{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Corona Virus Tweets Sentiment Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "UoLKpfUfbnER",
        "SQbZTpb1ZDiZ",
        "zl6kSLLigcnA",
        "vpZ6tIdDai0L",
        "lVyQxWs-24ng",
        "OUXeMMzm5xR4",
        "GE18axI0mja_",
        "wDxSaPeRm5g_"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<div style=\"border-radius:10px;\n",
        "            border:#0b0265 solid;\n",
        "           background-color:#99ffcc;\n",
        "           font-size:110%;\n",
        "           letter-spacing:0.5px;\n",
        "            text-align: center\">\n",
        "\n",
        "<center><h1 style=\"padding: 25px 0px; color:#0b0265; font-weight: bold; font-family: Cursive\">\n",
        "Corona Virus Tweets Sentiment Prediction 😷 🦠</h1></center>    \n",
        "\n",
        "</div>"
      ],
      "metadata": {
        "id": "OfINyREraBCO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p align=\"center\">\n",
        "  <img width=\"600\" height=\"300\" src=\"https://spectrum.ieee.org/media-library/photograph-of-someone-holding-a-photo-with-twitter-on-it-and-the-words-covid-19-in-the-background.jpg?id=25591747&width=1200&height=721\">\n",
        "</p>"
      ],
      "metadata": {
        "id": "iZ4T8q9Qbus_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Problem Statement**"
      ],
      "metadata": {
        "id": "UoLKpfUfbnER"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p align=\"center\">\n",
        "  <img width=\"600\" height=\"300\" src=\"https://habrastorage.org/webt/t6/sr/jr/t6srjrmjjmm6qn8gpld9emy4txu.gif\">\n",
        "</p>"
      ],
      "metadata": {
        "id": "X5mYqGaUcBV2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We Perform Text Classification on the data. The tweets have been pulled from Twitter and manual tagging has been done then.\n",
        "The names and usernames have been given codes to avoid any privacy concerns.\n",
        "\n",
        "**Columns:**\n",
        "\n",
        "1. Location\n",
        "2. Tweet At\n",
        "3. Original Tweet\n",
        "4. Label"
      ],
      "metadata": {
        "id": "57Ja3NuwIlcw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Import Libraries and Data**"
      ],
      "metadata": {
        "id": "SQbZTpb1ZDiZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/tylerneylon/explacy/master/explacy.py"
      ],
      "metadata": {
        "id": "Jtpgw4dkxu8B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c87a47c-40be-465b-fa1a-f9bb47d6888a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-06-26 14:14:59--  https://raw.githubusercontent.com/tylerneylon/explacy/master/explacy.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 6896 (6.7K) [text/plain]\n",
            "Saving to: ‘explacy.py’\n",
            "\n",
            "\rexplacy.py            0%[                    ]       0  --.-KB/s               \rexplacy.py          100%[===================>]   6.73K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2022-06-26 14:14:59 (7.87 MB/s) - ‘explacy.py’ saved [6896/6896]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install emoji"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HCgPQiTzt95I",
        "outputId": "9e6c0a42-36c7-4987-ede6-e66c13aee796"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting emoji\n",
            "  Downloading emoji-1.7.0.tar.gz (175 kB)\n",
            "\u001b[K     |████████████████████████████████| 175 kB 4.5 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: emoji\n",
            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for emoji: filename=emoji-1.7.0-py3-none-any.whl size=171046 sha256=83add1ed8950dd1a1a2d89e437712a0d90f86b6f8638b2c7538bfc04245796cf\n",
            "  Stored in directory: /root/.cache/pip/wheels/8a/4e/b6/57b01db010d17ef6ea9b40300af725ef3e210cb1acfb7ac8b6\n",
            "Successfully built emoji\n",
            "Installing collected packages: emoji\n",
            "Successfully installed emoji-1.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "_9CKkJnPYUio",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "8632cb3d-2c6b-448d-8e64-0819203025ff"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# visualisation\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import missingno as msno\n",
        "from PIL import Image\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.figure_factory as ff\n",
        "from plotly.offline import download_plotlyjs, init_notebook_mode, iplot\n",
        "init_notebook_mode(connected=True)\n",
        "\n",
        "# handle table-like data and matrices\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from collections import defaultdict, Counter\n",
        "import re\n",
        "import emoji\n",
        "\n",
        "# natural language processing\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "import string\n",
        "import spacy\n",
        "from spacy import displacy\n",
        "import explacy\n",
        "from wordcloud import WordCloud\n",
        "\n",
        "# ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# model\n",
        "from keras import backend as K\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "# to display the total number of rows and columns present in the dataset\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.max_rows', None)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UmgeqIch4RLD",
        "outputId": "fd36dea1-7eb6-41cd-cac1-8b5970438d30"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle datasets download datatattle/covid-19-nlp-text-classification\n",
        "! unzip /content/covid-19-nlp-text-classification.zip"
      ],
      "metadata": {
        "collapsed": true,
        "id": "zkLlal7MZLGW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d897e41c-362f-4201-a187-69b290f5a357"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.1.2)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2022.6.15)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Downloading covid-19-nlp-text-classification.zip to /content\n",
            "  0% 0.00/4.38M [00:00<?, ?B/s]\n",
            "100% 4.38M/4.38M [00:00<00:00, 238MB/s]\n",
            "Archive:  /content/covid-19-nlp-text-classification.zip\n",
            "  inflating: Corona_NLP_test.csv     \n",
            "  inflating: Corona_NLP_train.csv    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train = pd.read_csv('Corona_NLP_train.csv', encoding='latin1')\n",
        "data_test = pd.read_csv('Corona_NLP_test.csv', encoding='latin1')"
      ],
      "metadata": {
        "id": "RlOjBISgavyS"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Handling Missing Values**"
      ],
      "metadata": {
        "id": "zl6kSLLigcnA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_train.isnull().sum()"
      ],
      "metadata": {
        "id": "mWl8ZWjPgh9r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71e54e95-f9fc-4e1b-dda8-899f6b9a99d1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UserName            0\n",
              "ScreenName          0\n",
              "Location         8590\n",
              "TweetAt             0\n",
              "OriginalTweet       0\n",
              "Sentiment           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_test.isnull().sum()"
      ],
      "metadata": {
        "id": "KjfcHjpngpfC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd02ea9d-25ca-4dff-9304-172b256f6e38"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UserName           0\n",
              "ScreenName         0\n",
              "Location         834\n",
              "TweetAt            0\n",
              "OriginalTweet      0\n",
              "Sentiment          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "null = data_train.isnull().sum().sort_values(ascending=False)\n",
        "total = data_train.shape[0]\n",
        "percent_missing = (data_train.isnull().sum()/total).sort_values(ascending=False)\n",
        "\n",
        "missing_data= pd.concat([null, percent_missing], axis=1, keys=['Total missing', 'Percent missing'])\n",
        "\n",
        "missing_data.reset_index(inplace=True)\n",
        "missing_data= missing_data.rename(columns= { 'index': ' column name'})\n",
        " \n",
        "print ('Missing Values in Each Column:\\n', missing_data)"
      ],
      "metadata": {
        "id": "RaFxG0kniYSW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88f55848-9e5d-4072-fa4c-6366c563297c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values in Each Column:\n",
            "      column name  Total missing  Percent missing\n",
            "0       Location           8590         0.208713\n",
            "1       UserName              0         0.000000\n",
            "2     ScreenName              0         0.000000\n",
            "3        TweetAt              0         0.000000\n",
            "4  OriginalTweet              0         0.000000\n",
            "5      Sentiment              0         0.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msno.matrix(data_train, figsize=(10, 5));"
      ],
      "metadata": {
        "id": "qi0qu_Vcgnhk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "30fa7112-a47a-42ac-c9eb-5262051a12b1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are missing values in the location variable. Since we don't use the location for prediction, it doesn't matter."
      ],
      "metadata": {
        "id": "2t0Wphnwhgn3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "let's find if we have duplicate rows."
      ],
      "metadata": {
        "id": "kV2kFL9YhA8-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_train.duplicated().sum()"
      ],
      "metadata": {
        "id": "wGr9ccmMg6qR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c83e4c5-a96a-41c7-c336-f89ecdb95c86"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_test.duplicated().sum()"
      ],
      "metadata": {
        "id": "-22Lsjrtg9YR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15fb9ecf-98d2-4b03-f781-d281d1b6f1d0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Analysis and Visualization**"
      ],
      "metadata": {
        "id": "vpZ6tIdDai0L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_train.head(5)"
      ],
      "metadata": {
        "id": "TiZf6W1aZniy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "96800ae2-052c-4a9a-fd64-7984249b3b5f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   UserName  ScreenName   Location     TweetAt  \\\n",
              "0      3799       48751     London  16-03-2020   \n",
              "1      3800       48752         UK  16-03-2020   \n",
              "2      3801       48753  Vagabonds  16-03-2020   \n",
              "3      3802       48754        NaN  16-03-2020   \n",
              "4      3803       48755        NaN  16-03-2020   \n",
              "\n",
              "                                       OriginalTweet           Sentiment  \n",
              "0  @MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...             Neutral  \n",
              "1  advice Talk to your neighbours family to excha...            Positive  \n",
              "2  Coronavirus Australia: Woolworths to give elde...            Positive  \n",
              "3  My food stock is not the only one which is emp...            Positive  \n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train.shape, data_test.shape"
      ],
      "metadata": {
        "id": "CoEAfpXHfC4g",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90ae7adf-d774-4abb-8f51-1e63e995d655"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((41157, 6), (3798, 6))"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train.info()"
      ],
      "metadata": {
        "id": "_jKuSXEffg0e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cfa5549-b758-4b6f-e11d-9c299d8fd8c4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 41157 entries, 0 to 41156\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   UserName       41157 non-null  int64 \n",
            " 1   ScreenName     41157 non-null  int64 \n",
            " 2   Location       32567 non-null  object\n",
            " 3   TweetAt        41157 non-null  object\n",
            " 4   OriginalTweet  41157 non-null  object\n",
            " 5   Sentiment      41157 non-null  object\n",
            "dtypes: int64(2), object(4)\n",
            "memory usage: 1.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train.describe(include=object).T"
      ],
      "metadata": {
        "id": "IN88GbkOf1Y0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "6e26c9d7-7237-4fae-a3be-0e0d74d39303"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               count unique  \\\n",
              "Location       32567  12220   \n",
              "TweetAt        41157     30   \n",
