sentimentent.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mdrm89EUWAT5"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import nltk\n",
        "import re \n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Embedding, LSTM, GlobalMaxPooling1D, SpatialDropout1D"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_AKRFMSuZh4J"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "with zipfile.ZipFile(\"/content/drive/MyDrive/archive (2).zip\", 'r') as zip_ref:\n",
        "    zip_ref.extractall(\"/content\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ckURKFG6WEP8"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv('/content/twitter_training.csv', sep=',', names=['Tweet_ID','Entity','Sentiment','Tweet_content'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YGrMFd1SWEVp",
        "outputId": "aa05f5e6-e4fd-45a5-a248-a99870236dd8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Tweet_ID       Entity Sentiment  \\\n",
              "0      2401  Borderlands  Positive   \n",
              "1      2401  Borderlands  Positive   \n",
              "2      2401  Borderlands  Positive   \n",
              "3      2401  Borderlands  Positive   \n",
              "4      2401  Borderlands  Positive   \n",
              "\n",
              "                                       Tweet_content  \n",
              "0  im getting on borderlands and i will murder yo...  \n",
              "1  I am coming to the borders and I will kill you...  \n",
              "2  im getting on borderlands and i will kill you ...  \n",
              "3  im coming on borderlands and i will murder you...  \n",
              "4  im getting on borderlands 2 and i will murder ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e955fb1b-48a0-4305-a501-2b146d9bcf87\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tweet_ID</th>\n",
              "      <th>Entity</th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>Tweet_content</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2401</td>\n",
              "      <td>Borderlands</td>\n",
              "      <td>Positive</td>\n",
              "      <td>im getting on borderlands and i will murder yo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2401</td>\n",
              "      <td>Borderlands</td>\n",
              "      <td>Positive</td>\n",
              "      <td>I am coming to the borders and I will kill you...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2401</td>\n",
              "      <td>Borderlands</td>\n",
              "      <td>Positive</td>\n",
              "      <td>im getting on borderlands and i will kill you ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2401</td>\n",
              "      <td>Borderlands</td>\n",
              "      <td>Positive</td>\n",
              "      <td>im coming on borderlands and i will murder you...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2401</td>\n",
              "      <td>Borderlands</td>\n",
              "      <td>Positive</td>\n",
              "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e955fb1b-48a0-4305-a501-2b146d9bcf87')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e955fb1b-48a0-4305-a501-2b146d9bcf87 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e955fb1b-48a0-4305-a501-2b146d9bcf87');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "8q_ICuD6WEZT",
        "outputId": "359edd96-d0e3-4130-96bc-595ee0b5ce02"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<matplotlib.patches.Wedge at 0x7f4925bddad0>,\n",
              "  <matplotlib.patches.Wedge at 0x7f4925b6d310>,\n",
              "  <matplotlib.patches.Wedge at 0x7f4925b6d2d0>,\n",
              "  <matplotlib.patches.Wedge at 0x7f4925b794d0>],\n",
              " [Text(-0.8936408809046303, 0.6414093669225578, 'Negative'),\n",
              "  Text(-0.39649770564254117, -1.0260553442286633, 'Positive'),\n",
              "  Text(1.0532293937999033, -0.3173449921392938, 'Neutral'),\n",
              "  Text(0.5716146417662045, 0.9398173765782871, 'Irrelevant')],\n",
              " [Text(-0.4874404804934347, 0.3498596546850315, '30.2%'),\n",
              "  Text(-0.21627147580502246, -0.5596665513974527, '27.9%'),\n",
              "  Text(0.5744887602544927, -0.17309726843961476, '24.5%'),\n",
