LSTM_BiLSTM_Davidson.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **HATE SPEECH DETECTION USING LSTM AND BILSMT ON DAVIDSON**\n",
        "\n",
        "**REFERENCE**: The implementation here is a combination of different implementations: <br>\n",
        "[1] Pinkesh Badjatiya and Shashank Gupta in \"Deep Learning for Hate Speech Detection\" (to appear in WWW'17 proceedings) (2017) at https://github.com/pinkeshbadjatiya/twitter-hatespeech <br>\n",
        "[2] Adil Emre Bilgic(June, 2021) in \"Sentiment Analysis Hate Speech at https://www.kaggle.com/adilemrebilgic/sentiment-analysis-hate-speech/notebook<br>\n",
        "[3] Srishti Bhalla(Feb 2019) in Hate-Speech-Detection-on-Twitter-Data at https://github.com/srishb28/Hate-Speech-Detection-on-Twitter-Data trained on the dataset of Zeerak et. al. at https://github.com/ZeerakW/hatespeech mentioned also in the report<br>\n",
        "[4] Deephate: Paper: Cao, R., Ka-Wei Lee, R., Hoang, T.A.: DeepHate: Hate Speech Detection via Multi-Faceted Text Representations. In: WebSci ’20: 12th ACM Conference on Web Science, July 2020 Pages 11–20 (October 2020) https://gitlab.com/bottle_shop/safe/deephate/-/tree/master/DeepHate<br><br>\n",
        "\n",
        "In this notebook we present the approach as we are trying to detect hate speech in tweets of the Davidson dataset. The dataset is distributed as hate, offensive and neither. The label '1' denotes the tweet is offensive and label '0' is hate, label `2` is neither.\n",
        "\n",
        "There are two models: LSTM and BiLSTM. We train the models on Davidson dataset, where we split on **80%** for training and **20%** on testing. The data is not shuffled in order to have a fair comparison between other models presented in the paper i.e Deephate and TFIDF Based Model.\n",
        "\n",
        "> There is a simple LSTM network tested with different hidden units (**50, 64, 128** unit in the hidden layer) and a drop out rate of `0.2` and a reccurrent droput rate of `0.4`. There is one dense layer at the end with 1 unit and an activation function as sigmoid (since we are trying to predict different classes labels `0`, `1`, and `2`). We use `categorical_cross_entropy` loss as well.\n",
        "\n",
        "The models are fine tunded by differing the no.of.epochs 1-100 epochs and batch size (32,64,128, 256).You can have a look at the accuracy and training plot below, with respective comments.\n",
        "<br><br><br>\n",
        "**Table of Contents:**<br><br>\n",
        "\n",
        "---\n",
        "HATE SPEECH DETECTION USING LSTM AND BILSMT ON KAGGLE\n",
        "1. Imports and Preleminaries\n",
        "2. Data Cleaning\n",
        " - Text Pre-Processing\n",
        " - Cleaning Pipeline\n",
        " - Data Preparation\n",
        "3. LSTM and BiLSTM Models\n",
        "4. Training\n",
        "5. Model Training\n",
        " - Word Embeddings\n",
        "    - Train The Word2Vec Embedding on Davidson tweets\n",
        "    - Use Embedding Layer as trainable layer for LSTM and BiLSTM\n",
        "    - Use Embedding Layer as non-trainable for LSTM and BiLSTM\n",
        "6. Controversial Topic Analysis\n",
        "<br><br><br>\n",
        "How to run the notebook?\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "1.   Start the notebook and run every cell\n",
        "2.   Make sure to have all packages installed. It might be better if you use Google Colab or Kaggle as they already have most of the packages available for usage\n",
        "3.   Packages to be installed: Numpy, Pandas, Matplotlib, Seaborn, Tensorflow, nltk, keras, and genism\n",
        "4.   Then you can just run the notebook as usuall and enjoy the result :)\n",
        "5.   If anything happens please contact as to our email address as noted on the submission lab report.\n",
        "---\n",
        "\n",
        "<br>\n",
        "Thank you for your understanding!\n",
        "\n"
      ],
      "metadata": {
        "id": "zStaZ860ljwR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import and Preleminaries"
      ],
      "metadata": {
        "id": "xU4kDLr2fpRT"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3bBqLFte9vgF"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from nltk.corpus import stopwords\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import re\n",
        "import string\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from sklearn.metrics import precision_recall_fscore_support, accuracy_score, classification_report, confusion_matrix\n",
        "import keras.utils\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from nltk.tokenize import word_tokenize\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.layers import Dense, Input, LSTM, Embedding, Dropout, Activation\n",
        "from keras.layers import Bidirectional, GlobalMaxPool1D\n",
        "from keras.callbacks import EarlyStopping,ModelCheckpoint\n",
        "from keras.models import Model\n",
        "from keras import initializers, regularizers, constraints, optimizers, layers\n",
        "from keras.layers import LeakyReLU\n",
        "from keras.models import load_model\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_rGW--wl_rq",
        "outputId": "180550cb-d577-4c35-f3b9-8815011000a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8t0udr209vgJ"
      },
      "source": [
        "## Loading Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SV5Fdtns9vgK"
      },
      "source": [
        "Lets load the data to content folder in github. The data will be loadet to `\\content\\name_of_file`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "id": "eXqOIVsRePMO",
        "outputId": "4036fb74-3d0b-4a6c-d7a8-4f138e455705"
      },
      "source": [
        "# Use this only if you are using colab otherwise comment it out\n",
        "from google.colab import files\n",
        "dataset_file_dict = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1b79cca5-bc18-4082-8c13-f5ecd64ab752\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1b79cca5-bc18-4082-8c13-f5ecd64ab752\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving 150labeledLGBT.csv to 150labeledLGBT.csv\n",
