BitCoin_Major_Project_Sentiment_Analysis_and_Classification.ipynb

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    "colab": {
      "name": "BitCoin Major Project - Sentiment Analysis and Classification",
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/kiran-lenka/Bitcoin-Price-Predictor/blob/main/BitCoin_Major_Project_Sentiment_Analysis_and_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Requirements"
      ],
      "metadata": {
        "id": "KsTjTCTfQJxs"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZ9HbgDp8M9L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93794cc5-c347-4241-e2ee-dece26c8e0e4"
      },
      "source": [
        "\n",
        "# importing the necessary libraries\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import ssl  \n",
        "import os\n",
        "from datetime import date\n",
        "from io import StringIO\n",
        "import json\n",
        "\n",
        "import nltk \n",
        "nltk.download('wordnet')\n",
        "nltk.download('vader_lexicon')\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer as SIA\n",
        "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
        "from nltk.stem.porter import *\n",
        "\n",
        "import gensim\n",
        "from gensim.utils import simple_preprocess\n",
        "from gensim.parsing.preprocessing import STOPWORDS\n",
        "\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import plotly.figure_factory as ff\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.svm import NuSVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bitcoin Market Sentiment Analysis and Prediction"
      ],
      "metadata": {
        "id": "KQbAuYpqYs-B"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGcz7rXV1CMI"
      },
      "source": [
        "## Calculating public sentiment on Bitcoin"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vEEyPBLukNbn"
      },
      "source": [
        "### Reddit Sentiment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DcE-REKoyEHP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "4c73575d-1c2a-4bf7-d994-d98bc3933749"
      },
      "source": [
        "# grouping post by sentiment\n",
        "df_reddit_nlp = pd.read_csv('reddit-senti.csv')\n",
        "df_reddit_groupby = df_reddit_nlp.groupby('Sentiment').count()\n",
        "sentiment_lst = ['Negative', 'Neutral', 'Positive']\n",
        "lst1 = df_reddit_groupby['Post Titles']\n",
        "dict1 = {'Sentiment': sentiment_lst, 'Number': lst1}\n",
        "sent = pd.DataFrame(dict1)\n",
        "fig = px.bar(sent, x='Sentiment', y='Number', title='Reddit Sentiment Analysis')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1-ty72FkRTE"
      },
      "source": [
        "### Twitter Sentiment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "types = {'Tweets':'str','Sentiment':'str'}\n",
        "df_twitter_nlp = pd.read_csv('twitter-senti.csv',index_col=0,dtype=types)"
      ],
      "metadata": {
        "id": "kNECJJBGdPqE"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df_twitter_nlp"
      ],
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        "id": "qF1CZ-HWdRja",
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          "base_uri": "https://localhost:8080/",
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            "text/plain": [
              "        neg    neu    pos  compound  \\\n",
              "0     0.000    1.0  0.000    0.0000   \n",
              "1     0.292  0.708  0.000   -0.7650   \n",
              "2     0.000  0.854  0.146    0.4738   \n",
              "3     0.222  0.672  0.106   -0.5319   \n",
              "4     0.000    1.0  0.000    0.0000   \n",
              "...     ...    ...    ...       ...   \n",
              "7995  0.000  0.891  0.109    0.4215   \n",
              "7996  0.000  0.891  0.109    0.4215   \n",
              "7997  0.000  0.773  0.227    0.5719   \n",
              "7998  0.000  0.891  0.109    0.4215   \n",
              "7999  0.000  0.891  0.109    0.4215   \n",
              "\n",
              "                                                 Tweets Sentiment  \\\n",
              "0     @sanazmsvi @nftparticle #Flokifam\\nThe land of...         0   \n",
              "1     RT @NFTmagzine: #Sheepzy died for all of the #...        -1   \n",
              "2     RT @Zbaaboulinet: Scholarship Giveaway ! 50/50...         1   \n",
              "3     RT @Fantasywar2022: FANTASY WAR PRESALE WHITEL...        -1   \n",
              "4             GM #nft people ☀️📈 #gm #NFTartist #Crypto         0   \n",
              "...                                                 ...       ...   \n",
              "7995  RT @JoshuaAbel68: @AltCryptoGems You will be a...         1   \n",
              "7996  RT @JoshuaAbel68: @AltCryptoGems You will be a...         1   \n",
              "7997  @ForbesCrypto hidden gem, thank me later @Devo...         1   \n",
              "7998  RT @JoshuaAbel68: @AltCryptoGems You will be a...         1   \n",
              "7999  RT @JoshuaAbel68: @AltCryptoGems You will be a...         1   \n",
              "\n",
              "               Created at            Location  \n",