              "OriginalTweet  41157  41157   \n",
              "Sentiment      41157      5   \n",
              "\n",
              "                                                             top   freq  \n",
              "Location                                                  London    540  \n",
              "TweetAt                                               20-03-2020   3448  \n",
              "OriginalTweet  @MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...      1  \n",
              "Sentiment                                               Positive  11422  "
            ],
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              "\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-f0fe5f8d-c7eb-4fb9-814a-85a40a266d42 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-f0fe5f8d-c7eb-4fb9-814a-85a40a266d42');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Unique Sentiments in Train Data:', data_train.Sentiment.unique(), '\\n')\n",
        "\n",
        "print(data_train.Sentiment.value_counts())"
      ],
      "metadata": {
        "id": "nn03dhp-iwCe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea4151dd-9a9b-43a1-d616-553c939b6e22"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique Sentiments in Train Data: ['Neutral' 'Positive' 'Extremely Negative' 'Negative' 'Extremely Positive'] \n",
            "\n",
            "Positive              11422\n",
            "Negative               9917\n",
            "Neutral                7713\n",
            "Extremely Positive     6624\n",
            "Extremely Negative     5481\n",
            "Name: Sentiment, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Unique Sentiments in Test Data:', data_test.Sentiment.unique(), '\\n')\n",
        "\n",
        "print(data_test.Sentiment.value_counts())"
      ],
      "metadata": {
        "id": "f0-ZFEm7lwA1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "615df0cc-077a-4f28-a408-3dc0120d447a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique Sentiments in Test Data: ['Extremely Negative' 'Positive' 'Extremely Positive' 'Negative' 'Neutral'] \n",
            "\n",
            "Negative              1041\n",
            "Positive               947\n",
            "Neutral                619\n",
            "Extremely Positive     599\n",
            "Extremely Negative     592\n",
            "Name: Sentiment, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train['Sentiment'] = data_train['Sentiment'].map({'Extremely Positive':'Positive','Extremely Negative':'Negative','Neutral':'Neutral','Positive':'Positive','Negative':'Negative'})\n",
        "data_test['Sentiment'] = data_test['Sentiment'].map({'Extremely Positive':'Positive','Extremely Negative':'Negative','Neutral': 'Neutral','Positive':'Positive','Negative':'Negative'})"
      ],
      "metadata": {
        "id": "JI7H4XVxjRvU"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Unique Sentiments in Train Data:', data_train.Sentiment.unique(), '\\n')\n",
        "\n",
        "class_d = data_train.groupby('Sentiment').count()['OriginalTweet'].reset_index().sort_values(by='OriginalTweet',ascending=False)\n",
        "class_d.style.background_gradient(cmap='Blues')"
      ],
      "metadata": {
        "id": "cVxjhMfFlUiU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "outputId": "39d05b23-fcf7-4490-8ce9-9c74e1e4f2aa"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique Sentiments in Train Data: ['Neutral' 'Positive' 'Negative'] \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<pandas.io.formats.style.Styler at 0x7f911cc2c8d0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_d24e0_row0_col1 {\n",
              "  background-color: #08306b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d24e0_row1_col1 {\n",
              "  background-color: #2272b6;\n",
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              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >Sentiment</th>\n",
              "      <th class=\"col_heading level0 col1\" >OriginalTweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th id=\"T_d24e0_level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
              "      <td id=\"T_d24e0_row0_col0\" class=\"data row0 col0\" >Positive</td>\n",
              "      <td id=\"T_d24e0_row0_col1\" class=\"data row0 col1\" >18046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d24e0_level0_row1\" class=\"row_heading level0 row1\" >0</th>\n",
              "      <td id=\"T_d24e0_row1_col0\" class=\"data row1 col0\" >Negative</td>\n",
              "      <td id=\"T_d24e0_row1_col1\" class=\"data row1 col1\" >15398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d24e0_level0_row2\" class=\"row_heading level0 row2\" >1</th>\n",
              "      <td id=\"T_d24e0_row2_col0\" class=\"data row2 col0\" >Neutral</td>\n",
              "      <td id=\"T_d24e0_row2_col1\" class=\"data row2 col1\" >7713</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Unique Sentiments in Test Data:', data_test.Sentiment.unique(), '\\n')\n",
        "\n",
        "class_d = data_test.groupby('Sentiment').count()['OriginalTweet'].reset_index().sort_values(by='OriginalTweet',ascending=False)\n",
        "class_d.style.background_gradient(cmap='Blues')"
      ],
      "metadata": {
        "id": "FVr6c3uRltpr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "outputId": "20466811-488f-4b71-e3f0-c6f2d3770299"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique Sentiments in Test Data: ['Negative' 'Positive' 'Neutral'] \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f911ec09f10>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_16a76_row0_col1 {\n",
              "  background-color: #08306b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_16a76_row1_col1 {\n",
              "  background-color: #08468b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_16a76_row2_col1 {\n",
              "  background-color: #f7fbff;\n",
              "  color: #000000;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_16a76_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >Sentiment</th>\n",
              "      <th class=\"col_heading level0 col1\" >OriginalTweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_16a76_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_16a76_row0_col0\" class=\"data row0 col0\" >Negative</td>\n",
              "      <td id=\"T_16a76_row0_col1\" class=\"data row0 col1\" >1633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16a76_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
              "      <td id=\"T_16a76_row1_col0\" class=\"data row1 col0\" >Positive</td>\n",
              "      <td id=\"T_16a76_row1_col1\" class=\"data row1 col1\" >1546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16a76_level0_row2\" class=\"row_heading level0 row2\" >1</th>\n",
              "      <td id=\"T_16a76_row2_col0\" class=\"data row2 col0\" >Neutral</td>\n",
              "      <td id=\"T_16a76_row2_col1\" class=\"data row2 col1\" >619</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train['Sentiment'].unique()"
      ],
      "metadata": {
        "id": "fjL3jGt6qyBo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85349704-a5b7-4dcd-ff68-faea30fa8047"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Neutral', 'Positive', 'Negative'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_test['Sentiment'].value_counts()"
      ],
      "metadata": {
        "id": "CnlLZo73q0Dp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2716c809-45f1-4b45-ed0f-1013441dd7f6"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Negative    1633\n",
              "Positive    1546\n",
              "Neutral      619\n",
              "Name: Sentiment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n",
        "\n",
        "fig.add_trace(go.Pie(labels=['Positive', 'Negative', 'Neutral'], values=data_train['Sentiment'].value_counts(), name='Train Sentiment', \n",
        "                     marker_colors=['gold', 'mediumturquoise', '#EC7063']), 1, 1)\n",
        "fig.add_trace(go.Pie(labels=['Positive', 'Negative', 'Neutral'], values=[data_test['Sentiment'].value_counts()[1], data_test['Sentiment'].value_counts()[0], data_test['Sentiment'].value_counts()[2]], name='Test Sentiment', \n",
        "                     marker_colors=['gold', 'mediumturquoise', '#EC7063']), 1, 2)\n",
        "\n",
        "fig.update_traces(hole=0.5, textfont_size=20, marker=dict(line=dict(color='black', width=2)))\n",
        "\n",
        "fig.update_layout(\n",
        "    title_text='<b>Sentiment Distribution in Train and Test Data<b>', \n",
        "    # Add annotations in the center of the donut pies.\n",
        "    annotations=[dict(text='Sentiment Train', x=0.135, y=0.5, font_size=20, showarrow=False),\n",
        "                 dict(text='Sentiment Test', x=0.86, y=0.5, font_size=20, showarrow=False)])\n",
        "iplot(fig)"
      ],
      "metadata": {
        "id": "ZUuJya0lniN4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "e152b381-7169-4a38-d680-4b047ed0906a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
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        "* There is uneven distribution of classes with positive taking the largest of pie followed by negative.\n"
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    {
      "cell_type": "code",
      "source": [
        "tweet_len_pos = data_train[data_train['Sentiment']=='Positive']['OriginalTweet'].str.len()\n",
        "tweet_len_neg = data_train[data_train['Sentiment']=='Negative']['OriginalTweet'].str.len()\n",
        "tweet_len_neu = data_train[data_train['Sentiment']=='Neutral']['OriginalTweet'].str.len()"
      ],
      "metadata": {
        "id": "vWvjiYuhu4R9"
      },
      "execution_count": 26,
      "outputs": []
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        "fig = make_subplots(rows=1, cols=3)\n",
        "\n",
        "fig.add_trace(go.Histogram(x = tweet_len_pos, name='Positive', marker_color='#EB89B5') , 1, 1)\n",
        "fig.add_trace(go.Histogram(x = tweet_len_neg, name='Negative', marker_color='#E9F00B') , 1, 2)\n",
        "fig.add_trace(go.Histogram(x = tweet_len_neu, name='Neutral', marker_color='#5DADE2') , 1, 3)\n",
        "\n",
        "fig.update_layout(title_text='<b>Characters in Tweets<b>')\n",
        "iplot(fig)"
      ],
      "metadata": {
        "id": "A9EoKYuPplzX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "2ebc57bd-e4fc-44e3-804a-5b7b2f5fa961"
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      "execution_count": 27,
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    {
      "cell_type": "code",
      "source": [
        "tweet_words_pos = data_train[data_train['Sentiment']=='Positive']['OriginalTweet'].str.split().map(lambda x: len(x))\n",
        "tweet_words_neg = data_train[data_train['Sentiment']=='Negative']['OriginalTweet'].str.split().map(lambda x: len(x))\n",
        "tweet_words_neu = data_train[data_train['Sentiment']=='Neutral']['OriginalTweet'].str.split().map(lambda x: len(x))"
      ],
      "metadata": {
        "id": "eaktBazdxYAu"
      },
      "execution_count": 28,
      "outputs": []
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        "fig = make_subplots(rows=1, cols=3)\n",
        "\n",
        "fig.add_trace(go.Histogram(x = tweet_words_pos, name='Positive', marker_color='#00CC96') , 1, 1)\n",
        "fig.add_trace(go.Histogram(x = tweet_words_neg, name='Negative', marker_color='#FFA15A') , 1, 2)\n",
        "fig.add_trace(go.Histogram(x = tweet_words_neu, name='Neutral', marker_color='#E5527A') , 1, 3)\n",
        "\n",
        "fig.update_layout(title_text='<b>Number of words in Tweets<b>')\n",
        "iplot(fig)"
      ],
      "metadata": {
        "id": "ZyPbGGrg0SFb",
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          "base_uri": "https://localhost:8080/",
          "height": 542
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        "outputId": "12df1125-5ed4-4a22-effe-7bfa235a9109"
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      "execution_count": 29,
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of words in Tweets<b>\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,0.2888888888888889]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0]},\"xaxis2\":{\"anchor\":\"y2\",\"domain\":[0.35555555555555557,0.6444444444444445]},\"yaxis2\":{\"anchor\":\"x2\",\"domain\":[0.0,1.0]},\"xaxis3\":{\"anchor\":\"y3\",\"domain\":[0.7111111111111111,1.0]},\"yaxis3\":{\"anchor\":\"x3\",\"domain\":[0.0,1.0]}},                        {\"responsive\": true}                    ).then(function(){\n",
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    {
      "cell_type": "code",
      "source": [
        "tweet_words_pos = data_train[data_train['Sentiment']=='Positive']['OriginalTweet'].str.split().map(lambda x: len(x))\n",
