ML_mini_proj_2_sentiment_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOrtWq1dWRdJlsSK0ObdyaP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/srdebayan/sentiment_analysis_neuralNetwork/blob/main/ML_mini_proj_2_sentiment_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This workbook is a NLP program which takes the tweet data and then creates two models- A sequential neural network and one convolution neural network to do the sentiment analysis. We can compare the performance of the two models here aswell. There are techniques which are used in processing the tweets aswell. "
      ],
      "metadata": {
        "id": "ohNdL-dVQ-IK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "eMoDNwiFQMxk"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "from numpy import array\n",
        "from keras.preprocessing.text import one_hot\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Activation, Dropout, Dense\n",
        "from keras.layers import Flatten, Conv1D, LSTM\n",
        "from keras.layers import GlobalMaxPooling1D\n",
        "from keras.layers import Embedding\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.text import Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#our dataset file is stored in this location\n",
        "url = 'https://raw.githubusercontent.com/srdebayan/sentiment_analysis_neuralNetwork/main/Sentiment140.tenPercent.sample.tweets.tsv'\n",
        "tweets_df = pd.read_table(url)\n",
        "# Dataset is now stored in a Pandas Dataframe\n",
        "\n",
        "tweets_df.isnull().values.any()\n",
        "\n",
        "tweets_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVMzDaq4Svf6",
        "outputId": "f79b9897-4750-4af2-dc60-17b84595d309"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(160000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking the tweets\n",
        "tweets_df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zjF3_VHrUdSY",
        "outputId": "01ecf6ae-6bbd-46b6-a493-abc65058b74c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sentiment_label                                         tweet_text\n",
              "0                4  @elephantbird Hey dear, Happy Friday to You  A...\n",
              "1                4  Ughhh layin downnnn    Waiting for zeina to co...\n",
              "2                0  @greeniebach I reckon he'll play, even if he's...\n",
              "3                0              @vaLewee I know!  Saw it on the news!\n",
              "4                0  very sad that http://www.fabchannel.com/ has c..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-873c9420-f98e-4c8f-b8d1-6c0458b23339\">\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>sentiment_label</th>\n",
              "      <th>tweet_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>@elephantbird Hey dear, Happy Friday to You  A...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>Ughhh layin downnnn    Waiting for zeina to co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>@greeniebach I reckon he'll play, even if he's...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>@vaLewee I know!  Saw it on the news!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>very sad that http://www.fabchannel.com/ has c...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-873c9420-f98e-4c8f-b8d1-6c0458b23339')\"\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-873c9420-f98e-4c8f-b8d1-6c0458b23339 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-873c9420-f98e-4c8f-b8d1-6c0458b23339');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweets_df[\"tweet_text\"][3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "5YCR9_fQU7zw",
        "outputId": "8f3aed2b-030b-48fb-818c-06d402d35fcd"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'@vaLewee I know!  Saw it on the news!'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking the balance of the data, it is equally balanced\n",
        "import seaborn as sns\n",
        "\n",
        "sns.countplot(x='sentiment_label', data=tweets_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "WafDi2o8VD1J",
        "outputId": "49d524e7-6130-4002-9371-05c074e7c221"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9021d39e80>"
            ]
          },
          "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will now process the tweets. We will clean the text so that it can be transformed easily."