              "0     2022-01-06 11:19:59                 NaN  \n",
              "1     2022-01-06 11:19:59                 NaN  \n",
              "2     2022-01-06 11:19:59              elbi ✨  \n",
              "3     2022-01-06 11:19:59            Việt Nam  \n",
              "4     2022-01-06 11:19:59                 NaN  \n",
              "...                   ...                 ...  \n",
              "7995  2022-01-03 19:25:13  Gwagwalada - Abuja  \n",
              "7996  2022-01-03 19:24:47                 NaN  \n",
              "7997  2022-01-03 19:24:43             unknown  \n",
              "7998  2022-01-03 19:24:30                 NaN  \n",
              "7999  2022-01-03 19:22:09                 NaN  \n",
              "\n",
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              "      <th>neg</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>@sanazmsvi @nftparticle #Flokifam\\nThe land of...</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-01-06 11:19:59</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.292</td>\n",
              "      <td>0.708</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.7650</td>\n",
              "      <td>RT @NFTmagzine: #Sheepzy died for all of the #...</td>\n",
              "      <td>-1</td>\n",
              "      <td>2022-01-06 11:19:59</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.854</td>\n",
              "      <td>0.146</td>\n",
              "      <td>0.4738</td>\n",
              "      <td>RT @Zbaaboulinet: Scholarship Giveaway ! 50/50...</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-01-06 11:19:59</td>\n",
              "      <td>elbi ✨</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.222</td>\n",
              "      <td>0.672</td>\n",
              "      <td>0.106</td>\n",
              "      <td>-0.5319</td>\n",
              "      <td>RT @Fantasywar2022: FANTASY WAR PRESALE WHITEL...</td>\n",
              "      <td>-1</td>\n",
              "      <td>2022-01-06 11:19:59</td>\n",
              "      <td>Việt Nam</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>GM #nft people ☀️📈 #gm #NFTartist #Crypto</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-01-06 11:19:59</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7995</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.891</td>\n",
              "      <td>0.109</td>\n",
              "      <td>0.4215</td>\n",
              "      <td>RT @JoshuaAbel68: @AltCryptoGems You will be a...</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-01-03 19:25:13</td>\n",
              "      <td>Gwagwalada - Abuja</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7996</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.891</td>\n",
              "      <td>0.109</td>\n",
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              "      <td>RT @JoshuaAbel68: @AltCryptoGems You will be a...</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-01-03 19:24:47</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>7997</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.773</td>\n",
              "      <td>0.227</td>\n",
              "      <td>0.5719</td>\n",
              "      <td>@ForbesCrypto hidden gem, thank me later @Devo...</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-01-03 19:24:43</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7998</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.891</td>\n",
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              "      <td>RT @JoshuaAbel68: @AltCryptoGems You will be a...</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-01-03 19:24:30</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>7999</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.891</td>\n",
              "      <td>0.109</td>\n",
              "      <td>0.4215</td>\n",
              "      <td>RT @JoshuaAbel68: @AltCryptoGems You will be a...</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-01-03 19:22:09</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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          "metadata": {},
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    {
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      "metadata": {
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          "height": 542
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        "outputId": "aa5b84b7-51a6-4a33-8447-e9e36fca372f"
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      "source": [
        "# grouping post by sentiment\n",
        "df_twitter_groupby = df_twitter_nlp.groupby('Sentiment').count()\n",
        "lst1 = df_twitter_groupby['Tweets']\n",
        "sentiment_lst = ['Negative', 'Neutral', 'Positive']\n",
        "dict1 = {'Sentiment': sentiment_lst, 'Number': lst1}\n",
        "sent = pd.DataFrame(dict1)\n",
        "fig = px.bar(sent, x='Sentiment', y='Number', title='Twitter Sentiment Analysis')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "</body>\n",
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          },
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing and Building Models"
      ],