        "tweet_avgwords_pos = tweet_words_pos.map(lambda x: np.mean(x))\n",
        "tweet_words_neg = data_train[data_train['Sentiment']=='Negative']['OriginalTweet'].str.split().map(lambda x: len(x))\n",
        "tweet_avgwords_neg = tweet_words_neg.map(lambda x: np.mean(x))\n",
        "tweet_words_neu = data_train[data_train['Sentiment']=='Neutral']['OriginalTweet'].str.split().map(lambda x: len(x))\n",
        "tweet_avgwords_neu = tweet_words_neu.map(lambda x: np.mean(x))"
      ],
      "metadata": {
        "id": "sM4IdCgD020l"
      },
      "execution_count": 30,
      "outputs": []
    },
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        "fig = make_subplots(rows=1, cols=3)\n",
        "\n",
        "fig.add_trace(go.Histogram(x = tweet_avgwords_pos, name='Positive', marker_color='purple') , 1, 1)\n",
        "fig.add_trace(go.Histogram(x = tweet_avgwords_neg, name='Negative', marker_color='gold') , 1, 2)\n",
        "fig.add_trace(go.Histogram(x = tweet_avgwords_neu, name='Neutral', marker_color='#C82735') , 1, 3)\n",
        "\n",
        "fig.update_layout(title_text='<b>Average Word Length in Tweets<b>')\n",
        "iplot(fig)"
      ],
      "metadata": {
        "id": "BaLgNJrE14C4",
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          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "f31c132f-c53e-4ab6-b5df-fbfd210bdc22"
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      "execution_count": 31,
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    {
      "cell_type": "code",
      "source": [
        "stop = set(stopwords.words('english'))\n",
        "np.array(stop)"
      ],
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89d59a01-ba25-436b-da00-b7e897f02610"
      },
      "execution_count": 32,
      "outputs": [
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              "      dtype=object)"
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          },
          "metadata": {},
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        "def create_corpus(target):\n",
        "    corpus = [] \n",
        "    for x in data_train[data_train['Sentiment']==target]['OriginalTweet'].str.split():\n",
        "        for i in x:\n",
        "            corpus.append(i)\n",
        "    return corpus"
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      "metadata": {
        "id": "73dulxHFQK78"
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      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "corpus_pos = create_corpus('Positive')\n",
        "\n",
        "dic_pos = defaultdict(int)\n",
        "for word in corpus_pos:\n",
        "    if word in stop:\n",
        "        dic_pos[word]+=1\n",
        "\n",
        "top_stop_pos = sorted(dic_pos.items(), key=lambda x:x[1], reverse=True)[:10] \n",
        "\n",
        "w_pos = []\n",
        "n_pos = []\n",
        "for i in range(len(top_stop_pos)):\n",
        "  w_pos.append(top_stop_pos[i][0])\n",
        "  n_pos.append(top_stop_pos[i][1])"
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        "             title='<b>Most Common Stop Words in Positive Sentences<b>', text_auto=True, color=n_pos)\n",
        "\n",
        "fig.update_traces(marker=dict(line=dict(color='black', width=2)))\n",
        "fig.update_layout({'yaxis': {'title':'Count'}, 'xaxis': {'title':'Stop Words'}})\n",
        "iplot(fig)"
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        "top_stop_neg = sorted(dic_neg.items(), key=lambda x:x[1], reverse=True)[:10] \n",
        "\n",
        "w_neg = []\n",
        "n_neg = []\n",
        "for i in range(len(top_stop_neg)):\n",
        "  w_neg.append(top_stop_neg[i][0])\n",
        "  n_neg.append(top_stop_neg[i][1])"
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        "corpus_neu = create_corpus('Neutral')\n",
        "\n",
        "dic_neu = defaultdict(int)\n",
        "for word in corpus_neu:\n",
        "    if word in stop:\n",
        "        dic_neu[word]+=1\n",
        "\n",
        "top_stop_neu = sorted(dic_neu.items(), key=lambda x:x[1], reverse=True)[:10] \n",
        "\n",
        "w_neu = []\n",
        "n_neu = []\n",
        "for i in range(len(top_stop_neu)):\n",
        "  w_neu.append(top_stop_neu[i][0])\n",
        "  n_neu.append(top_stop_neu[i][1])"
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        "             title='<b>Most Common Stop Words in Neutral Sentences<b>', text_auto=True, color=n_neu)\n",
        "\n",
        "fig.update_traces(marker=dict(line=dict(color='black', width=2)))\n",
        "fig.update_layout({'yaxis': {'title':'Count'}, 'xaxis': {'title':'Stop Words'}})\n",
        "iplot(fig)"
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        "string.punctuation"
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      "metadata": {
        "id": "RlIfvrqkYl5Q",
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          "height": 35
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        "outputId": "c8c3774b-91eb-4375-cfb6-3b3e2c10f1dc"
      },
      "execution_count": 40,
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
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        "corpus_pos = create_corpus('Positive')\n",
        "\n",
        "dic_pos = defaultdict(int)\n",
        "for word in corpus_pos:\n",
        "    if word in string.punctuation:\n",
        "        dic_pos[word]+=1\n",
        "\n",
        "top_stop_pos = sorted(dic_pos.items(), key=lambda x:x[1], reverse=True)[:10] \n",
        "\n",
        "w_pos = []\n",
        "n_pos = []\n",
        "for i in range(len(top_stop_pos)):\n",
        "  w_pos.append(top_stop_pos[i][0])\n",
        "  n_pos.append(top_stop_pos[i][1])"
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      "execution_count": 41,
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        "fig = px.bar(x=w_pos, y=n_pos, color_continuous_scale=px.colors.sequential.Cividis,\n",
        "             title='<b>Most Common Punctuations in Positive Sentences<b>', text_auto=True, color=n_pos)\n",
        "\n",
        "fig.update_traces(marker=dict(line=dict(color='black', width=2)))\n",
        "fig.update_layout({'yaxis': {'title':'Count'}, 'xaxis': {'title':'Punctuations'}})\n",
        "iplot(fig)"
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        "corpus_neg = create_corpus('Negative')\n",
        "\n",
        "dic_neg = defaultdict(int)\n",
        "for word in corpus_neg:\n",
        "    if word in string.punctuation:\n",
        "        dic_neg[word]+=1\n",
        "\n",
        "top_stop_neg = sorted(dic_neg.items(), key=lambda x:x[1], reverse=True)[:10] \n",
        "\n",
        "w_neg = []\n",
        "n_neg = []\n",
        "for i in range(len(top_stop_neg)):\n",
        "  w_neg.append(top_stop_neg[i][0])\n",
        "  n_neg.append(top_stop_neg[i][1])"
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        "             title='<b>Most Common Punctuations in Negative Sentences<b>', text_auto=True, color=n_neg)\n",
        "\n",
        "fig.update_traces(marker=dict(line=dict(color='black', width=2)))\n",
        "fig.update_layout({'yaxis': {'title':'Count'}, 'xaxis': {'title':'Punctuations'}})\n",
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      "source": [
        "corpus_neu = create_corpus('Neutral')\n",
        "\n",
        "dic_neu = defaultdict(int)\n",
        "for word in corpus_neu:\n",
        "    if word in string.punctuation:\n",
        "        dic_neu[word]+=1\n",
        "\n",
        "top_stop_neu = sorted(dic_neu.items(), key=lambda x:x[1], reverse=True)[:10] \n",
        "\n",
        "w_neu = []\n",
        "n_neu = []\n",
        "for i in range(len(top_stop_neu)):\n",
        "  w_neu.append(top_stop_neu[i][0])\n",
        "  n_neu.append(top_stop_neu[i][1])"
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        "fig = px.bar(x=w_neu, y=n_neu, color_continuous_scale=px.colors.sequential.haline,\n",
        "             title='<b>Most Common Punctuations in Neutral Sentences<b>', text_auto=True, color=n_neu)\n",
        "\n",
        "fig.update_traces(marker=dict(line=dict(color='black', width=2)))\n",
        "fig.update_layout({'yaxis': {'title':'Count'}, 'xaxis': {'title':'Punctuations'}})\n",
        "iplot(fig)"
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        "    corpus = [] \n",
        "    for x in data_train['OriginalTweet'].str.split():\n",
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        "            corpus.append(i)\n",
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        "counter = Counter(create_corpus())\n",
        "common = counter.most_common()\n",
        "\n",
        "x=[]\n",
        "y=[]\n",
        "for word,count in common[:40]:\n",
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      "cell_type": "code",
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        "hashtag_list = []\n",
        "for word in create_corpus():\n",
        "   if word[0] == '#':\n",
        "     hashtag_list.append(word[1:])"
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      "metadata": {
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        "counter_hash = Counter(hashtag_list)\n",
        "common_hash = counter_hash.most_common()\n",
        "\n",
        "x=[]\n",
        "y=[]\n",
        "for word,count in common_hash[:12]:\n",
        "    if (word not in stop) :\n",
        "        x.append(word)\n",
        "        y.append(count)"
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        "             title='<b>Most Common Hashtags<b>', text_auto=True, color=y[::-1])\n",
        "\n",
        "fig.update_traces(marker=dict(line=dict(color='black', width=2)))\n",
        "fig.update_layout({'yaxis': {'title':'Hashtags'}, 'xaxis': {'title':'Count'}})\n",
        "iplot(fig)"
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        "mention_list = []\n",
        "for word in create_corpus():\n",
        "   if word[0] == '@':\n",
        "     mention_list.append(word[1:])"
      ],
      "metadata": {
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      },
      "execution_count": 53,
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        "counter_mention = Counter(mention_list)\n",
        "common_mention = counter_mention.most_common()\n",
        "\n",
        "x=[]\n",
        "y=[]\n",
        "for word,count in common_mention[1:13]:\n",
        "    if (word not in stop) :\n",
        "        x.append(word)\n",
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      "execution_count": 54,
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        "             title='<b>Most Common Mentions<b>', text_auto=True, color=y[::-1])\n",
        "\n",
        "fig.update_traces(marker=dict(line=dict(color='black', width=2)))\n",
        "fig.update_layout({'yaxis': {'title':'Mentions'}, 'xaxis': {'title':'Count'}})\n",
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "nlp = spacy.load('en_core_web_sm')"
      ],
      "metadata": {
        "id": "3Nd0SVkLyJOm"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "explacy.print_parse_info(nlp, data_train['OriginalTweet'][8])"
      ],
      "metadata": {
        "id": "Nj-leDFAxAGD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e298ee25-a593-497d-8028-0b26d487b137"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dep tree          Token                   Dep type Lemma                   Part of Sp\n",
            "───────────────── ─────────────────────── ──────── ─────────────────────── ──────────\n",
            "       ┌────►┌┬── Due                     prep     due                     ADP       \n",
            "       │     │└─► to                      pcomp    to                      PART      \n",
            "       │     └──► COVID-19                pobj     covid-19                VERB      \n",
            "       │     ┌──► our                     poss     our                     PRON      \n",
            "       │     │┌─► retail                  amod     retail                  ADJ       \n",
            "       │┌─►┌─┼┼── store                   nsubj    store                   NOUN      \n",
            "       ││  │ │└─► and                     cc       and                     CCONJ     \n",
            "       ││  │ └──► classroom               conj     classroom               NOUN      \n",
            "       ││  └─►┌── in                      prep     in                      ADP       \n",
            "       ││     └─► Atlanta                 pobj     Atlanta                 PROPN     \n",
            "       ││    ┌──► will                    aux      will                    AUX       \n",
            "       ││    │┌─► not                     neg      not                     PART      \n",