              "  Text(0.31178980459974787, 0.5126276599517929, '17.4%')])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.pie(data.Sentiment.value_counts()\n",
        ",labels=['Negative','Positive','Neutral','Irrelevant'],autopct='%1.1f%%',startangle=90)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qC_PSLRqlgIj"
      },
      "outputs": [],
      "source": [
        "data.dropna(axis=0, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kMxau-VkWEc6"
      },
      "outputs": [],
      "source": [
        "data.reset_index(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2O6Q9XzzWEgo"
      },
      "outputs": [],
      "source": [
        "replace_list = {r\"i'm\": 'i am',\n",
        "                r\"'re\": ' are',\n",
        "                r\"let’s\": 'let us',\n",
        "                r\"'s\":  ' is',\n",
        "                r\"'ve\": ' have',\n",
        "                r\"can't\": 'can not',\n",
        "                r\"cannot\": 'can not',\n",
        "                r\"shan’t\": 'shall not',\n",
        "                r\"n't\": ' not',\n",
        "                r\"'d\": ' would',\n",
        "                r\"'ll\": ' will',\n",
        "                r\"'scuse\": 'excuse',\n",
        "                ',': ' ,',\n",
        "                '.': ' .',\n",
        "                '!': ' !',\n",
        "                '?': ' ?',\n",
        "                '\\s+': ' '}\n",
        "def clean_text(text):\n",
        "    text = text.lower()\n",
        "    for s in replace_list:\n",
        "        text = text.replace(s, replace_list[s])\n",
        "    text = ' '.join(text.split())\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FBjCuxy5WEkF"
      },
      "outputs": [],
      "source": [
        "x = data['Tweet_content'].apply(lambda p: clean_text(p))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "hzSb8D00lOo5",
        "outputId": "1662ad37-83cc-42e5-c0ce-b859d0894d37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "max Tweet_content len: 311\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "Tweet_content_len = x.apply(lambda p: len(p.split(' ')))\n",
        "max_Tweet_content_len = Tweet_content_len.max()\n",
        "print('max Tweet_content len: {0}'.format(max_Tweet_content_len))\n",
        "plt.figure(figsize = (10, 8))\n",
        "plt.hist(Tweet_content_len, density = True)\n",
        "plt.xlabel('Tweet_content len')\n",
        "plt.ylabel('probability')\n",
        "plt.grid()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9weHEsOmAHX",
        "outputId": "365df2e8-c3bf-40bf-a521-81396284177d"
      },
      "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: nltk in /usr/local/lib/python3.7/dist-packages (3.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk) (1.2.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk) (7.1.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk) (2022.6.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk) (4.64.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install nltk\n",
        "import nltk\n",
        "# ntlk.download('stopwords')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wjgJK2kFnQuC"
      },
      "outputs": [],
      "source": [
        "from nltk.corpus import stopwords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WlXdUEvvnzzm",
        "outputId": "9d05d0a0-6b96-486a-b715-953ee9aefaaf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "NLTK Downloader\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "Downloader> d\n",
            "\n",
            "Download which package (l=list; x=cancel)?\n",
            "  Identifier> all\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "    Downloading collection 'all'\n",
            "       | \n",
            "       | Downloading package abc to /root/nltk_data...\n",
            "       |   Unzipping corpora/abc.zip.\n",
            "       | Downloading package alpino to /root/nltk_data...\n",
            "       |   Unzipping corpora/alpino.zip.\n",
            "       | Downloading package averaged_perceptron_tagger to\n",
            "       |     /root/nltk_data...\n",
            "       |   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "       | Downloading package averaged_perceptron_tagger_ru to\n",