      "metadata": {
        "id": "cdzMLkJ7Y0mV"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MmCDpbMF9RKI"
      },
      "source": [
        "stemmer = SnowballStemmer(\"english\")\n",
        "\n",
        "def lemmatize_stemming(text):\n",
        "    return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos='v'))\n",
        "\n",
        "# Tokenize and lemmatize\n",
        "def preprocess(text):\n",
        "    \n",
        "    result = []\n",
        "    \n",
        "    for token in gensim.utils.simple_preprocess(text) :\n",
        "        \n",
        "        if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3:\n",
        "            \n",
        "            # TODO: Apply lemmatize_stemming() on the token, then add to the results list\n",
        "            result.append(lemmatize_stemming(token))\n",
        "    \n",
        "    return result\n",
        "\n",
        "def classifier_models():\n",
        "  LR = LogisticRegression(random_state=0)\n",
        "  model_perf(LR, 'Logistic Regression')\n",
        "\n",
        "  RFC = RandomForestClassifier(criterion = 'gini')\n",
        "  model_perf(RFC, 'Random Forest')\n",
        "\n",
        "  SGDC = SGDClassifier(max_iter=1000, tol=1e-3)\n",
        "  model_perf(SGDC, 'Stochastic Gradient Descent')\n",
        "\n",
        "  svm = SVC()\n",
        "  model_perf(svm, 'Support Vector Machine')\n",
        "  '''\n",
        "  LSVC = LinearSVC(random_state=0, tol=1e-5)\n",
        "  model_perf(LSVC, 'Linear Support Vector Classification')\n",
        "\n",
        "  GNB = GaussianNB()\n",
        "  model_perf(GNB, 'Naive Bayes')  \n",
        "\n",
        "  BNB = BernoulliNB()\n",
        "  model_perf(BNB, 'Bernoulli Naive Bayes')\n",
        "\n",
        "  MNB = MultinomialNB()\n",
        "  model_perf(MNB, 'Multinomial Naive Bayes')\n",
        "\n",
        "  AdaB = AdaBoostClassifier(n_estimators=100, random_state=0)\n",
        "  model_perf(AdaB, 'AdaBoost')\n",
        "  '''\n",
        "\n",
        "  LGBM = LGBMClassifier(random_state=5)\n",
        "  model_perf(LGBM, 'Light Gradient Boosting Machine')\n",
        "\n",
        "  GBC = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)\n",
        "  model_perf(GBC, 'Gradient Boost Classifier')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Performance Comparison between different classifiers"
      ],
      "metadata": {
        "id": "cfT_c2AVY6bN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reddit"
      ],
      "metadata": {
        "id": "0KHqh2FtZQP0"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cNuTTg76NF6K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e98b472-ff74-4420-e06b-13251b14dfed"
      },
      "source": [
        "# passing reddit posts through various classifiers\n",
        "\n",
        "name_arr = []\n",
        "acc_arr = []\n",
        "  \n",
        "def model_perf(model, name):\n",
        "  model.fit(X_train, y_train)\n",
        "  preds = model.predict(X_test)\n",
        "  acc = round(accuracy_score(y_test, preds), 5)*100\n",
        "  name_arr.append(name)\n",
        "  acc_arr.append(acc)\n",
        "  print('======================================')\n",
        "  print('Accuracy ' + f'{name}' + ':', acc,'\\n')\n",
        "  print(classification_report(y_test, preds))\n",
        "  print('======================================')\n",
        "\n",
        "processed_docs = df_reddit_nlp['Post Titles'].map(preprocess)\n",
        "\n",
        "# this is the step where each word is given a number from which it can be recognized\n",
        "tokenizer = Tokenizer(oov_token='<00V>')\n",
        "tokenizer.fit_on_texts(processed_docs)\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "# here the tokenized numbers are put into sequences as compared to the post titles\n",
        "sequences = tokenizer.texts_to_sequences(processed_docs)\n",
        "\n",
        "# here we make all the sentences of equal length\n",
        "padded = pad_sequences(sequences, padding='post')\n",
        "\n",
        "X = padded\n",
        "y = df_reddit_nlp['Sentiment']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 42)\n",
        "\n",
        "classifier_models()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "======================================\n",
            "Accuracy Logistic Regression: 56.908 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.48      0.03      0.05       438\n",
            "           0       0.59      0.91      0.71      1389\n",
            "           1       0.49      0.28      0.35       793\n",
            "\n",
            "    accuracy                           0.57      2620\n",
            "   macro avg       0.52      0.40      0.37      2620\n",
            "weighted avg       0.54      0.57      0.49      2620\n",
            "\n",
            "======================================\n",
            "======================================\n",
            "Accuracy Random Forest: 82.786 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.92      0.66      0.77       438\n",
            "           0       0.83      0.90      0.86      1389\n",
