" 0.03411832, 0.02558874, 0.03411832, 0.01705916, 0.00852958,\n",
" 0.01705916, 0.0426479 , 0.03411832, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.05970706, 0.00852958, 0.0426479 , 0.01705916,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.00852958, 0.01705916,\n",
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" 0.06823664, 0.01705916, 0.00852958, 0.05970706, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.05117748, 0.00852958,\n",
" 0.01705916, 0.02558874, 0.00852958, 0.00852958, 0.01705916,\n",
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" 0.00852958, 0.02558874, 0.05117748, 0.00852958, 0.10235496,\n",
" 0.00852958, 0.01705916, 0.01705916, 0.00852958, 0.01705916,\n",
" 0.02558874, 0.00852958, 0.0852958 , 0.07676622, 0.03411832,\n",
" 0.00852958, 0.14500285, 0.05117748, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.0852958 , 0.00852958,\n",
" 0.03411832, 0.00852958, 0.00852958, 0.09382538, 0.02558874,\n",
" 0.00852958, 0.02558874, 0.01705916, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.03411832, 0.05970706, 0.01705916, 0.01705916,\n",
" 0.0426479 , 0.00852958, 0.00852958, 0.00852958, 0.02558874,\n",
" 0.02558874, 0.02558874, 0.0426479 , 0.01705916, 0.10235496,\n",
" 0.0426479 , 0.00852958, 0.13647327, 0.01705916, 0.00852958,\n",
" 0.00852958, 0.02558874, 0.00852958, 0.00852958, 0.03411832,\n",
" 0.00852958, 0.00852958, 0.05117748, 0.01705916, 0.00852958,\n",
" 0.00852958, 0.07676622, 0.06823664, 0.07676622, 0.05970706,\n",
" 0.00852958, 0.01705916, 0.02558874, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.03411832, 0.02558874, 0.00852958, 0.01705916,\n",
" 0.01705916, 0.00852958, 0.13647327, 0.05970706, 0.00852958,\n",
" 0.01705916, 0.01705916, 0.00852958, 0.03411832, 0.10235496,\n",
" 0.00852958, 0.0426479 , 0.03411832, 0.05117748, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.0426479 , 0.00852958, 0.00852958,\n",
" 0.05117748, 0.05970706, 0.02558874, 0.00852958, 0.02558874,\n",
" 0.02558874, 0.02558874, 0.00852958, 0.01705916, 0.0426479 ,\n",
" 0.01705916, 0.00852958, 0.00852958, 0.06823664, 0.00852958,\n",
" 0.00852958, 0.01705916, 0.00852958, 0.14500285, 0.0426479 ,\n",
" 0.02558874, 0.02558874, 0.00852958, 0.00852958, 0.00852958,\n",
" 0.01705916, 0.05970706, 0.00852958, 0.03411832, 0.00852958,\n",
" 0.02558874, 0.00852958, 0.01705916, 0.16206201, 0.00852958,\n",
" 0.0426479 , 0.00852958, 0.00852958, 0.00852958, 0.05970706,\n",
" 0.01705916, 0.00852958, 0.01705916, 0.02558874, 0.01705916,\n",
" 0.15353243, 0.01705916, 0.0426479 , 0.0426479 , 0.01705916,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.00852958, 0.09382538,\n",
" 0.00852958, 0.00852958, 0.01705916, 0.06823664, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.01705916, 0.01705916, 0.01705916,\n",
" 0.00852958, 0.00852958, 0.02558874, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.07676622, 0.06823664, 0.00852958, 0.00852958,\n",
" 0.01705916, 0.01705916, 0.01705916, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.01705916, 0.01705916, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.06823664, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.14500285, 0.01705916, 0.00852958,\n",
" 0.03411832, 0.01705916, 0.00852958, 0.02558874, 0.0852958 ,\n",
" 0.05117748, 0.02558874, 0.22176907, 0.06823664, 0.05117748,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.00852958, 0.01705916,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.01705916, 0.01705916,\n",
" 0.07676622, 0.00852958, 0.00852958, 0.19618033, 0.00852958,\n",