            "       |     /root/nltk_data...\n",
            "       |   Unzipping taggers/averaged_perceptron_tagger_ru.zip.\n",
            "       | Downloading package basque_grammars to /root/nltk_data...\n",
            "       |   Unzipping grammars/basque_grammars.zip.\n",
            "       | Downloading package biocreative_ppi to /root/nltk_data...\n",
            "       |   Unzipping corpora/biocreative_ppi.zip.\n",
            "       | Downloading package bllip_wsj_no_aux to /root/nltk_data...\n",
            "       |   Unzipping models/bllip_wsj_no_aux.zip.\n",
            "       | Downloading package book_grammars to /root/nltk_data...\n",
            "       |   Unzipping grammars/book_grammars.zip.\n",
            "       | Downloading package brown to /root/nltk_data...\n",
            "       |   Unzipping corpora/brown.zip.\n",
            "       | Downloading package brown_tei to /root/nltk_data...\n",
            "       |   Unzipping corpora/brown_tei.zip.\n",
            "       | Downloading package cess_cat to /root/nltk_data...\n",
            "       |   Unzipping corpora/cess_cat.zip.\n",
            "       | Downloading package cess_esp to /root/nltk_data...\n",
            "       |   Unzipping corpora/cess_esp.zip.\n",
            "       | Downloading package chat80 to /root/nltk_data...\n",
            "       |   Unzipping corpora/chat80.zip.\n",
            "       | Downloading package city_database to /root/nltk_data...\n",
            "       |   Unzipping corpora/city_database.zip.\n",
            "       | Downloading package cmudict to /root/nltk_data...\n",
            "       |   Unzipping corpora/cmudict.zip.\n",
            "       | Downloading package comparative_sentences to\n",
            "       |     /root/nltk_data...\n",
            "       |   Unzipping corpora/comparative_sentences.zip.\n",
            "       | Downloading package comtrans to /root/nltk_data...\n",
            "       | Downloading package conll2000 to /root/nltk_data...\n",
            "       |   Unzipping corpora/conll2000.zip.\n",
            "       | Downloading package conll2002 to /root/nltk_data...\n",
            "       |   Unzipping corpora/conll2002.zip.\n",
            "       | Downloading package conll2007 to /root/nltk_data...\n",
            "       | Downloading package crubadan to /root/nltk_data...\n",
            "       |   Unzipping corpora/crubadan.zip.\n",
            "       | Downloading package dependency_treebank to /root/nltk_data...\n",
            "       |   Unzipping corpora/dependency_treebank.zip.\n",
            "       | Downloading package dolch to /root/nltk_data...\n",
            "       |   Unzipping corpora/dolch.zip.\n",
            "       | Downloading package europarl_raw to /root/nltk_data...\n",
            "       |   Unzipping corpora/europarl_raw.zip.\n",
            "       | Downloading package extended_omw to /root/nltk_data...\n",
            "       | Downloading package floresta to /root/nltk_data...\n",
            "       |   Unzipping corpora/floresta.zip.\n",
            "       | Downloading package framenet_v15 to /root/nltk_data...\n",
            "       |   Unzipping corpora/framenet_v15.zip.\n",
            "       | Downloading package framenet_v17 to /root/nltk_data...\n",
            "       |   Unzipping corpora/framenet_v17.zip.\n",
            "       | Downloading package gazetteers to /root/nltk_data...\n",
            "       |   Unzipping corpora/gazetteers.zip.\n",
            "       | Downloading package genesis to /root/nltk_data...\n",
            "       |   Unzipping corpora/genesis.zip.\n",
            "       | Downloading package gutenberg to /root/nltk_data...\n",
            "       |   Unzipping corpora/gutenberg.zip.\n",
            "       | Downloading package ieer to /root/nltk_data...\n",
            "       |   Unzipping corpora/ieer.zip.\n",
            "       | Downloading package inaugural to /root/nltk_data...\n",
            "       |   Unzipping corpora/inaugural.zip.\n",
            "       | Downloading package indian to /root/nltk_data...\n",
            "       |   Unzipping corpora/indian.zip.\n",