            "           1       0.79      0.79      0.79       793\n",
            "\n",
            "    accuracy                           0.83      2620\n",
            "   macro avg       0.85      0.78      0.81      2620\n",
            "weighted avg       0.83      0.83      0.83      2620\n",
            "\n",
            "======================================\n",
            "======================================\n",
            "Accuracy Stochastic Gradient Descent: 52.71 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.23      0.16      0.19       438\n",
            "           0       0.56      0.88      0.69      1389\n",
            "           1       0.56      0.11      0.18       793\n",
            "\n",
            "    accuracy                           0.53      2620\n",
            "   macro avg       0.45      0.38      0.35      2620\n",
            "weighted avg       0.51      0.53      0.45      2620\n",
            "\n",
            "======================================\n",
            "======================================\n",
            "Accuracy Support Vector Machine: 59.504000000000005 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.93      0.06      0.12       438\n",
            "           0       0.59      0.94      0.73      1389\n",
            "           1       0.59      0.29      0.39       793\n",
            "\n",
            "    accuracy                           0.60      2620\n",
            "   macro avg       0.70      0.43      0.41      2620\n",
            "weighted avg       0.65      0.60      0.52      2620\n",
            "\n",
            "======================================\n",
            "======================================\n",
            "Accuracy Light Gradient Boosting Machine: 72.748 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.88      0.30      0.45       438\n",
            "           0       0.70      0.94      0.80      1389\n",
            "           1       0.76      0.60      0.67       793\n",
            "\n",
            "    accuracy                           0.73      2620\n",
            "   macro avg       0.78      0.61      0.64      2620\n",
            "weighted avg       0.75      0.73      0.70      2620\n",
            "\n",
            "======================================\n",
            "======================================\n",
            "Accuracy Gradient Boost Classifier: 60.611000000000004 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.57      0.04      0.07       438\n",
            "           0       0.62      0.89      0.73      1389\n",
            "           1       0.57      0.42      0.48       793\n",
            "\n",
            "    accuracy                           0.61      2620\n",
            "   macro avg       0.59      0.45      0.43      2620\n",
            "weighted avg       0.59      0.61      0.54      2620\n",
            "\n",
            "======================================\n"
          ]
        }
      ]
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          "height": 542
        },
        "outputId": "da9a5cd1-a589-4535-8a6c-872e46ce15f4"
      },
      "source": [
        "dict2 = {'Name of the models': name_arr, 'Accuracy': acc_arr}\n",
        "perf = pd.DataFrame(dict2)\n",
        "fig = px.bar(perf, x='Name of the models', y='Accuracy', title='Accuracy scores of various classifiers models')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "markdown",
      "source": [
        "### Twitter"
      ],
      "metadata": {
        "id": "TjNTSRq8ZWBm"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUr-hJfT7GyE",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "835ed626-4ab1-4f4b-b9b3-c97377e5678a"
      },
      "source": [
        "# passing twitter posts through various classifiers\n",
        "\n",
        "name_arr = []\n",
        "acc_arr = []\n",
        "  \n",
        "def model_perf(model, name):\n",
        "  model.fit(X_train, y_train)\n",
        "  preds = model.predict(X_test)\n",
        "  acc = round(accuracy_score(y_test, preds), 5)*100\n",
        "  name_arr.append(name)\n",
        "  acc_arr.append(acc)\n",
        "  print('======================================')\n",
        "  print('Accuracy ' + f'{name}' + ':', acc,'\\n')\n",
        "  print(classification_report(y_test, preds))\n",
        "  print('======================================')\n",
        "\n",
        "df_twitter_nlp.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
        "df_twitter_nlp.fillna('', inplace=True)\n",
        "processed_docs = df_twitter_nlp['Tweets'].map(preprocess)\n",
        "\n",
        "# this is the step where each word is given a number from which it can be recognized\n",
        "tokenizer = Tokenizer(oov_token='<00V>')\n",
        "tokenizer.fit_on_texts(processed_docs)\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "# here the tokenized numbers are put into sequences as compared to the post titles\n",
        "sequences = tokenizer.texts_to_sequences(processed_docs)\n",
        "\n",
        "# here we make all the sentences of equal length\n",
        "padded = pad_sequences(sequences, padding='post')\n",