            "┌┬┬┬┬──┴┴────┴┼── be                      ROOT     be                      AUX       \n",
            "│││││         └─► open                    acomp    open                    ADJ       \n",
            "││││└─►┌───────── for                     prep     for                     ADP       \n",
            "││││   │  ┌─►┌┬── walk                    amod     walk                    NOUN      \n",
            "││││   │  │  │└─► -                       punct    -                       PUNCT     \n",
            "││││   │  │  └──► in                      prt      in                      ADP       \n",
            "││││   └─►└──┬┬── business                pobj     business                NOUN      \n",
            "││││         │└─► or                      cc       or                      CCONJ     \n",
            "││││         └──► classes                 conj     class                   NOUN      \n",
            "│││└────►┌─────── for                     prep     for                     ADP       \n",
            "│││      │  ┌───► the                     det      the                     DET       \n",
            "│││      │  │┌──► next                    amod     next                    ADJ       \n",
            "│││      │  ││┌─► two                     nummod   two                     NUM       \n",
            "│││      └─►└┴┴── weeks                   pobj     week                    NOUN      \n",
            "││└─────────────► ,                       punct    ,                       PUNCT     \n",
            "│└─────►┌──────── beginning               advcl    begin                   VERB      \n",
            "│       └─►┌──┬── Monday                  npadvmod Monday                  PROPN     \n",
            "│          │  └─► ,                       punct    ,                       PUNCT     \n",
            "│          └─►┌── March                   appos    March                   PROPN     \n",
            "│             └─► 16                      nummod   16                      NUM       \n",
            "└───────────────► .                       punct    .                       PUNCT     \n",
            "            ┌───►                         dep                              SPACE     \n",
            "            │┌──► We                      nsubj    we                      PRON      \n",
            "            ││┌─► will                    aux      will                    AUX       \n",
            "   ┌┬───────┴┴┴── continue                ROOT     continue                VERB      \n",
            "   ││         ┌─► to                      aux      to                      PART      \n",
            "   │└─►┌┬─────┴── process                 xcomp    process                 VERB      \n",
            "   │   ││  ┌─►┌── online                  amod     online                  ADV       \n",
            "   │   ││  │  └─► and                     cc       and                     CCONJ     \n",
            "   │   ││  │  ┌─► phone                   compound phone                   NOUN      \n",
            "   │   │└─►└──┴── orders                  dobj     order                   NOUN      \n",
            "   │   └─────►┌── as                      prep     as                      ADP       \n",
            "   │          └─► normal                  amod     normal                  ADJ       \n",
            "   └────────────► !                       punct    !                       PUNCT     \n",
            "       ┌┬─────┬── Thank                   ROOT     thank                   VERB      \n",
            "       ││     └─► you                     dobj     you                     PRON      \n",
            "       │└─►┌───── for                     prep     for                     ADP       \n",
            "       │   │  ┌─► your                    poss     your                    PRON      \n",
            "       │   └─►└── understanding           pobj     understanding           NOUN      \n",
            "       └────────► !                       punct    !                       PUNCT     \n",
            "                  https://t.co/kw91zJ5O5i ROOT     https://t.co/kw91zJ5O5i PROPN     \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc = nlp(data_train['OriginalTweet'][8])\n",
        "\n",
        "# Count the frequencies of different coarse-grained POS tags:\n",
        "POS_counts = doc.count_by(spacy.attrs.POS)\n",
        "\n",
        "for k,v in sorted(POS_counts.items()):\n",
        "    print(f'{k}. {doc.vocab[k].text:{5}}: {v}')"
      ],
      "metadata": {
        "id": "wd1b0cinzv_H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "180939ea-bcc1-4a42-f14b-2831cfa3c405"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "84. ADJ  : 4\n",
            "85. ADP  : 7\n",
            "86. ADV  : 1\n",
            "87. AUX  : 3\n",
            "89. CCONJ: 3\n",
            "90. DET  : 1\n",
            "92. NOUN : 9\n",
            "93. NUM  : 2\n",
            "94. PART : 3\n",
            "95. PRON : 4\n",
            "96. PROPN: 4\n",
            "97. PUNCT: 6\n",
            "100. VERB : 5\n",
            "103. SPACE: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Render the dependency parse\n",
        "displacy.render(doc, style='dep', jupyter=True, options={'distance': 100, 'color': 'black', 'bg': '#e6ffff'})"
      ],
      "metadata": {
        "id": "qh2VYxTvz2tu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "4938e8ab-5bf9-4e70-90db-78182304c973"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "<span class=\"tex2jax_ignore\"><svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xml:lang=\"en\" id=\"9b3b19dc86b94486b89139f39c5b1359-0\" class=\"displacy\" width=\"4750\" height=\"387.0\" direction=\"ltr\" style=\"max-width: none; height: 387.0px; color: black; background: #e6ffff; font-family: Arial; direction: ltr\">\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">Due</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">ADP</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"150\">to</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"150\">PART</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"250\">COVID-19</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"250\">VERB</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"350\">our</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"350\">PRON</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"450\">retail</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"450\">ADJ</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"550\">store</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"550\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"650\">and</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"650\">CCONJ</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
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              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"750\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"850\">in</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"850\">ADP</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"950\">Atlanta</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"950\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1050\">will</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1050\">AUX</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1150\">not</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1150\">PART</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1250\">be</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1250\">AUX</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1350\">open</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1350\">ADJ</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1450\">for</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1450\">ADP</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1550\">walk-</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1550\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1650\">in</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1650\">ADP</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1750\">business</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1750\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1850\">or</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1850\">CCONJ</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1950\">classes</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1950\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2050\">for</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2050\">ADP</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2150\">the</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2150\">DET</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2250\">next</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2250\">ADJ</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2350\">two</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2350\">NUM</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2450\">weeks,</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2450\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2550\">beginning</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2550\">VERB</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2650\">Monday,</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2650\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2750\">March</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2750\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2850\">16.</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2850\">NUM</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2950\"> </tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2950\">SPACE</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3050\">We</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3050\">PRON</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3150\">will</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3150\">AUX</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3250\">continue</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3250\">VERB</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3350\">to</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3350\">PART</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3450\">process</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3450\">VERB</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3550\">online</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3550\">ADV</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3650\">and</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3650\">CCONJ</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3750\">phone</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3750\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3850\">orders</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3850\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3950\">as</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3950\">ADP</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4050\">normal!</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4050\">ADJ</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4150\">Thank</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4150\">VERB</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4250\">you</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4250\">PRON</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4350\">for</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4350\">ADP</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4450\">your</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4450\">PRON</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4550\">understanding!</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4550\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"297.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4650\">https://t.co/kw91zJ5O5i</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4650\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-9b3b19dc86b94486b89139f39c5b1359-0-0\" stroke-width=\"2px\" d=\"M70,252.0 C70,2.0 1250.0,2.0 1250.0,252.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-0\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">prep</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M70,254.0 L62,242.0 78,242.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
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              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M130.0,254.0 L138.0,242.0 122.0,242.0\" fill=\"currentColor\"/>\n",
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              "\n",
              "<g class=\"displacy-arrow\">\n",