      ],
      "metadata": {
        "id": "yoNK6UZzQ09B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tweets_df['tweet_text']=tweets_df['tweet_text'].str.lower()\n",
        "#tweets_df['tweet_text'].tail()\n",
        "stopwordlist = ['a', 'about', 'above', 'after', 'again', 'ain', 'all', 'am', 'an',\n",
        "             'and','any','are', 'as', 'at', 'be', 'because', 'been', 'before',\n",
        "             'being', 'below', 'between','both', 'by', 'can', 'd', 'did', 'do',\n",
        "             'does', 'doing', 'down', 'during', 'each','few', 'for', 'from',\n",
        "             'further', 'had', 'has', 'have', 'having', 'he', 'her', 'here',\n",
        "             'hers', 'herself', 'him', 'himself', 'his', 'how', 'i', 'if', 'in',\n",
        "             'into','is', 'it', 'its', 'itself', 'just', 'll', 'm', 'ma',\n",
        "             'me', 'more', 'most','my', 'myself', 'now', 'o', 'of', 'on', 'once',\n",
        "             'only', 'or', 'other', 'our', 'ours','ourselves', 'out', 'own', 're',\n",
        "             's', 'same', 'she', \"shes\", 'should', \"shouldve\",'so', 'some', 'such',\n",
        "             't', 'than', 'that', \"thatll\", 'the', 'their', 'theirs', 'them',\n",
        "             'themselves', 'then', 'there', 'these', 'they', 'this', 'those',\n",
        "             'through', 'to', 'too','under', 'until', 'up', 've', 'very', 'was',\n",
        "             'we', 'were', 'what', 'when', 'where','which','while', 'who', 'whom',\n",
        "             'why', 'will', 'with', 'won', 'y', 'you', \"youd\",\"youll\", \"youre\",\n",
        "             \"youve\", 'your', 'yours', 'yourself', 'yourselves']\n",
        "# Removing Stopwords\n",
        "\n",
        "STOPWORDS = set(stopwordlist)\n",
        "def cleaning_stopwords(text):\n",
        "    return \" \".join([word for word in str(text).split() if word not in STOPWORDS])\n",
        "tweets_df['tweet_text'] = tweets_df['tweet_text'].apply(lambda text: cleaning_stopwords(text))\n",
        "tweets_df['tweet_text'].tail()\n",
        "# removing URLs\n",
        "\n",
        "def cleaning_URLs(data):\n",
        "    return re.sub('((www.[^s]+)|(https?://[^s]+))',' ',data)\n",
        "tweets_df['tweet_text'] = tweets_df['tweet_text'].apply(lambda x: cleaning_URLs(x))\n",
        "tweets_df['tweet_text'].head()\n",
        "# removing numbers \n",
        "\n",
        "def cleaning_numbers(data):\n",
        "    return re.sub('[0-9]+', '', data)\n",
        "tweets_df['tweet_text'] = tweets_df['tweet_text'].apply(lambda x: cleaning_numbers(x))\n",
        "tweets_df['tweet_text'].head()\n",
        "# fucntion to removes pattern in the input text.\n",
        "\n",
        "def remove_pattern(input_txt, pattern):\n",
        "    r = re.findall(pattern, input_txt)\n",
        "    for word in r:\n",
        "        input_txt = re.sub(word, \"\", input_txt)\n",
        "    return input_txt\n",
        "# remove twitter handles (@user)\n",
        "\n",
        "tweets_df['tweet_text'] = np.vectorize(remove_pattern)(tweets_df['tweet_text'], \"@[\\w]*\")\n",
        "tweets_df.head()\n",
        "# remove special characters, numbers and punctuations\n",
        "\n",
        "tweets_df['tweet_text'] = tweets_df['tweet_text'].str.replace(\"[^a-zA-Z#]\", \" \")\n",
        "tweets_df.head()\n",
        "# remove short words\n",
        "\n",
        "tweets_df['tweet_text'] = tweets_df['tweet_text'].apply(lambda x: \" \".join([w for w in x.split() if len(w)>3]))\n",
        "tweets_df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "h0cjpRrRu8ht",
        "outputId": "028848b7-7fbc-4c21-8fc8-2c1467f495cd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-6e9cfadf44d3>:51: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  tweets_df['tweet_text'] = tweets_df['tweet_text'].str.replace(\"[^a-zA-Z#]\", \" \")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sentiment_label                                        tweet_text\n",
              "0                4         dear happy friday already rice bowl lunch\n",
              "1                4  ughhh layin downnnn waiting zeina cook breakfast\n",
              "2                0             reckon play even know nothing without\n",
              "3                0                                         know news\n",