            "       | Downloading package jeita to /root/nltk_data...\n",
            "       | Downloading package kimmo to /root/nltk_data...\n",
            "       |   Unzipping corpora/kimmo.zip.\n",
            "       | Downloading package knbc to /root/nltk_data...\n",
            "       | Downloading package large_grammars to /root/nltk_data...\n",
            "       |   Unzipping grammars/large_grammars.zip.\n",
            "       | Downloading package lin_thesaurus to /root/nltk_data...\n",
            "       |   Unzipping corpora/lin_thesaurus.zip.\n",
            "       | Downloading package mac_morpho to /root/nltk_data...\n",
            "       |   Unzipping corpora/mac_morpho.zip.\n",
            "       | Downloading package machado to /root/nltk_data...\n",
            "       | Downloading package masc_tagged to /root/nltk_data...\n",
            "       | Downloading package maxent_ne_chunker to /root/nltk_data...\n",
            "       |   Unzipping chunkers/maxent_ne_chunker.zip.\n",
            "       | Downloading package maxent_treebank_pos_tagger to\n",
            "       |     /root/nltk_data...\n",
            "       |   Unzipping taggers/maxent_treebank_pos_tagger.zip.\n",
            "       | Downloading package moses_sample to /root/nltk_data...\n",
            "       |   Unzipping models/moses_sample.zip.\n",
            "       | Downloading package movie_reviews to /root/nltk_data...\n",
            "       |   Unzipping corpora/movie_reviews.zip.\n",
            "       | Downloading package mte_teip5 to /root/nltk_data...\n",
            "       |   Unzipping corpora/mte_teip5.zip.\n",
            "       | Downloading package mwa_ppdb to /root/nltk_data...\n",
            "       |   Unzipping misc/mwa_ppdb.zip.\n",
            "       | Downloading package names to /root/nltk_data...\n",
            "       |   Unzipping corpora/names.zip.\n",
            "       | Downloading package nombank.1.0 to /root/nltk_data...\n",
            "       | Downloading package nonbreaking_prefixes to\n",
            "       |     /root/nltk_data...\n",
            "       |   Unzipping corpora/nonbreaking_prefixes.zip.\n",
            "       | Downloading package nps_chat to /root/nltk_data...\n",
            "       |   Unzipping corpora/nps_chat.zip.\n",
            "       | Downloading package omw to /root/nltk_data...\n",
            "       | Downloading package omw-1.4 to /root/nltk_data...\n",
            "       | Downloading package opinion_lexicon to /root/nltk_data...\n",
            "       |   Unzipping corpora/opinion_lexicon.zip.\n",
            "       | Downloading package panlex_swadesh to /root/nltk_data...\n",
            "       | Downloading package paradigms to /root/nltk_data...\n",
            "       |   Unzipping corpora/paradigms.zip.\n",
            "       | Downloading package pe08 to /root/nltk_data...\n",
            "       |   Unzipping corpora/pe08.zip.\n",
            "       | Downloading package perluniprops to /root/nltk_data...\n",
            "       |   Unzipping misc/perluniprops.zip.\n",
            "       | Downloading package pil to /root/nltk_data...\n",
            "       |   Unzipping corpora/pil.zip.\n",
            "       | Downloading package pl196x to /root/nltk_data...\n",
            "       |   Unzipping corpora/pl196x.zip.\n",
            "       | Downloading package porter_test to /root/nltk_data...\n",
            "       |   Unzipping stemmers/porter_test.zip.\n",
            "       | Downloading package ppattach to /root/nltk_data...\n",
            "       |   Unzipping corpora/ppattach.zip.\n",
            "       | Downloading package problem_reports to /root/nltk_data...\n",
            "       |   Unzipping corpora/problem_reports.zip.\n",
            "       | Downloading package product_reviews_1 to /root/nltk_data...\n",
            "       |   Unzipping corpora/product_reviews_1.zip.\n",
            "       | Downloading package product_reviews_2 to /root/nltk_data...\n",
            "       |   Unzipping corpora/product_reviews_2.zip.\n",
            "       | Downloading package propbank to /root/nltk_data...\n",
            "       | Downloading package pros_cons to /root/nltk_data...\n",