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              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-2\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">pobj</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M235.0,254.0 L243.0,242.0 227.0,242.0\" fill=\"currentColor\"/>\n",
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              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-9b3b19dc86b94486b89139f39c5b1359-0-3\" stroke-width=\"2px\" d=\"M370,252.0 C370,152.0 535.0,152.0 535.0,252.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-3\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">poss</textPath>\n",
              "    </text>\n",
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              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-9b3b19dc86b94486b89139f39c5b1359-0-4\" stroke-width=\"2px\" d=\"M470,252.0 C470,202.0 530.0,202.0 530.0,252.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-4\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">amod</textPath>\n",
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              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-9b3b19dc86b94486b89139f39c5b1359-0-5\" stroke-width=\"2px\" d=\"M570,252.0 C570,52.0 1245.0,52.0 1245.0,252.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-5\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">nsubj</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M570,254.0 L562,242.0 578,242.0\" fill=\"currentColor\"/>\n",
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              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-9b3b19dc86b94486b89139f39c5b1359-0-6\" stroke-width=\"2px\" d=\"M570,252.0 C570,202.0 630.0,202.0 630.0,252.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-6\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">cc</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M630.0,254.0 L638.0,242.0 622.0,242.0\" fill=\"currentColor\"/>\n",
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              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-9b3b19dc86b94486b89139f39c5b1359-0-7\" stroke-width=\"2px\" d=\"M570,252.0 C570,152.0 735.0,152.0 735.0,252.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
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              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M735.0,254.0 L743.0,242.0 727.0,242.0\" fill=\"currentColor\"/>\n",
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              "\n",
              "<g class=\"displacy-arrow\">\n",
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              "<g class=\"displacy-arrow\">\n",
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              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-37\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">prep</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M3945.0,254.0 L3953.0,242.0 3937.0,242.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-9b3b19dc86b94486b89139f39c5b1359-0-38\" stroke-width=\"2px\" d=\"M3970,252.0 C3970,202.0 4030.0,202.0 4030.0,252.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-38\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">amod</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M4030.0,254.0 L4038.0,242.0 4022.0,242.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-9b3b19dc86b94486b89139f39c5b1359-0-39\" stroke-width=\"2px\" d=\"M4170,252.0 C4170,202.0 4230.0,202.0 4230.0,252.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-39\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">dobj</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M4230.0,254.0 L4238.0,242.0 4222.0,242.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-9b3b19dc86b94486b89139f39c5b1359-0-40\" stroke-width=\"2px\" d=\"M4170,252.0 C4170,152.0 4335.0,152.0 4335.0,252.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-40\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">prep</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M4335.0,254.0 L4343.0,242.0 4327.0,242.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-9b3b19dc86b94486b89139f39c5b1359-0-41\" stroke-width=\"2px\" d=\"M4470,252.0 C4470,202.0 4530.0,202.0 4530.0,252.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-41\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">poss</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M4470,254.0 L4462,242.0 4478,242.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-9b3b19dc86b94486b89139f39c5b1359-0-42\" stroke-width=\"2px\" d=\"M4370,252.0 C4370,152.0 4535.0,152.0 4535.0,252.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-9b3b19dc86b94486b89139f39c5b1359-0-42\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">pobj</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M4535.0,254.0 L4543.0,242.0 4527.0,242.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "</svg></span>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colors = {'GPE': 'linear-gradient(90deg, #aa9cfc, #fc9ce7)', 'DATE': 'radial-gradient(yellow, green)'}\n",
        "options = {'ents': ['GPE', 'DATE'], 'colors':colors}\n",
        "for sent in doc.sents:\n",
        "    displacy.render(nlp(sent.text), style='ent', jupyter=True, options=options)"
      ],
      "metadata": {
        "id": "1tEIZ7_X2Bxw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "outputId": "391fe3dc-0882-4769-9b24-da1b2d231cb5"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Due to COVID-19 our retail store and classroom in \n",
              "<mark class=\"entity\" style=\"background: linear-gradient(90deg, #aa9cfc, #fc9ce7); padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    Atlanta\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
              "</mark>\n",
              " will not be open for walk-in business or classes for \n",
              "<mark class=\"entity\" style=\"background: radial-gradient(yellow, green); padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    the next two weeks\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
              "</mark>\n",
              ", beginning \n",
              "<mark class=\"entity\" style=\"background: radial-gradient(yellow, green); padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    Monday, March 16\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
              "</mark>\n",
              ".</div></span>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\"> We will continue to process online and phone orders as normal!</div></span>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Thank you for your understanding!</div></span>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">https://t.co/kw91zJ5O5i</div></span>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Preprocessing**"
      ],
      "metadata": {
        "id": "lVyQxWs-24ng"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lower Case"
      ],
      "metadata": {
        "id": "fMHhFWzggKmd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_train['OriginalTweet'] = data_train['OriginalTweet'].agg(lambda x:x.lower())\n",
        "data_test['OriginalTweet'] = data_test['OriginalTweet'].agg(lambda x:x.lower())"
      ],
      "metadata": {
        "id": "pUgRlZYM3DYg"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove Stopwords"
      ],
      "metadata": {
        "id": "ISXLETbOjQCc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nlp = spacy.load('en_core_web_sm')\n",
        "stop = set(nlp.Defaults.stop_words)\n",
        "np.array(set(nlp.Defaults.stop_words))"
      ],
      "metadata": {
        "id": "zjV1BvkmlOv2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4cc339a3-e266-4cfd-ee15-fdede3026f31"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array({'those', 'about', 'whoever', 'until', 'these', 'others', 'however', 'many', 'eleven', 'is', 'out', 'give', 'were', 'meanwhile', 'therein', 'into', 'after', 'namely', 'part', 'enough', '’re', 'whereupon', 'beside', 'myself', 'or', 'formerly', 'always', 'though', '‘s', 'off', 'elsewhere', 'ca', 'most', 'thereafter', 'last', 'ours', 'but', 'beyond', 'whole', 'somehow', 'so', 'from', 'under', 'unless', \"'re\", 'here', '‘ve', 'other', 'see', 'below', 'whereafter', 'yourself', 'former', 'never', 'done', 'again', 'front', 'whence', 'am', 'through', 'side', 'did', 'made', 'none', 'must', 'being', 'mostly', 'there', 'afterwards', 'them', 'of', 'neither', '’ve', 'among', 'herself', 'whereby', 'nine', 'hereafter', 're', 'besides', 'his', 'seem', 'herein', 'top', 'used', 'throughout', 'i', 'and', 'not', 'together', 'because', 'a', 'should', 'fifty', 'same', 'mine', 'serious', 'now', 'him', 'which', 'no', 'keep', 'can', 'the', 'some', 'anyhow', 'show', 'why', 'yours', 'by', 'bottom', 'up', 'five', 'thereupon', 'you', 'hereby', 'ourselves', 'hundred', 'what', 'first', 'along', 'whereas', 'while', 'around', 'may', 'this', 'itself', 'onto', 'might', 'amongst', 'call', 'latter', 'sometime', 'whom', 'latterly', 'regarding', 'thus', '‘re', 'another', '’m', 'cannot', 'two', 'nor', '‘d', 'yourselves', 'who', 'my', 'else', 'becomes', 'across', 'n’t', '’s', 'name', 'she', 'me', 'wherever', '‘ll', 'still', 'at', 'say', 'our', 'everywhere', 'do', 'too', 'where', 'please', 'seeming', '’ll', 'whose', 'via', 'sometimes', 'since', 'very', \"'ve\", 'nevertheless', 'could', 'even', \"'ll\", 'full', 'that', 'once', 'been', 'your', 'further', 'before', 'put', 'an', 'any', 'beforehand', 'us', 'have', 'would', \"n't\", 'moreover', 'above', 'hence', 'are', 'various', 'within', 'became', 'her', 'than', 'sixty', 'himself', 'three', '‘m', 'nothing', 'per', 'often', 'back', 'nowhere', 'they', 'one', 'whether', 'hereupon', 'on', 'become', 'both', 'someone', 'seemed', 'several', 'its', 'rather', 'upon', '’d', 'fifteen', 'has', 'forty', \"'s\", 'twenty', 'between', 'all', 'we', 'to', 'noone', 'was', 'also', 'much', 'each', 'themselves', 'amount', 'due', 'least', 'during', 'therefore', 'how', 'only', 'it', 'really', 'move', 'down', 'go', 'almost', 'get', 'empty', 'somewhere', 'thence', 'eight', 'few', 'four', 'quite', 'behind', 'otherwise', 'be', 'next', 'towards', 'just', 'ten', 'already', 'make', 'whither', 'against', 'everyone', 'more', 'every', 'everything', 'as', 'without', 'thru', 'then', 'if', 'over', 'anyone', 'such', 'ever', 'hers', 'anywhere', 'whenever', 'will', 'for', 'whatever', 'third', 'does', \"'d\", 'except', 'wherein', 'anything', 'when', 'doing', 'alone', 'something', 'nobody', 'he', 'yet', 'anyway', 'their', 'had', 'six', 'own', 'using', 'twelve', 'becoming', 'indeed', 'perhaps', 'less', 'n‘t', 'take', 'seems', 'in', \"'m\", 'although', 'either', 'toward', 'with', 'thereby', 'well'},\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train['OriginalTweet'] = data_train['OriginalTweet'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
        "data_test['OriginalTweet'] = data_test['OriginalTweet'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))"
      ],
      "metadata": {
        "id": "FrwqHZ3amF9g"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove Urls and Mentions"
      ],
      "metadata": {
        "id": "Bll5iTm0rsy8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_train['OriginalTweet'] = data_train['OriginalTweet'].apply(lambda x: re.sub(r\"(?:\\@|https?\\://)\\S+\", \"\", x))\n",
        "data_test['OriginalTweet'] = data_test['OriginalTweet'].apply(lambda x: re.sub(r\"(?:\\@|https?\\://)\\S+\", \"\", x))"
      ],
      "metadata": {
        "id": "sKnCQaGQrvD2"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove Emoji"
      ],
      "metadata": {
        "id": "0swmsv6OtODC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_train['OriginalTweet'] = data_train['OriginalTweet'].apply(lambda x: re.sub(emoji.get_emoji_regexp(), r\"\", x))\n",
        "data_test['OriginalTweet'] = data_test['OriginalTweet'].apply(lambda x: re.sub(emoji.get_emoji_regexp(), r\"\", x))"
      ],
      "metadata": {
        "id": "1-hbpd0xtQAz"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove Hashtags"
      ],
      "metadata": {
        "id": "M5iczZ6VtyAC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#clean hashtags at the end of the sentence, and keep those in the middle of the sentence by removing just the # symbol\n",
        "\n",
        "data_train['OriginalTweet'] = data_train['OriginalTweet'].apply(lambda x: ' '.join(word.strip() for word in re.split('#(?!(?:hashtag)\\b)[\\w-]+(?=(?:\\s+#[\\w-]+)*\\s*$)', x)))\n",
        "data_test['OriginalTweet'] = data_test['OriginalTweet'].apply(lambda x: ' '.join(word.strip() for word in re.split('#(?!(?:hashtag)\\b)[\\w-]+(?=(?:\\s+#[\\w-]+)*\\s*$)', x)))\n",
        "\n",
        "data_train['OriginalTweet'] = data_train['OriginalTweet'].apply(lambda x: ' '.join(word.strip() for word in re.split('#|_', x)))\n",
        "data_test['OriginalTweet'] = data_test['OriginalTweet'].apply(lambda x: ' '.join(word.strip() for word in re.split('#|_', x)))"
      ],
      "metadata": {