        "\n",
        "X = padded\n",
        "y = df_twitter_nlp['Sentiment']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 42)\n",
        "\n",
        "classifier_models()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "======================================\n",
            "Accuracy Logistic Regression: 46.858 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "                   0.00      0.00      0.00         3\n",
            "          -1       0.50      0.01      0.02       257\n",
            "           0       0.41      0.52      0.46       918\n",
            "           1       0.52      0.53      0.52      1225\n",
            "\n",
            "    accuracy                           0.47      2403\n",
            "   macro avg       0.36      0.26      0.25      2403\n",
            "weighted avg       0.48      0.47      0.45      2403\n",
            "\n",
            "======================================\n",
            "======================================\n",
            "Accuracy Random Forest: 83.937 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "                   1.00      0.67      0.80         3\n",
            "          -1       0.98      0.59      0.73       257\n",
            "           0       0.81      0.84      0.83       918\n",
            "           1       0.84      0.89      0.87      1225\n",
            "\n",
            "    accuracy                           0.84      2403\n",
            "   macro avg       0.91      0.75      0.81      2403\n",
            "weighted avg       0.85      0.84      0.84      2403\n",
            "\n",
            "======================================\n",
            "======================================\n",
            "Accuracy Stochastic Gradient Descent: 36.912 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "                   0.00      0.00      0.00         3\n",
            "          -1       0.10      0.37      0.16       257\n",
            "           0       0.47      0.16      0.24       918\n",
            "           1       0.55      0.52      0.54      1225\n",
            "\n",
            "    accuracy                           0.37      2403\n",
            "   macro avg       0.28      0.26      0.24      2403\n",
            "weighted avg       0.47      0.37      0.38      2403\n",
            "\n",
            "======================================\n",
            "======================================\n",
            "Accuracy Support Vector Machine: 58.343999999999994 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "                   0.00      0.00      0.00         3\n",
            "          -1       0.68      0.11      0.18       257\n",
            "           0       0.60      0.35      0.44       918\n",
            "           1       0.58      0.86      0.69      1225\n",
            "\n",
            "    accuracy                           0.58      2403\n",
            "   macro avg       0.46      0.33      0.33      2403\n",
            "weighted avg       0.59      0.58      0.54      2403\n",
            "\n",
            "======================================\n",
            "======================================\n",
            "Accuracy Light Gradient Boosting Machine: 83.645 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "                   1.00      1.00      1.00         3\n",
            "          -1       0.92      0.56      0.70       257\n",
            "           0       0.81      0.83      0.82       918\n",
            "           1       0.84      0.89      0.87      1225\n",
            "\n",
            "    accuracy                           0.84      2403\n",
            "   macro avg       0.89      0.82      0.85      2403\n",
            "weighted avg       0.84      0.84      0.83      2403\n",
            "\n",
            "======================================\n",
            "======================================\n",
            "Accuracy Gradient Boost Classifier: 70.537 \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "                   0.00      0.00      0.00         3\n",
            "          -1       0.75      0.31      0.44       257\n",
            "           0       0.65      0.71      0.68       918\n",
            "           1       0.74      0.79      0.77      1225\n",
            "\n",
            "    accuracy                           0.71      2403\n",
            "   macro avg       0.54      0.45      0.47      2403\n",
            "weighted avg       0.71      0.71      0.70      2403\n",
            "\n",
            "======================================\n"
          ]
        }
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        "outputId": "57f0619f-42ed-495a-e038-adf3efbd5f8a"
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      "source": [
        "dict3 = {'Name of the models': name_arr, 'Accuracy': acc_arr}\n",
        "perf1 = pd.DataFrame(dict3)\n",
        "fig = px.bar(dict3, x='Name of the models', y='Accuracy', title='Accuracy scores of various classifiers models')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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