            "       |   Unzipping corpora/pros_cons.zip.\n",
            "       | Downloading package ptb to /root/nltk_data...\n",
            "       |   Unzipping corpora/ptb.zip.\n",
            "       | Downloading package punkt to /root/nltk_data...\n",
            "       |   Unzipping tokenizers/punkt.zip.\n",
            "       | Downloading package qc to /root/nltk_data...\n",
            "       |   Unzipping corpora/qc.zip.\n",
            "       | Downloading package reuters to /root/nltk_data...\n",
            "       | Downloading package rslp to /root/nltk_data...\n",
            "       |   Unzipping stemmers/rslp.zip.\n",
            "       | Downloading package rte to /root/nltk_data...\n",
            "       |   Unzipping corpora/rte.zip.\n",
            "       | Downloading package sample_grammars to /root/nltk_data...\n",
            "       |   Unzipping grammars/sample_grammars.zip.\n",
            "       | Downloading package semcor to /root/nltk_data...\n",
            "       | Downloading package senseval to /root/nltk_data...\n",
            "       |   Unzipping corpora/senseval.zip.\n",
            "       | Downloading package sentence_polarity to /root/nltk_data...\n",
            "       |   Unzipping corpora/sentence_polarity.zip.\n",
            "       | Downloading package sentiwordnet to /root/nltk_data...\n",
            "       |   Unzipping corpora/sentiwordnet.zip.\n",
            "       | Downloading package shakespeare to /root/nltk_data...\n",
            "       |   Unzipping corpora/shakespeare.zip.\n",
            "       | Downloading package sinica_treebank to /root/nltk_data...\n",
            "       |   Unzipping corpora/sinica_treebank.zip.\n",
            "       | Downloading package smultron to /root/nltk_data...\n",
            "       |   Unzipping corpora/smultron.zip.\n",
            "       | Downloading package snowball_data to /root/nltk_data...\n",
            "       | Downloading package spanish_grammars to /root/nltk_data...\n",
            "       |   Unzipping grammars/spanish_grammars.zip.\n",
            "       | Downloading package state_union to /root/nltk_data...\n",
            "       |   Unzipping corpora/state_union.zip.\n",
            "       | Downloading package stopwords to /root/nltk_data...\n",
            "       |   Unzipping corpora/stopwords.zip.\n",
            "       | Downloading package subjectivity to /root/nltk_data...\n",
            "       |   Unzipping corpora/subjectivity.zip.\n",
            "       | Downloading package swadesh to /root/nltk_data...\n",
            "       |   Unzipping corpora/swadesh.zip.\n",
            "       | Downloading package switchboard to /root/nltk_data...\n",
            "       |   Unzipping corpora/switchboard.zip.\n",
            "       | Downloading package tagsets to /root/nltk_data...\n",
            "       |   Unzipping help/tagsets.zip.\n",
            "       | Downloading package timit to /root/nltk_data...\n",
            "       |   Unzipping corpora/timit.zip.\n",
            "       | Downloading package toolbox to /root/nltk_data...\n",
            "       |   Unzipping corpora/toolbox.zip.\n",
            "       | Downloading package treebank to /root/nltk_data...\n",
            "       |   Unzipping corpora/treebank.zip.\n",
            "       | Downloading package twitter_samples to /root/nltk_data...\n",
            "       |   Unzipping corpora/twitter_samples.zip.\n",
            "       | Downloading package udhr to /root/nltk_data...\n",
            "       |   Unzipping corpora/udhr.zip.\n",
            "       | Downloading package udhr2 to /root/nltk_data...\n",
            "       |   Unzipping corpora/udhr2.zip.\n",
            "       | Downloading package unicode_samples to /root/nltk_data...\n",
            "       |   Unzipping corpora/unicode_samples.zip.\n",
            "       | Downloading package universal_tagset to /root/nltk_data...\n",
            "       |   Unzipping taggers/universal_tagset.zip.\n",
            "       | Downloading package universal_treebanks_v20 to\n",
            "       |     /root/nltk_data...\n",
            "       | Downloading package vader_lexicon to /root/nltk_data...\n",
            "       | Downloading package verbnet to /root/nltk_data...\n",