        "id": "I0Xz8ywBtz4r"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove non utf8/ascii characters such as '\\x9a\\x91\\x97\\x9a\\x97'"
      ],
      "metadata": {
        "id": "H5Ed-WCG7-XM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_train['OriginalTweet'] = data_train['OriginalTweet'].apply(lambda x: re.sub(r'[^\\x00-\\x7f]',r'', x))\n",
        "data_test['OriginalTweet'] = data_test['OriginalTweet'].apply(lambda x: re.sub(r'[^\\x00-\\x7f]',r'', x))"
      ],
      "metadata": {
        "id": "TPin3Itq8Bif"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove Punctuations"
      ],
      "metadata": {
        "id": "J14mv9ZSnsa5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "string.punctuation"
      ],
      "metadata": {
        "id": "dfCJWU6SnPpJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4309a2c3-7a6a-4b10-abf2-c8087fb64860"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_punctuations(text):\n",
        "    for punctuation in string.punctuation + 'Ã'+'±'+'ã'+'¼'+'â'+'»'+'§':\n",
        "        text = text.replace(punctuation, '')\n",
        "    return text"
      ],
      "metadata": {
        "id": "e6k4IiKPon84"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_train['OriginalTweet'] = data_train['OriginalTweet'].apply(remove_punctuations)\n",
        "data_test['OriginalTweet'] = data_test['OriginalTweet'].apply(remove_punctuations)"
      ],
      "metadata": {
        "id": "XASsvdb0o5dm"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove Special Characters"
      ],
      "metadata": {
        "id": "NEnk1S3By7S6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_train['OriginalTweet'] = data_train['OriginalTweet'].apply(lambda x: x.replace('$', ''))\n",
        "data_test['OriginalTweet'] = data_test['OriginalTweet'].apply(lambda x: x.replace('$', ''))\n",
        "\n",
        "data_train['OriginalTweet'] = data_train['OriginalTweet'].apply(lambda x: x.replace('&', ''))\n",
        "data_test['OriginalTweet'] = data_test['OriginalTweet'].apply(lambda x: x.replace('&', ''))"
      ],
      "metadata": {
        "id": "6YDWv68iy9p-"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove multiple Spaces"
      ],
      "metadata": {
        "id": "Dddz7ILPyuwW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_train['OriginalTweet'] = data_train['OriginalTweet'].apply(lambda x: re.sub(\"\\s\\s+\" , \" \", x))\n",
        "data_test['OriginalTweet'] = data_test['OriginalTweet'].apply(lambda x: re.sub(\"\\s\\s+\" , \" \", x))"
      ],
      "metadata": {
        "id": "Xifn5Pf6xLbm"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove Empty Rows"
      ],
      "metadata": {
        "id": "84DIbT8m7Ch3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(data_train[data_train['OriginalTweet'] == ''])"
      ],
      "metadata": {
        "id": "2jgL6_yB55Oc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "053256bc-cd1e-4285-d079-a42a1f912f60"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(data_test[data_test['OriginalTweet'] == ''])"
      ],
      "metadata": {
        "id": "mT8ZQkcD6XcW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc5cf412-1180-487b-afe9-4acdbb64aecd"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train.shape, data_test.shape"
      ],
      "metadata": {
        "id": "-4HXJ4Mh9cPm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16413349-0ad5-4e8e-c2dd-c61bc72a089f"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((41157, 6), (3798, 6))"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = data_train[data_train['OriginalTweet'] != '']\n",
        "test = data_test[data_test['OriginalTweet'] != '']"
      ],
      "metadata": {
        "id": "N0INDpJr6aWW"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape, test.shape"
      ],
      "metadata": {
        "id": "JDOABunUPMUK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2153b76-a3e7-4574-b83c-30b50526b598"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((41134, 6), (3796, 6))"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = train['OriginalTweet']\n",
        "y_train = train['Sentiment']\n",
        "X_test = test['OriginalTweet']\n",
        "y_test = test['Sentiment']"
      ],
      "metadata": {
        "id": "WLQrT_nxPx7Y"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mask = np.array(Image.open('/content/twitter_logo.png'))\n",
        "\n",
        "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=[31, 16])\n",
        "\n",
        "wordcloud1 = WordCloud(width = 800, height = 800, \n",
        "                background_color = 'black',\n",
        "                colormap = 'Set2', \n",
        "                min_font_size = 10,\n",
        "                mask = mask).generate(' '.join(train[train['Sentiment'] == 'Positive']['OriginalTweet'])) \n",
        "\n",
        "ax1.imshow(wordcloud1)\n",
        "ax1.axis('off')\n",
        "ax1.set_title('Positive Sentiment',fontsize=35)\n",
        "\n",
        "wordcloud2 = WordCloud(width = 800, height = 800, \n",
        "                background_color = 'black',\n",
        "                colormap = 'Set2', \n",
        "                min_font_size = 10,\n",
        "                mask = mask).generate(' '.join(train[train['Sentiment'] == 'Negative']['OriginalTweet'])) \n",
        "\n",
        "ax2.imshow(wordcloud2)\n",
        "ax2.axis('off')\n",
        "ax2.set_title('Negative Sentiment',fontsize=35)\n",
        "\n",
        "wordcloud3 = WordCloud(width = 800, height = 800, \n",
        "                background_color = 'black',\n",
        "                colormap = 'Set2', \n",
        "                min_font_size = 10,\n",
        "                mask = mask).generate(' '.join(train[train['Sentiment'] == 'Neutral']['OriginalTweet'])) \n",
        "\n",
        "ax3.imshow(wordcloud3)\n",
        "ax3.axis('off')\n",
        "ax3.set_title('Neutral Sentiment',fontsize=35);  "
      ],
      "metadata": {
        "id": "QB5dXJY5QEFM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "b13d9144-bae3-444f-8201-9478d22f9ce5"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2232x1152 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_corpus(target):\n",
        "    corpus = [] \n",
        "    for x in data_train[data_train['Sentiment']==target]['OriginalTweet'].str.split():\n",
        "        for i in x:\n",
        "            corpus.append(i)\n",
        "    return corpus"
      ],
      "metadata": {
        "id": "97ub1u3Yd6jw"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "counter_p = Counter(create_corpus('Positive'))\n",
        "common_p = counter_p.most_common()\n",
        "\n",
        "x_p=[]\n",
        "y_p=[]\n",
        "for word,count in common_p[:10]:\n",
        "  x_p.append(word)\n",
        "  y_p.append(count)\n",
        "\n",
        "counter_neg = Counter(create_corpus('Negative'))\n",
        "common_neg = counter_neg.most_common()\n",
        "\n",
        "x_neg=[]\n",
        "y_neg=[]\n",
        "for word,count in common_neg[:10]:\n",
        "  x_neg.append(word)\n",
        "  y_neg.append(count)\n",
        "\n",
        "counter_neu = Counter(create_corpus('Neutral'))\n",
        "common_neu = counter_neu.most_common()\n",
        "\n",
        "x_neu=[]\n",
        "y_neu=[]\n",
        "for word,count in common_neu[:10]:\n",
        "  x_neu.append(word)\n",
        "  y_neu.append(count)     "
      ],
      "metadata": {
        "id": "5mcSXXcCeNNB"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = make_subplots(rows=1, cols=3)\n",
        "\n",
        "fig.add_trace(go.Bar(x = y_p[::-1], y = x_p[::-1], name='Positive', marker_color='#cc0000', orientation='h', \n",
        "                     text=y_p[::-1], textposition='auto') , 1, 1)\n",
        "fig.add_trace(go.Bar(x = y_neg[::-1], y = x_neg[::-1], name='Negative', marker_color='#80ff00', orientation='h', \n",
        "                     text=y_neg[::-1], textposition='auto') , 1, 2)\n",
        "fig.add_trace(go.Bar(x = y_neu[::-1], y = x_neu[::-1], name='Neutral', marker_color='#00b8e6', orientation='h', \n",
        "                     text=y_neu[::-1], textposition='auto') , 1, 3)\n",
        "\n",
        "fig.update_layout(title_text='<b>Common Words in Tweets After Cleaning<b>')\n",
        "iplot(fig)"
      ],
      "metadata": {
        "id": "AWb5PXc4ZKhO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "bb424e10-8316-4020-877b-58cbf4eb0c5c"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.plotly.v1+json": {
              "data": [
                {
                  "marker": {
                    "color": "#cc0000"
                  },
                  "name": "Positive",
                  "orientation": "h",
                  "text": [
                    "2083",
                    "2256",
                    "2544",
                    "2722",
                    "2918",
                    "2953",
                    "2959",
                    "3220",
                    "3278",
                    "4359"
                  ],
                  "textposition": "auto",
                  "x": [
                    2083,
                    2256,
                    2544,
                    2722,
                    2918,
                    2953,
                    2959,
                    3220,
                    3278,
                    4359
                  ],
                  "xaxis": "x",
                  "y": [
                    "19",
                    "people",
                    "amp",
                    "food",
                    "supermarket",
                    "prices",
                    "grocery",
                    "coronavirus",
                    "store",
                    "covid19"
                  ],
                  "yaxis": "y",
                  "type": "bar"
                },
                {
                  "marker": {
                    "color": "#80ff00"
                  },
                  "name": "Negative",
                  "orientation": "h",
                  "text": [
                    "1904",
                    "1959",
                    "2040",
                    "2183",
                    "2611",
                    "2624",
                    "3515",
                    "3526",
                    "3701",
                    "3770"
                  ],
                  "textposition": "auto",
                  "x": [
                    1904,
                    1959,
                    2040,
                    2183,
                    2611,
                    2624,
                    3515,
                    3526,
                    3701,
                    3770
                  ],
                  "xaxis": "x2",
                  "y": [
                    "amp",
                    "panic",
                    "grocery",
                    "store",
                    "supermarket",
                    "people",
                    "coronavirus",
                    "food",
                    "prices",
                    "covid19"
                  ],
                  "yaxis": "y2",
                  "type": "bar"
                },
                {
                  "marker": {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Models**"
      ],
      "metadata": {
        "id": "OUXeMMzm5xR4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#HyperParameters\n",
        "vocab_size = 1000000\n",
        "max_length = 100\n",
        "embedding_dim = 64\n",
        "trunc_type = 'post'\n",
        "oov_tok = '<OOV>'\n",
        "\n",
        "#Initialize the Tokenizer class\n",
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token = oov_tok)\n",
        "\n",
        "#Generate the word index dictionary for the training sentences\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "#Generate and pad the training sequences\n",
        "sequences = tokenizer.texts_to_sequences(X_train)\n",
        "padded = pad_sequences(sequences, maxlen=max_length, truncating=trunc_type)\n",
        "\n",
        "#Generate and pad the test sequences\n",
        "testing_sequences = tokenizer.texts_to_sequences(X_test)\n",
        "testing_padded = pad_sequences(testing_sequences, maxlen=max_length, truncating=trunc_type)\n",
        "\n",
        "y_train = y_train.map({'Negative':0, 'Neutral':1, 'Positive':2})\n",
        "y_test = y_test.map({'Negative':0, 'Neutral':1, 'Positive':2})\n",
        "\n",
        "training_padded = np.array(padded)\n",
        "training_labels = np.array(y_train)\n",
        "testing_padded = np.array(testing_padded)\n",
        "testing_labels = np.array(y_test)"
      ],
      "metadata": {
        "id": "5SZg7Ima50Oa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model 1**"
      ],
      "metadata": {