              "4                0                     down services used over years"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ea5ae36-ee55-4672-8fb2-ca746e98bf58\">\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>sentiment_label</th>\n",
              "      <th>tweet_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>dear happy friday already rice bowl lunch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>ughhh layin downnnn waiting zeina cook breakfast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>reckon play even know nothing without</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>know news</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>down services used over years</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ea5ae36-ee55-4672-8fb2-ca746e98bf58')\"\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-4ea5ae36-ee55-4672-8fb2-ca746e98bf58 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-4ea5ae36-ee55-4672-8fb2-ca746e98bf58');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = []\n",
        "text = list(tweets_df['tweet_text'])\n",
        "for tx in text:\n",
        "    X.append(tx)\n",
        "X[3]    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MyITf1Oyb8v1",
        "outputId": "2d568756-7f86-4b96-d07e-5c45212b7efe"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'know news'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = tweets_df['sentiment_label']\n",
        "y[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBslLU5oco-I",
        "outputId": "98ee8c44-9729-464b-9d4c-3975d5ffb074"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#converting the labels of the sentiment to 1 or 0\n",
        "y = tweets_df['sentiment_label']\n",
        "\n",
        "y = np.array(list(map(lambda x: 1 if x==4 else 0, y)))"
      ],
      "metadata": {
        "id": "ki7r6I8mlgP8"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
        "tokenizer = Tokenizer(num_words=5000)\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "\n",
        "X_train = tokenizer.texts_to_sequences(X_train)\n",
        "X_test = tokenizer.texts_to_sequences(X_test)"
      ],
      "metadata": {
        "id": "lafJOhACc5b5"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7GDCJi4u2mbb",
        "outputId": "2cb59159-3cc3-45ec-d3ed-ec6560d66560"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding 1 because of reserved 0 index\n",
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "\n",
        "maxlen = 100\n",
        "\n",
        "X_train = pad_sequences(X_train, padding='post', maxlen=maxlen)\n",
        "X_test = pad_sequences(X_test, padding='post', maxlen=maxlen)"
      ],
      "metadata": {
        "id": "qpKVcPd2dPhc"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files"
      ],
      "metadata": {
        "id": "kP4DzaW6nvdh"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rjhW62VHTRmF",
        "outputId": "36b47ff5-2026-4e53-c688-541b60c9c7f4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import array\n",
        "from numpy import asarray\n",
        "from numpy import zeros\n",
        "\n",
        "embeddings_dictionary = dict()\n",
        "glove_file = open('/content/gdrive/MyDrive/glove.6B.100d.txt', encoding=\"utf8\")\n",
        "\n",
        "\n",
        "for line in glove_file:\n",
        "    records = line.split()\n",
        "    word = records[0]\n",
        "    vector_dimensions = asarray(records[1:], dtype='float32')\n",
        "    embeddings_dictionary [word] = vector_dimensions\n",
        "glove_file.close()"
      ],
      "metadata": {
        "id": "OOe8oEZ6ddOo"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_matrix = zeros((vocab_size, 100))\n",
        "for word, index in tokenizer.word_index.items():\n",
        "    embedding_vector = embeddings_dictionary.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix[index] = embedding_vector"
      ],
      "metadata": {
        "id": "JXK2NhzvpbY2"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sequential neural network"
      ],
      "metadata": {
        "id": "THsfxVGzYCYV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "embedding_layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=maxlen , trainable=False)\n",
        "model.add(embedding_layer)\n",
        "\n",
        "model.add(Flatten())\n",
        "#model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.add(Dense(16, input_dim=2, activation='relu', name= 'input'))\n",
        "model.add(Dense(32, activation='relu', name= 'hidden_1'))\n",