            "       |   Unzipping corpora/verbnet.zip.\n",
            "       | Downloading package verbnet3 to /root/nltk_data...\n",
            "       |   Unzipping corpora/verbnet3.zip.\n",
            "       | Downloading package webtext to /root/nltk_data...\n",
            "       |   Unzipping corpora/webtext.zip.\n",
            "       | Downloading package wmt15_eval to /root/nltk_data...\n",
            "       |   Unzipping models/wmt15_eval.zip.\n",
            "       | Downloading package word2vec_sample to /root/nltk_data...\n",
            "       |   Unzipping models/word2vec_sample.zip.\n",
            "       | Downloading package wordnet to /root/nltk_data...\n",
            "       | Downloading package wordnet2021 to /root/nltk_data...\n",
            "       | Downloading package wordnet31 to /root/nltk_data...\n",
            "       | Downloading package wordnet_ic to /root/nltk_data...\n",
            "       |   Unzipping corpora/wordnet_ic.zip.\n",
            "       | Downloading package words to /root/nltk_data...\n",
            "       |   Unzipping corpora/words.zip.\n",
            "       | Downloading package ycoe to /root/nltk_data...\n",
            "       |   Unzipping corpora/ycoe.zip.\n",
            "       | \n",
            "     Done downloading collection all\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "Downloader> q\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "nltk.download(\n",
        "    \n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uN7RWUJ9lsXb"
      },
      "outputs": [],
      "source": [
        "import re \n",
        "REPLACE_WITH_SPACE = re.compile(\"(@)\")\n",
        "SPACE = \" \"\n",
        "from nltk.corpus import stopwords\n",
        "english_stop_words = stopwords.words('english')\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "\n",
        "import re \n",
        "REPLACE_WITH_SPACE = re.compile(\"(@)\")\n",
        "SPACE = \" \"\n",
        "from nltk.corpus import stopwords\n",
        "english_stop_words = stopwords.words('english')\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "#1\n",
        "def reviews(reviews):  \n",
        "    reviews = [REPLACE_WITH_SPACE.sub(SPACE, line.lower()) for line in reviews]\n",
        "    \n",
        "    return reviews\n",
        "\n",
        "def remove_stop_words(corpus):\n",
        "    removed_stop_words = []\n",
        "    for review in corpus:\n",
        "        removed_stop_words.append(\n",
        "            ' '.join([word for word in review.split()  if word not in english_stop_words]))\n",
        "    return removed_stop_words\n",
        "\n",
        "def get_stemmed_text(corpus):\n",
        "    stemmer = PorterStemmer()\n",
        "\n",
        "    return [' '.join([stemmer.stem(word) for word in review.split()]) for review in corpus]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QMM1jWaLqUpQ"
      },
      "outputs": [],
      "source": [
        "y =data['Sentiment']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qudeP1ocqUap"
      },
      "outputs": [],
      "source": [
        "#1\n",
        "reviewtweet = reviews(x)\n",
        "#2\n",
        "no_stop_words_tweet = remove_stop_words(reviewtweet)\n",
        "#3\n",
        "stemmed_reviews_tweet = get_stemmed_text(no_stop_words_tweet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qklQMQ4dqUMD"
      },
      "outputs": [],
      "source": [
        "max_words = 8000\n",
        "\n",
        "\n",
        "tokenizer = Tokenizer(\n",
        "    num_words = max_words,\n",
        "    filters = '\"#$%&()*+-/:;<=>@[\\]^_`{|}~'\n",
        ")\n",
        "tokenizer.fit_on_texts(stemmed_reviews_tweet)\n",
        "x = tokenizer.texts_to_sequences(stemmed_reviews_tweet)\n",
        "x = pad_sequences(x, maxlen = 300)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WIKeEgFqqSbr"
      },
      "outputs": [],
      "source": [
        "label_tokenizer = Tokenizer()\n",
        "label_tokenizer.fit_on_texts(y)\n",
        "\n",
        "y = np.array(label_tokenizer.texts_to_sequences(y))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yqj6wJISqnIA"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size = 0.8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1L5DzK8Xqm-h"