" 0.00852958, 0.02558874, 0.01705916, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.00852958, 0.03411832,\n",
" 0.01705916, 0.02558874, 0.02558874, 0.01705916, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.01705916, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.07676622, 0.02558874,\n",
" 0.00852958, 0.01705916, 0.00852958, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.17912117, 0.24735781, 0.02558874, 0.00852958,\n",
" 0.01705916, 0.01705916, 0.02558874, 0.00852958, 0.13647327,\n",
" 0.00852958, 0.01705916, 0.00852958, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.03411832, 0.00852958, 0.03411832,\n",
" 0.00852958, 0.0426479 , 0.05117748, 0.02558874, 0.01705916,\n",
" 0.01705916, 0.05970706, 0.00852958, 0.02558874, 0.01705916,\n",
" 0.00852958, 0.01705916, 0.00852958, 0.06823664, 0.09382538,\n",
" 0.00852958, 0.00852958, 0.05117748, 0.00852958, 0.03411832,\n",
" 0.00852958, 0.00852958, 0.02558874, 0.02558874, 0.02558874,\n",
" 0.01705916, 0.00852958, 0.00852958, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.01705916, 0.00852958, 0.0426479 , 0.00852958,\n",
" 0.05970706, 0.00852958, 0.12794369, 0.00852958, 0.03411832,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.01705916, 0.05970706,\n",
" 0.00852958, 0.05970706, 0.05117748, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.12794369, 0.00852958, 0.05970706,\n",
" 0.05970706, 0.00852958, 0.0426479 , 0.00852958, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.00852958, 0.00852958,\n",
" 0.00852958, 0.03411832, 0.00852958, 0.00852958, 0.01705916,\n",
" 0.00852958, 0.00852958, 0.02558874, 0.12794369, 0.00852958,\n",
" 0.00852958, 0.00852958, 0.00852958, 0.00852958, 0.06823664,\n",
" 0.00852958, 0.06823664]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"metadata": {
"id": "g9Xy-KJ82rlu",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 9054
},
"outputId": "f1cb1ccc-093a-48f6-96ec-feab190c42f2"
},
"cell_type": "code",
"source": [
"index_value = {i[1]:i[0] for i in tfidf_vec.vocabulary_.items()}\n",
"fully_indexed = {index_value[column]:value for row in transformed \n",
" for (column,value) in enumerate(row)}\n",
"fully_indexed"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'a8': 0.017059159255788922,\n",
" 'accelerating': 0.008529579627894461,\n",
" 'accountable': 0.008529579627894461,\n",
" 'achieve': 0.008529579627894461,\n",
" 'achieved': 0.008529579627894461,\n",
" 'achieving': 0.008529579627894461,\n",
" 'actively': 0.008529579627894461,\n",
" 'activities': 0.017059159255788922,\n",
" 'add': 0.008529579627894461,\n",
" 'added': 0.034118318511577844,\n",
" 'addition': 0.025588738883683383,\n",
" 'address': 0.017059159255788922,\n",
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" 'adoption': 0.008529579627894461,\n",
" 'advanced': 0.008529579627894461,\n",
" 'affected': 0.034118318511577844,\n",
" 'ahead': 0.025588738883683383,\n",
" 'ai': 0.034118318511577844,\n",
" 'aim': 0.017059159255788922,\n",
" 'aiming': 0.008529579627894461,\n",
" 'aims': 0.017059159255788922,\n",
" 'america': 0.042647898139472305,\n",
" 'amid': 0.034118318511577844,\n",
" 'amounting': 0.008529579627894461,\n",
" 'announcement': 0.008529579627894461,\n",
" 'anticipated': 0.008529579627894461,\n",
" 'appliances': 0.05970705739526123,\n",
" 'application': 0.008529579627894461,\n",
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" 'year': 0.06823663702315569,\n",
" 'yield': 0.008529579627894461,\n",
" 'yoy': 0.06823663702315569}"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"metadata": {
"id": "uESNNyRq3XnA",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}