        "id": "GE18axI0mja_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.GlobalAveragePooling1D(), # or use flatten()\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "# Print the model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nX-s9qNIBwCe",
        "outputId": "30bd9bae-dfc9-4e95-f130-588fc669ea3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_2 (Embedding)     (None, 100, 100)          200000000 \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 100)              0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 64)                6464      \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 3)                 195       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 200,006,659\n",
            "Trainable params: 200,006,659\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "VlohHKOfHGcC",
        "outputId": "1a21363f-b41d-4ee1-9742-cd6387ff086e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filepath = 'my_best_model.epoch{epoch:02d}-loss{val_accuracy:.2f}.hdf5'\n",
        "checkpoint = ModelCheckpoint(filepath='DNN_Best_Model', monitor='val_accuracy', verbose=1, \n",
        "                             save_best_only=True, mode='max', save_freq='epoch')\n",
        "callbacks = [checkpoint]"
      ],
      "metadata": {
        "id": "Sh5ZiSavsrpP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Compile the model\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), \n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "1fFwQI7vCQ9Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(training_padded, training_labels, epochs = 16, validation_data=(testing_padded, testing_labels), \n",
        "                    verbose = 1, callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LTde7NzECT6S",
        "outputId": "993f7001-25ea-4db0-a8d1-f2eb3f174989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 1.0526 - accuracy: 0.4386\n",
            "Epoch 1: val_accuracy improved from -inf to 0.40727, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 57s 40ms/step - loss: 1.0526 - accuracy: 0.4387 - val_loss: 1.0334 - val_accuracy: 0.4073\n",
            "Epoch 2/16\n",
            "1286/1286 [==============================] - ETA: 0s - loss: 1.0347 - accuracy: 0.4408\n",
            "Epoch 2: val_accuracy improved from 0.40727 to 0.41834, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 50s 39ms/step - loss: 1.0347 - accuracy: 0.4408 - val_loss: 1.0155 - val_accuracy: 0.4183\n",
            "Epoch 3/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.9970 - accuracy: 0.5324\n",
            "Epoch 3: val_accuracy improved from 0.41834 to 0.59932, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 49s 38ms/step - loss: 0.9969 - accuracy: 0.5325 - val_loss: 0.9615 - val_accuracy: 0.5993\n",
            "Epoch 4/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.9186 - accuracy: 0.6322\n",
            "Epoch 4: val_accuracy improved from 0.59932 to 0.62540, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 49s 38ms/step - loss: 0.9186 - accuracy: 0.6323 - val_loss: 0.8873 - val_accuracy: 0.6254\n",
            "Epoch 5/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.8293 - accuracy: 0.6642\n",
            "Epoch 5: val_accuracy improved from 0.62540 to 0.65411, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 49s 38ms/step - loss: 0.8293 - accuracy: 0.6642 - val_loss: 0.8162 - val_accuracy: 0.6541\n",
            "Epoch 6/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.7496 - accuracy: 0.6877\n",
            "Epoch 6: val_accuracy improved from 0.65411 to 0.67756, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 49s 38ms/step - loss: 0.7495 - accuracy: 0.6878 - val_loss: 0.7572 - val_accuracy: 0.6776\n",
            "Epoch 7/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.6786 - accuracy: 0.7203\n",
            "Epoch 7: val_accuracy improved from 0.67756 to 0.71259, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 49s 38ms/step - loss: 0.6786 - accuracy: 0.7202 - val_loss: 0.7100 - val_accuracy: 0.7126\n",
            "Epoch 8/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.6168 - accuracy: 0.7749\n",
            "Epoch 8: val_accuracy improved from 0.71259 to 0.73656, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 49s 38ms/step - loss: 0.6167 - accuracy: 0.7749 - val_loss: 0.6731 - val_accuracy: 0.7366\n",
            "Epoch 9/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.5640 - accuracy: 0.8086\n",
            "Epoch 9: val_accuracy improved from 0.73656 to 0.74552, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 49s 38ms/step - loss: 0.5640 - accuracy: 0.8086 - val_loss: 0.6469 - val_accuracy: 0.7455\n",
            "Epoch 10/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.5196 - accuracy: 0.8293\n",
            "Epoch 10: val_accuracy improved from 0.74552 to 0.75501, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 49s 38ms/step - loss: 0.5196 - accuracy: 0.8292 - val_loss: 0.6313 - val_accuracy: 0.7550\n",
            "Epoch 11/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.4825 - accuracy: 0.8435\n",
            "Epoch 11: val_accuracy improved from 0.75501 to 0.76185, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 49s 38ms/step - loss: 0.4825 - accuracy: 0.8435 - val_loss: 0.6139 - val_accuracy: 0.7619\n",
            "Epoch 12/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.4502 - accuracy: 0.8553\n",
            "Epoch 12: val_accuracy improved from 0.76185 to 0.76607, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 50s 39ms/step - loss: 0.4502 - accuracy: 0.8553 - val_loss: 0.6019 - val_accuracy: 0.7661\n",
            "Epoch 13/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.4228 - accuracy: 0.8663\n",
            "Epoch 13: val_accuracy improved from 0.76607 to 0.76976, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 50s 39ms/step - loss: 0.4228 - accuracy: 0.8662 - val_loss: 0.6010 - val_accuracy: 0.7698\n",
            "Epoch 14/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.3981 - accuracy: 0.8754\n",
            "Epoch 14: val_accuracy improved from 0.76976 to 0.77424, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 50s 39ms/step - loss: 0.3980 - accuracy: 0.8754 - val_loss: 0.5887 - val_accuracy: 0.7742\n",
            "Epoch 15/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.3757 - accuracy: 0.8839\n",
            "Epoch 15: val_accuracy improved from 0.77424 to 0.77503, saving model to DNN_Best_Model\n",
            "INFO:tensorflow:Assets written to: DNN_Best_Model/assets\n",
            "1286/1286 [==============================] - 50s 39ms/step - loss: 0.3758 - accuracy: 0.8839 - val_loss: 0.5976 - val_accuracy: 0.7750\n",
            "Epoch 16/16\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.3559 - accuracy: 0.8913\n",
            "Epoch 16: val_accuracy did not improve from 0.77503\n",
            "1286/1286 [==============================] - 46s 36ms/step - loss: 0.3558 - accuracy: 0.8914 - val_loss: 0.5859 - val_accuracy: 0.7748\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "acc      = history.history['accuracy']\n",
        "val_acc  = history.history['val_accuracy']\n",
        "loss     = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs   = range(len(acc)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot  (epochs, acc, 'bo', color = '#ff0066')\n",
        "plt.plot  (epochs, val_acc, color = '#00ccff')\n",
        "plt.title ('Training and validation accuracy')\n",
        "plt.legend(['Train', 'Test'], loc = 'upper left')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.figure()\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot  (epochs, loss, 'bo', color = '#ff0066')\n",
        "plt.plot  (epochs, val_loss, color = '#00ccff')\n",
        "plt.legend(['Train', 'Test'], loc = 'upper left')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.title ('Train and Test Loss');"
      ],
      "metadata": {
        "id": "Mgj0qwH7EVZh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "53a2aa63-a5ec-4588-d522-f4a358e8c726"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_DNN_model = tf.keras.models.load_model('DNN_Best_Model')"
      ],
      "metadata": {
        "id": "24ukIeeYAs5Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_DNN_model.evaluate(testing_padded, testing_labels, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYPFBNDQAsOZ",
        "outputId": "c9a5a907-b635-44ac-900a-7273b8793826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "119/119 [==============================] - 0s 2ms/step - loss: 0.5976 - accuracy: 0.7750\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5976424217224121, 0.7750263214111328]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = best_DNN_model.predict(testing_padded)\n",
        "y_pred = np.argmax(predictions, axis = 1)"
      ],
      "metadata": {
        "id": "akN-VTz9BZ0A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = ['Negative', 'Neutral', 'Positive']\n",
        "print(classification_report(testing_labels, y_pred, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9Mllmg-Cu7s",
        "outputId": "3cd1b0d7-d6f5-4702-c959-bb500aa74207"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.84      0.74      0.79      1633\n",
            "     Neutral       0.62      0.64      0.63       617\n",
            "    Positive       0.78      0.86      0.82      1546\n",
            "\n",
            "    accuracy                           0.78      3796\n",
            "   macro avg       0.75      0.75      0.75      3796\n",
            "weighted avg       0.78      0.78      0.77      3796\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(testing_labels, y_pred)\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "YtZ4NxSZF2zT",
        "outputId": "7d50e40e-f827-4c8c-93d1-e680b58fdd27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model 2: LSTM**"
      ],
      "metadata": {
        "id": "wDxSaPeRm5g_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameters\n",
        "lstm1_dim = 64\n",
        "lstm2_dim = 32\n",
        "dense_dim = 64\n",
        "\n",
        "# Build the model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(lstm1_dim, return_sequences=True)),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(lstm2_dim)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(dense_dim, activation='relu'),\n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NQ4sgKozL-13",
        "outputId": "5abdff35-2cfa-4352-ade2-3cacca55c153"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_3 (Embedding)     (None, None, 100)         200000000 \n",
            "                                                                 \n",
            " bidirectional_4 (Bidirectio  (None, None, 128)        84480     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " bidirectional_5 (Bidirectio  (None, 64)               41216     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 3)                 195       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 200,130,051\n",
            "Trainable params: 200,130,051\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "f2I3YGPTHgFZ",
        "outputId": "c6744a6c-682b-4d7c-9aa1-e4531fee098e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filepath = 'my_best_model.epoch{epoch:02d}-loss{val_accuracy:.2f}.hdf5'\n",
        "checkpoint = ModelCheckpoint(filepath='LSTM_Best_Model', monitor='val_accuracy', verbose=1, \n",
        "                             save_best_only=True, mode='max', save_freq='epoch')\n",
        "callbacks = [checkpoint]"
      ],
      "metadata": {
        "id": "RbGDr3uUHyAW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Compile the model\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), \n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "U35jHkOenK0m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(training_padded, training_labels, epochs = 5, validation_data=(testing_padded, testing_labels), \n",
        "                    verbose = 1, callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INxXM3A2nYBc",
        "outputId": "4d11092a-2210-4578-9120-27e106253be4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1286/1286 [==============================] - ETA: 0s - loss: 0.6065 - accuracy: 0.7524\n",
            "Epoch 1: val_accuracy improved from -inf to 0.83061, saving model to LSTM_Best_Model\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_13_layer_call_fn, lstm_cell_13_layer_call_and_return_conditional_losses, lstm_cell_14_layer_call_fn, lstm_cell_14_layer_call_and_return_conditional_losses, lstm_cell_16_layer_call_fn while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: LSTM_Best_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: LSTM_Best_Model/assets\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fe993eea1d0> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fe98d945b50> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fe98dcd7890> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fe98e0e0d10> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1286/1286 [==============================] - 104s 77ms/step - loss: 0.6065 - accuracy: 0.7524 - val_loss: 0.4667 - val_accuracy: 0.8306\n",