      },
      "outputs": [],
      "source": [
        "model_lstm = Sequential()\n",
        "model_lstm.add(Embedding(input_dim = max_words, output_dim = 128, input_length = 300))\n",
        "model_lstm.add(SpatialDropout1D(0.3))\n",
        "model_lstm.add(LSTM(128, dropout = 0.3, recurrent_dropout = 0.3))\n",
        "model_lstm.add(Dense(128, activation = 'relu'))\n",
        "model_lstm.add(Dropout(0.3))\n",
        "model_lstm.add(Dense(5, activation = 'softmax'))\n",
        "model_lstm.compile(\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer='Adam',\n",
        "    metrics=['accuracy']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsOP28QeqmyR",
        "outputId": "ea86101b-402e-4c68-f96e-f7c1c404f808"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "116/116 [==============================] - 802s 7s/step - loss: 1.2619 - accuracy: 0.4313 - val_loss: 1.0105 - val_accuracy: 0.5856\n",
            "Epoch 2/10\n",
            "116/116 [==============================] - 789s 7s/step - loss: 0.8774 - accuracy: 0.6556 - val_loss: 0.7842 - val_accuracy: 0.6945\n",
            "Epoch 3/10\n",
            "116/116 [==============================] - 807s 7s/step - loss: 0.6921 - accuracy: 0.7379 - val_loss: 0.6882 - val_accuracy: 0.7345\n",
            "Epoch 4/10\n",
            "116/116 [==============================] - 801s 7s/step - loss: 0.6020 - accuracy: 0.7716 - val_loss: 0.6414 - val_accuracy: 0.7545\n",
            "Epoch 5/10\n",
            "116/116 [==============================] - 791s 7s/step - loss: 0.5434 - accuracy: 0.7944 - val_loss: 0.6049 - val_accuracy: 0.7686\n",
            "Epoch 6/10\n",
            "116/116 [==============================] - 794s 7s/step - loss: 0.4958 - accuracy: 0.8135 - val_loss: 0.5764 - val_accuracy: 0.7770\n",
            "Epoch 7/10\n",
            "116/116 [==============================] - 800s 7s/step - loss: 0.4574 - accuracy: 0.8263 - val_loss: 0.5578 - val_accuracy: 0.7891\n",
            "Epoch 8/10\n",
            "116/116 [==============================] - 796s 7s/step - loss: 0.4256 - accuracy: 0.8382 - val_loss: 0.5410 - val_accuracy: 0.7991\n",
            "Epoch 9/10\n",
            "116/116 [==============================] - 793s 7s/step - loss: 0.4022 - accuracy: 0.8469 - val_loss: 0.5340 - val_accuracy: 0.8032\n",
            "Epoch 10/10\n",
            "116/116 [==============================] - 808s 7s/step - loss: 0.3781 - accuracy: 0.8560 - val_loss: 0.5191 - val_accuracy: 0.8104\n"
          ]
        }
      ],
      "source": [
        "history = model_lstm.fit(\n",
        "    x_train,\n",
        "    y_train,\n",
        "    validation_data=(x_test,y_test) , \n",
        "    epochs = 10,\n",
        "    batch_size = 512\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U3akRZ73q5lt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0de7ab0d-a3f0-4eb1-8360-97b404d567d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 300, 128)          1024000   \n",
            "                                                                 \n",
            " spatial_dropout1d (SpatialD  (None, 300, 128)         0         \n",
            " ropout1D)                                                       \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 128)               131584    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 5)                 645       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,172,741\n",
            "Trainable params: 1,172,741\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model_lstm.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HQoxtRJ3rAw_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "a53a8a7c-a0f8-4885-eea3-0e6e243988ee"
      },
      "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"
          }
        }
      ],
      "source": [
        "plt.figure(1)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.legend(['training','validation'])\n",
        "plt.title('Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.figure(2)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.legend(['training','validation'])\n",
        "plt.title('Accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}