        "model.add(Dense(64, activation='relu', name= 'hidden_2'))\n",
        "model.add(Dense(128, activation='relu', name= 'hidden_3'))\n",
        "model.add(Dense(256,  activation='relu', name= 'hidden_4'))\n",
        "#model.add(Dense(512, activation='relu', name= 'hidden_5'))\n",
        "#model.add(Dense(1028, activation='relu', name= 'hidden_6'))\n",
        "#model.add(Dense(2056, activation='relu', name= 'hidden_7'))\n",
        "model.add(Dense(1, input_dim=2, activation='sigmoid', name= 'output'))"
      ],
      "metadata": {
        "id": "NxaX5EZ2pn_m"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GP2dlIaprhl",
        "outputId": "90dcc03f-ef7c-4719-9f16-0c1b6f11f0ee"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 100, 100)          5559800   \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 10000)             0         \n",
            "                                                                 \n",
            " input (Dense)               (None, 16)                160016    \n",
            "                                                                 \n",
            " hidden_1 (Dense)            (None, 32)                544       \n",
            "                                                                 \n",
            " hidden_2 (Dense)            (None, 64)                2112      \n",
            "                                                                 \n",
            " hidden_3 (Dense)            (None, 128)               8320      \n",
            "                                                                 \n",
            " hidden_4 (Dense)            (None, 256)               33024     \n",
            "                                                                 \n",
            " output (Dense)              (None, 1)                 257       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,764,073\n",
            "Trainable params: 204,273\n",
            "Non-trainable params: 5,559,800\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, batch_size=128, epochs=10, verbose=1, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WDS5Oixu463o",
        "outputId": "d3629b64-afab-461f-f421-9416fed7f588"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "800/800 [==============================] - 14s 16ms/step - loss: 0.5989 - acc: 0.6728 - val_loss: 0.5892 - val_acc: 0.6810\n",
            "Epoch 2/10\n",
            "800/800 [==============================] - 12s 16ms/step - loss: 0.5735 - acc: 0.6957 - val_loss: 0.5847 - val_acc: 0.6910\n",
            "Epoch 3/10\n",
            "800/800 [==============================] - 12s 15ms/step - loss: 0.5622 - acc: 0.7042 - val_loss: 0.5765 - val_acc: 0.6968\n",
            "Epoch 4/10\n",
            "800/800 [==============================] - 11s 14ms/step - loss: 0.5515 - acc: 0.7124 - val_loss: 0.5853 - val_acc: 0.6903\n",
            "Epoch 5/10\n",
            "800/800 [==============================] - 11s 14ms/step - loss: 0.5418 - acc: 0.7206 - val_loss: 0.5840 - val_acc: 0.6923\n",
            "Epoch 6/10\n",
            "800/800 [==============================] - 13s 17ms/step - loss: 0.5325 - acc: 0.7282 - val_loss: 0.5866 - val_acc: 0.6934\n",
            "Epoch 7/10\n",
            "800/800 [==============================] - 12s 15ms/step - loss: 0.5242 - acc: 0.7326 - val_loss: 0.5994 - val_acc: 0.6923\n",
            "Epoch 8/10\n",
            "800/800 [==============================] - 12s 15ms/step - loss: 0.5160 - acc: 0.7391 - val_loss: 0.5927 - val_acc: 0.6897\n",
            "Epoch 9/10\n",
            "800/800 [==============================] - 12s 16ms/step - loss: 0.5087 - acc: 0.7447 - val_loss: 0.6112 - val_acc: 0.6896\n",
            "Epoch 10/10\n",
            "800/800 [==============================] - 12s 16ms/step - loss: 0.5024 - acc: 0.7494 - val_loss: 0.6120 - val_acc: 0.6874\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = model.evaluate(X_test, y_test, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_8MlQDcqIOo",
        "outputId": "fdc114bb-8ef6-4bc9-fc8b-ecea975b6c10"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000/1000 [==============================] - 4s 4ms/step - loss: 0.6104 - acc: 0.6868\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Test Score:\", score[0])\n",
        "print(\"Test Accuracy:\", score[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XRMyaokgqNXM",