            "Epoch 2/5\n",
            "1286/1286 [==============================] - ETA: 0s - loss: 0.3170 - accuracy: 0.8973\n",
            "Epoch 2: val_accuracy improved from 0.83061 to 0.83483, saving model to LSTM_Best_Model\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_13_layer_call_fn, lstm_cell_13_layer_call_and_return_conditional_losses, lstm_cell_14_layer_call_fn, lstm_cell_14_layer_call_and_return_conditional_losses, lstm_cell_16_layer_call_fn while saving (showing 5 of 8). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: LSTM_Best_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: LSTM_Best_Model/assets\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fe993eea1d0> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fe98d945b50> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fe98dcd7890> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fe98e0e0d10> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1286/1286 [==============================] - 96s 75ms/step - loss: 0.3170 - accuracy: 0.8973 - val_loss: 0.4928 - val_accuracy: 0.8348\n",
            "Epoch 3/5\n",
            "1286/1286 [==============================] - ETA: 0s - loss: 0.2153 - accuracy: 0.9328\n",
            "Epoch 3: val_accuracy did not improve from 0.83483\n",
            "1286/1286 [==============================] - 67s 52ms/step - loss: 0.2153 - accuracy: 0.9328 - val_loss: 0.5586 - val_accuracy: 0.8222\n",
            "Epoch 4/5\n",
            "1286/1286 [==============================] - ETA: 0s - loss: 0.1489 - accuracy: 0.9526\n",
            "Epoch 4: val_accuracy did not improve from 0.83483\n",
            "1286/1286 [==============================] - 67s 52ms/step - loss: 0.1489 - accuracy: 0.9526 - val_loss: 0.6227 - val_accuracy: 0.8145\n",
            "Epoch 5/5\n",
            "1285/1286 [============================>.] - ETA: 0s - loss: 0.1065 - accuracy: 0.9660\n",
            "Epoch 5: val_accuracy did not improve from 0.83483\n",
            "1286/1286 [==============================] - 67s 52ms/step - loss: 0.1065 - accuracy: 0.9660 - val_loss: 0.7465 - val_accuracy: 0.8048\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "acc      = history.history['accuracy']\n",
        "val_acc  = history.history['val_accuracy']\n",
        "loss     = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs   = range(len(acc)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot  (epochs, acc, 'bo', color = '#ff0066')\n",
        "plt.plot  (epochs, val_acc, color = '#00ccff')\n",
        "plt.title ('Training and validation accuracy')\n",
        "plt.legend(['Train', 'Test'], loc = 'upper left')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.figure()\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot  (epochs, loss, 'bo', color = '#ff0066')\n",
        "plt.plot  (epochs, val_loss, color = '#00ccff')\n",
        "plt.legend(['Train', 'Test'], loc = 'upper left')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.title ('Train and Test Loss');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "s7fh2rOrH98m",
        "outputId": "5a2bc495-2724-490c-d1e3-7dd0f1381afd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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McjlBmJlZLicIMzPL5QRhZma5nCDMzCyXE4SZmeVygjAzs1xOEGZmlssJwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCyXE4SZmeVygjAzs1wVTRCSTpS0RNJSSV/MWT9J0l2SHpO0UFJzZt0Zkv6YPs6oZJxmZra7iiUISbXAFcC7gWnA6ZKmFRX7FvDjiDgMuBj4errtKOCrwEzgSOCrkvapVKxmZra7StYgjgSWRsRzEbEFuA44pajMNODudPqezPoTgN9ExKqIeA34DXBiBWM1M7MilUwQ44Flmfm2dFnW74HT0un3AyMkjS5xWySdKalVUmt7e3vZArcuzF8Eky+Amk8kz/MXVTsiM6uQandSnwccI+kR4BhgOdBR6sYRMS8iWiKiZezYsZWK0QrmL4Izr4EXVkGQPJ95jZOE2QBVyQSxHJiQmW9Ol+0QES9FxGkRMQO4MF22upRtrQouvBk2bNl12YYtyXIzG3AqmSAeAqZKmiKpAZgFLMgWkDRGUiGGC4Cr0ulfAX8jaZ+0c/pv0mVWTS+u6tlyM+vXKpYgImIb8GmSL/angRsi4klJF0t6X1rsWGCJpD8A+wGXpNuuAv6VJMk8BFycLrNqmjiqZ8vNrF9TRFQ7hrJoaWmJ1tbWaocxsBX6ILLNTEMbYN4cmD2zenGZ2R6TtDgiWvLWVbuT2vqT2TOTZDBpFIjk2cnBbMCqq3YA1s/MnumEYDZIuAZhZma5nCDMzCyXE4SZmeVygjAzs1xOEGZmlssJwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCxXtwlC0nszQ3KbmdkgUcoX/4eAP0r6pqQ3VzogMzPrG7pNEBExB5gBPAtcLemB9F7QIyoenZmZVU1JTUcRsRa4EbgO2B94P/CwpHMqGJuZmVVRKX0Q75N0E7AQqAeOjIh3A4cDn6tseGZmVi2l3A/iA8BlEXFvdmFEbJD08cqEZWZm1VZKgrgIWFGYkTQE2C8ino+IuyoVmJmZVVcpfRA/A7Zn5jvSZWZmNoCVkiDqImLHXerT6YbKhWRmZn1BKQmiXdL7CjOSTgFerVxIZmbWF5TSB3EWMF/SdwABy4CPVjQqMzOrum4TREQ8C7xN0vB0fn3FozIzs6orpQaBpJOAQ4EmSQBExMUVjMvMzKqslAvlriQZj+kckiamDwKTKhyXmZlVWSmd1EdHxEeB1yLiX4CjgIMqG5aZmVVbKQliU/q8QdIBwFaS8ZjMzGwAK6UP4hZJI4H/BzwMBPD9ikZlZmZV12WCSG8UdFdErAZ+LulWoCki1vRKdGZmVjVdNjFFxHbgisz8ZicHM7PBoZQ+iLskfUCF81vNzGxQKCVBfIJkcL7NktZKWidpbYXjMjOzKivlSmrfWtTMbBDqNkFIenve8uIbCJmZ2cBSymmun89MNwFHAouBd1QkIjMz6xNKaWJ6b3Ze0gTg2xWLyMzM+oRSOqmLtQGHlFJQ0omSlkhaKumLOesnSrpH0iOSHpP0nnT5ZEkbJT2aPq7cgzjNzOwNKKUP4j9Irp6GJKFMJ7miurvtakmuoTieJKk8JGlBRDyVKfZl4IaI+K6kacDtwOR03bMRMb3UN2JmZuVVSh9Ea2Z6G/DTiPjfErY7ElgaEc8BSLoOOAXIJogA9kqn9wZeKmG/ZmbWC0pJEDcCmyKiA5KagaShEbGhm+3Gk9x9rqANmFlU5iLg15LOAYYB78qsmyLpEWAt8OWIuK+EWM3MrExKupIaGJKZHwLcWabXPx24OiKagfcAP0nHf1oBTIyIGcA/AddK2qt4Y0lnSmqV1Nre3l6mkMzMDEpLEE3Z24ym00NL2G45MCEz35wuy/o4cEO63wdITqMdk475tDJdvhh4lpx7UETEvIhoiYiWsWPHlhCSmZmVqpQE8bqktxZmJB0BbCxhu4eAqZKmSGoAZgELisq8CLwz3e8hJAmiXdLYtJMbSQcCU4HnSnhNMzMrk1L6ID4D/EzSSyS3HB1HcgvSLkXENkmfBn4F1AJXRcSTki4GWiNiAfA54PuSPkvSYT03IiK9evtiSVuB7cBZEbFqT96gmZntGUVE94WkeuDgdHZJRGytaFR7oKWlJVpbW7svaGZmO0haHBEteeu6bWKS9ClgWEQ8ERFPAMMlfbLcQZqZWd9SSh/EP6R3lAMgIl4D/qFyIZmZWV9QSoKozd4sKO08bqhcSGZm1heU0kn9S+B6Sd9L5z8B3FG5kMzMrC8oJUF8ATgTOCudf4zkTCYzMxvAum1iiojtwCLgeZLxld4BPF3ZsMzMrNo6rUFIOohkKIzTgVeB6wEi4rjeCc3MzKqpqyamZ4D7gJMjYilAekGbDVIdAes6YP12GFMHTXtyNxEz6ze6ShCnkQyPcY+kXwLXkVxJbf1EBGxKv9TXdsC67cn0jud0em1H0fJO1m/MXFMpYHIDvLkJDk4fhelxdSD/p5j1e50miIi4GbhZ0jCS+zh8BthX0neBmyLi170U46BS+JVe/GW9NueLu5T1HSW+7tAaGFEDI2p3Ph/QAHsVlmWWD6uBFVvhmU2wZBMsXLdr8tirZvekcXATTG10rcOsPynlntSvA9eSDLm9D/BBkjObnCDY9Vd67q/xzr7EO1m/sfuRT4DkD1f8xb1XDYyv37lsr6L1I4q+7Avrh9dA7Rv4xb89oG1rkiwKSeOZNHFckxlBy7UOs/6lpLGY+oM9HYtpy3Z4ZGPpTS5ry/Arvbsv7hG1aZku1jepf3yhru+AP2xOkkY2gSzZ1HmtI5s4XOswq6yuxmIq5TqIAe21DnjbM/nr8n6l710LzfWdf3HnJoAy/Ervr4bXwluHJo+svFrHki5qHdmk4VqHWe8Y9AliVB3c9qb8X+6N/eRXen9UI5jYkDyOL7pXYGe1jnvXw4btO8u51mFWWYM+QdQL3rN3taOwrErUOgrTrnWYlW7QJwjrP7qrdfxx866d5Hm1jhE1+c1VrnWY7c4JwgaE4bUwY2jyyMrWOrKJo6taR3ECca3DBisnCBvQuqp1vJ72dfS01lGYdq3DBjonCBu0hvWw1vHbEmsdBzfC/vWudVj/5wRhVqQntY5CAums1lFIHJMaYFw97FeXPI+pS17HrC9zgjDrgZ7WOu5dB/NX7b6fWmDfTMLY8aiD/Yrm9651bcSqwwnCrAy6q3W8tBVe3govb0ueXymaf3xjsmxbzr4blSaNomRSnFz2q0sSmFm5OEGYVdiwWphaC1Obui63PZIr+19Ok8cr23ZOFx7Pb4HfvQ7t2yBvkJzhNV3XRsbVJ8v2q4MGd7BbN5wgzPqIGsHouuRx6JCuy26LJEnk1UYK809sgjvXwepOBgsbVdt5baS4v2QwDhNjThBm/VKdkjOl9q/vvuym7fDnotpIce1k0etJgsl2tBfUAPt2URvJzo90f8mA4gRhNsA11ezsH+nO+o6u+0pe2QpPbUzmt+a0cTWo69pIdn64+0v6PCcIM9theC28qRbe1E25yPSXFNdGCvMvboEH0/6SnIoJw2o6r42MTxPapIZk4EyrDicIM+sxKRkJeVQdTOumbEfAq9kkklM7eWoT3LMOVuX0l4ys3ZksJjbsPr1/va8pqRQnCDOrqNr0NN396uHwbspu3p7UQJZvSWogL6TPhen71u/e6V6v5B4tuySQxp3TExuSm3VZzzlBmFmf0ZjpLzmqkzJrO3YmjeIkcs86WL519yatMXVd10L29YCMuZwgzKxf2asW/nJI8sizLZILE1/YvHsCWbIJfr0WXi/KII3KTxyF6QkNSfIabJwgzGxAqVPXZ21FJM1Uxc1Xhek71sKKrbtvN65u9yasbDIZNQBP8XWCMLNBRYJ96pLH9KH5ZTZvT5qqdkkiaY3k8Y1w6xrYVHSa77Carmsh4xuS/pL+xAnCzKxIYw0c2Jg88kR6ZlZeR/qLW+DhDcnpvVk1wAH1nSSRtEaydx87pdcJwsyshyQYW588jhiWX2bjdljWSQJ5aAP892rYUlQL2asmv/mqML1/fe8Oe+IEYWZWAUNq4KCm5JFneySn9Gabr7JJ5IH1u18XUgc05ySOg5vgmBHlfw9OEGZmVVCTGU9rZie1kHUdSS2ks2tCfroFOoCjhsH9by5/jE4QZmZ91IhamDYkeeTpSE/pzRtksRwqemavpBMlLZG0VNIXc9ZPlHSPpEckPSbpPZl1F6TbLZF0QiXjNDPrj2qVXKNxcDf3GtlTFatBSKoFrgCOB9qAhyQtiIinMsW+DNwQEd+VNA24HZicTs8CDgUOAO6UdFBEdDKyvZmZlVslaxBHAksj4rmI2AJcB5xSVCaAwg0a9wZeSqdPAa6LiM0R8Sdgabo/MzPrJZVMEOOBZZn5tnRZ1kXAHEltJLWHc3qwLZLOlNQqqbW9vb1ccZuZGRXugyjB6cDVEdEMvAf4iaSSY4qIeRHREhEtY8eOrViQZmaDUSXPYloOTMjMN6fLsj4OnAgQEQ9IagLGlLitmZlVUCVrEA8BUyVNkdRA0um8oKjMi8A7ASQdAjQB7Wm5WZIaJU0BpgIPVjBWMzMrUrEaRERsk/Rp4FdALXBVRDwp6WKgNSIWAJ8Dvi/psyQd1nMjIoAnJd0APAVsAz7lM5jMzHqXku/j/q+lpSVaW1urHYaZWb8iaXFEtOStq3YntZmZ9VFOEGZmlssJwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCyXE4SZmeVygjAzs1xOEGZmlssJwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCyXE4SZmeVygjAzs1xOEGZmlssJwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCyXE4SZmeVygjAzs1xOEGZmlssJwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCyXE4SZmeVygjAzs1xOEGZmlssJwszMcjlBzF8Eky+Amk8kz/MXVTsiM7M+oa7aAVTV/EVw5jWwYUsy/8KqZB5g9szqxWVm1gdUtAYh6URJSyQtlfTFnPWXSXo0ffxB0urMuo7MugUVCfDCm3cmh4INW5LlZmaDXMVqEJJqgSuA44E24CFJCyLiqUKZiPhspvw5wIzMLjZGxPRKxQfAi6t6ttzMbBCpZA3iSGBpRDwXEVuA64BTuih/OvDTCsazu4mjerbczGwQqWSCGA8sy8y3pct2I2kSMAW4O7O4SVKrpN9JOrWT7c5My7S2t7f3PMJLToWhDbsuG9qQLDczG+T6yllMs4AbI6Ijs2xSRLQAHwa+LekvijeKiHkR0RIRLWPHju35q86eCfPmwKRRIJLneXPcQW1mRmXPYloOTMjMN6fL8swCPpVdEBHL0+fnJC0k6Z94tuxRzp7phGBmlqOSNYiHgKmSpkhqIEkCu52NJOnNwD7AA5ll+0hqTKfHAH8FPFW8rZmZVU7FahARsU3Sp4FfAbXAVRHxpKSLgdaIKCSLWcB1ERGZzQ8BvidpO0kSuzR79pOZmVWedv1e7r9aWlqitbW12mGYmfUrkhan/b276Sud1GZm1sc4QZiZWa4B08QkqR144Q3sYgzwapnCKSfH1TOOq2ccV88MxLgmRUTudQIDJkG8UZJaO2uHqybH1TOOq2ccV88MtrjcxGRmZrmcIMzMLJcTxE7zqh1AJxxXzziunnFcPTOo4nIfhJmZ5XINwszMcjlBmJlZrkGVIEq4BWqjpOvT9YskTe4jcc2V1J65Bevf91JcV0n6s6QnOlkvSZencT8m6a19JK5jJa3JHK9/7qW4Jki6R9JTkp6U9I85ZXr9mJUYV68fM0lNkh6U9Ps0rn/JKdPrn8kS46rKZzJ97VpJj0i6NWddeY9XRAyKB8mAgc8CBwINwO+BaUVlPglcmU7PAq7vI3HNBb5ThWP2duCtwBOdrH8PcAfJ3TTeBizqI3EdC9xaheO1P/DWdHoE8Iecv2WvH7MS4+r1Y5Yeg+HpdD2wCHhbUZlqfCZLiasqn8n0tf8JuDbv71Xu4zWYahCl3AL1FOBH6fSNwDslqQ/EVRURcS/Q1Q26TwF+HInfASMl7d8H4qqKiFgREQ+n0+uAp9n9Loq9fsxKjKvXpcdgfTpbnz6Kz5rp9c9kiXFVhaRm4CTgvzopUtbjNZgSRBxlpS8AAAO/SURBVCm3QN1RJiK2AWuA0X0gLoAPpE0SN0qakLO+Gkq+rWwVHJU2Edwh6dDefvG0aj+D5NdnVlWPWRdxQRWOWdpc8ijwZ+A3EdHp8erFz2QpcUF1PpPfBs4HtneyvqzHazAliP7sFmByRBwG/IadvxAs38Mk48scDvwHcHNvvrik4cDPgc9ExNrefO2udBNXVY5ZRHRExHSSO04eKekve+N1u1NCXL3+mZR0MvDniFhc6dcqGEwJopRboO4oI6kO2BtYWe24ImJlRGxOZ/8LOKLCMZWqJ7eV7TURsbbQRBARtwP1Su5MWHGS6km+hOdHxH/nFKnKMesurmoes/Q1VwP3ACcWrarGZ7LbuKr0mfwr4H2Snidpin6HpGuKypT1eA2mBFHKLVAXAGek038L3B1pb0814ypqo34fSRtyX7AA+Gh6Zs7bgDURsaLaQUkaV2h3lXQkyf95xb9U0tf8AfB0RPx7J8V6/ZiVElc1jpmksZJGptNDgOOBZ4qK9fpnspS4qvGZjIgLIqI5IiaTfE/cHRFzioqV9XhV7JajfU2UdgvUHwA/kbSUpBN0Vh+J61xJ7wO2pXHNrXRcAJJ+SnJ2yxhJbcBXSTrsiIgrgdtJzspZCmwA/q6PxPW3wNmStgEbgVm9kOgh+YX3EeDxtP0a4EvAxExs1ThmpcRVjWO2P/AjSbUkCemGiLi12p/JEuOqymcyTyWPl4faMDOzXIOpicnMzHrACcLMzHI5QZiZWS4nCDMzy+UEYWZmuZwgzHpAUkdmBM9HlTP67hvY92R1MkKtWTUMmusgzMpkYzoEg9mA5xqEWRlIel7SNyU9nt5L4E3p8smS7k4HdbtL0sR0+X6SbkoHx/u9pKPTXdVK+r6S+xD8Or2S16wqnCDMemZIURPThzLr1kTEW4DvkIy6CcnAdz9KB3WbD1yeLr8c+G06ON5bgSfT5VOBKyLiUGA18IEKvx+zTvlKarMekLQ+IobnLH8eeEdEPJcOjPdyRIyW9Cqwf0RsTZeviIgxktqB5syAb4WhuH8TEVPT+S8A9RHxtcq/M7PduQZhVj7RyXRPbM5Md+B+QqsiJwiz8vlQ5vmBdPp+dg6YNhu4L52+CzgbdtycZu/eCtKsVP51YtYzQzIjogL8MiIKp7ruI+kxklrA6emyc4AfSvo80M7O0Vv/EZgn6eMkNYWzgaoPlW6W5T4IszJI+yBaIuLVasdiVi5uYjIzs1yuQZiZWS7XIMzMLJcThJmZ5XKCMDOzXE4QZmaWywnCzMxy/X8iJni1o0zHewAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_LSTM_model = tf.keras.models.load_model('LSTM_Best_Model')"
      ],
      "metadata": {
        "id": "EEDRd3UXH-wl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_LSTM_model.evaluate(testing_padded, testing_labels, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OdLdZxrBhe-c",
        "outputId": "73a2d0cb-079e-4181-9a1b-daf2d5de2a1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "119/119 [==============================] - 3s 10ms/step - loss: 0.4928 - accuracy: 0.8348\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4927993714809418, 0.8348261117935181]"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions2 = best_LSTM_model.predict(testing_padded)\n",
        "y_pred2 = np.argmax(predictions2, axis = 1)"
      ],
      "metadata": {
        "id": "kyTBpcY1hqV4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = ['Negative', 'Neutral', 'Positive']\n",
        "print(classification_report(testing_labels, y_pred2, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Q2OaOZ1htpi",
        "outputId": "17e76801-6054-45fe-dc02-2d3772e7e334"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.85      0.83      0.84      1633\n",
            "     Neutral       0.79      0.75      0.77       617\n",
            "    Positive       0.84      0.88      0.86      1546\n",
            "\n",
            "    accuracy                           0.83      3796\n",
            "   macro avg       0.82      0.82      0.82      3796\n",
            "weighted avg       0.83      0.83      0.83      3796\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(testing_labels, y_pred2)\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "xiozAoGPh5p4",
        "outputId": "f74b9d01-be6e-4efe-bcbf-4c469515dfe7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}