        "outputId": "846f27a3-e55e-43a6-8917-fa16c8a4bfaa"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Score: 0.6103923320770264\n",
            "Test Accuracy: 0.686843752861023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "02sUoOGyqYGA",
        "outputId": "57e43b0b-033d-474e-afbe-02d523cd0311"
      },
      "execution_count": 21,
      "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": "markdown",
      "source": [
        "**CNN**"
      ],
      "metadata": {
        "id": "S8C6gxk6EUR3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "embedding_layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=maxlen , trainable=False)\n",
        "model.add(embedding_layer)\n",
        "\n",
        "model.add(Conv1D(128, 5, activation='relu'))\n",
        "model.add(GlobalMaxPooling1D())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])"
      ],
      "metadata": {
        "id": "vBWq_1JiEYrw"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKJMRk-FEd2R",
        "outputId": "3f303cf9-8de0-4047-bb75-511e3a43e2eb"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 100, 100)          5559800   \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 96, 128)           64128     \n",
            "                                                                 \n",
            " global_max_pooling1d (Globa  (None, 128)              0         \n",
            " lMaxPooling1D)                                                  \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,624,057\n",
            "Trainable params: 64,257\n",
            "Non-trainable params: 5,559,800\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, batch_size=128, epochs=10, verbose=1, validation_split=0.2)\n",
        "\n",
        "score = model.evaluate(X_test, y_test, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxjLw17tEiWI",
        "outputId": "6f2fe9ab-4e6c-43aa-eeed-b4fcfd15c16d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "800/800 [==============================] - 101s 126ms/step - loss: 0.5731 - acc: 0.6982 - val_loss: 0.5556 - val_acc: 0.7140\n",
            "Epoch 2/10\n",
            "800/800 [==============================] - 97s 121ms/step - loss: 0.5283 - acc: 0.7334 - val_loss: 0.5498 - val_acc: 0.7200\n",
            "Epoch 3/10\n",
            "800/800 [==============================] - 93s 116ms/step - loss: 0.5026 - acc: 0.7512 - val_loss: 0.5532 - val_acc: 0.7172\n",
            "Epoch 4/10\n",
            "800/800 [==============================] - 94s 118ms/step - loss: 0.4755 - acc: 0.7698 - val_loss: 0.5632 - val_acc: 0.7159\n",
            "Epoch 5/10\n",
            "800/800 [==============================] - 98s 122ms/step - loss: 0.4502 - acc: 0.7856 - val_loss: 0.5729 - val_acc: 0.7151\n",
            "Epoch 6/10\n",
            "800/800 [==============================] - 90s 113ms/step - loss: 0.4269 - acc: 0.8000 - val_loss: 0.5865 - val_acc: 0.7127\n",
            "Epoch 7/10\n",
            "800/800 [==============================] - 92s 115ms/step - loss: 0.4040 - acc: 0.8127 - val_loss: 0.6072 - val_acc: 0.7082\n",
            "Epoch 8/10\n",
            "800/800 [==============================] - 91s 114ms/step - loss: 0.3864 - acc: 0.8227 - val_loss: 0.6291 - val_acc: 0.7063\n",
            "Epoch 9/10\n",
            "800/800 [==============================] - 94s 117ms/step - loss: 0.3661 - acc: 0.8342 - val_loss: 0.6471 - val_acc: 0.7043\n",
            "Epoch 10/10\n",
            "800/800 [==============================] - 92s 115ms/step - loss: 0.3496 - acc: 0.8427 - val_loss: 0.6666 - val_acc: 0.7023\n",
            "1000/1000 [==============================] - 12s 12ms/step - loss: 0.6533 - acc: 0.7050\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Test Score:\", score[0])\n",
        "print(\"Test Accuracy:\", score[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTFmjhYHHQDB",
        "outputId": "352bc75c-098b-4895-f86b-c0fdbd5b4993"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Score: 0.6532533168792725\n",
            "Test Accuracy: 0.7049687504768372\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','test'], loc = 'upper left')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','test'], loc = 'upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "x5bmzMt7HVab",
        "outputId": "91943f57-2945-4c3f-90fd-8dd9fedd7856"
      },
      "execution_count": 26,
      "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"
          }
        }
      ]
    }
  ]
}