            "Saving 190labeledTrump.csv to 190labeledTrump.csv\n",
            "Saving 190labeledCovid.csv to 190labeledCovid.csv\n",
            "Saving davidson.csv to davidson.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This are the dataset that are used for controversial topic analysis. Make sure the content path is correct in your system. In colab the files would be uploaded to /content directory. This would be used in the end of the notebook to analyse the result based on the model training. We would use LSTM for testing\n"
      ],
      "metadata": {
        "id": "3zh6MwZokkrU"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCkbH96G6m-2"
      },
      "source": [
        "df_trump = pd.read_csv(\"/content/190labeledTrump.csv\")\n",
        "df_lgbt = pd.read_csv(\"/content/150labeledLGBT.csv\")\n",
        "df_covid = pd.read_csv(\"/content/190labeledCovid.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4eNQ0jU9vgK",
        "outputId": "51e8d4e0-99d0-437f-cbaf-7bd722a898c6"
      },
      "source": [
        "davidson_df = pd.read_csv(\"/content/davidson.csv\")\n",
        "print(davidson_df.head())\n",
        "print(len(davidson_df))\n",
        "classes = davidson_df.groupby('class')\n",
        "print(classes.describe())\n",
        "\n",
        "# df_temp = davidson_df[['class', 'tweet']]\n",
        "# test_data = df_temp[19827:].copy() # 4956 will be test data so we can compare with deep hate and tfidf\n",
        "# data = df_temp[:19827].copy()\n",
        "# print(data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Unnamed: 0  count  ...  class                                              tweet\n",
            "0           0      3  ...      2  !!! RT @mayasolovely: As a woman you shouldn't...\n",
            "1           1      3  ...      1  !!!!! RT @mleew17: boy dats cold...tyga dwn ba...\n",
            "2           2      3  ...      1  !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...\n",
            "3           3      3  ...      1  !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...\n",
            "4           4      6  ...      1  !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...\n",
            "\n",
            "[5 rows x 7 columns]\n",
            "24783\n",
            "      Unnamed: 0                                   ... neither               \n",
            "           count          mean          std   min  ...     25%  50%  75%  max\n",
            "class                                              ...                       \n",
            "0         1430.0  10983.741958  7241.005639  85.0  ...     0.0  0.0  0.0  4.0\n",
            "1        19190.0  12827.344919  7304.666765   1.0  ...     0.0  0.0  0.0  3.0\n",
            "2         4163.0  12590.555369  7222.217918   0.0  ...     2.0  3.0  3.0  9.0\n",
            "\n",
            "[3 rows x 40 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4abjhYdS9vgM"
      },
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acVPMBXc9vgM",
        "outputId": "700b5bb4-6b63-48c2-cfa3-9f0da1160fb6"
      },
      "source": [
        "davidson_df.isnull().values.any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "davidson_df['tweet'].str.split().map(lambda x: len(x)).hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "sbeaH2-vm-ty",
        "outputId": "56d82cc7-213c-4fd0-be69-62baabcaaf9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc44328550>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Text Pre-Processing\n",
        "\n",
        "This is rather a simple cleaning pipeline:\n",
        "\n",
        "\n",
        "1.   Remove special characters and leave only letters.\n",
        "2.   Lowercase all letters and split the text\n",
        "3.   Remove emojis/links/usernames and normalize hash tags.\n",
        "4.   Apply stemming on the data to fix the influence of the bad words\n",
        "5.   Remove all the stopwords as they are do not contain a lot of information stored.\n",
        "6.   Create a sequence again\n",
        "\n",
        "> This method was part of the Deephate project found in the submission folder in gitlab. We tried to align our methods in order to have a much more fair comparison. However, in LSTM we also make use of Stemming as we believe this might be a factor that influence the learning of the model"
      ],
      "metadata": {
        "id": "acZFb9X_mBaD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cleaning Pipleine\n",
        "The cleaning is similar to deephate.\n",
        "Reference: Code implementation for the paper \"DeepHate: Hate Speech Detection via Multi-Faceted Text Representations\" (WebSci'20) Roy Lee (2021) at https://gitlab.com/bottle_shop/safe/deephate/-/blob/master/DeepHate/dataset.py"
      ],
      "metadata": {
        "id": "K3TF8IZqsHSl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def expand_match(contraction):\n",
        "    contraction_mapping = {\n",
        "        \"isn't\": \"is not\",\n",
        "        \"aren't\": \"are not\",\n",
        "        \"con't\": \"cannot\",\n",
        "        \"can't've\": \"cannot have\",\n",
        "        \"you'll've\": \"your will have\",\n",
        "        \"you're\": \"you are\",\n",
        "        \"you've\": \"you have\"\n",
        "    }\n",
        "    match = contraction.group(0)\n",
        "    first_char = match[0]\n",
        "    expanded_contraction = contraction_mapping.get(match) \\\n",
        "        if contraction_mapping.get(match) \\\n",
        "        else contraction_mapping.get(match.lower())\n",
        "    expanded_contraction = first_char + expanded_contraction[1:]\n",
        "    return expanded_contraction"
      ],
      "metadata": {
        "id": "V6sKnEhk8biD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_tweets(text):\n",
        "    url_pattern = re.compile(r'(https?:\\/\\/(?:www\\.|(?!www))[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\\.[^\\s]{2,}|https?:\\/\\/(?:www\\.|(?!www))'r'[a-zA-Z0-9]\\.[^\\s]{2,}|www\\.[a-zA-Z0-9]\\.[^\\s]{2,})')\n",
        "    emojis_pattern = re.compile(u'([\\U00002600-\\U000027BF])|([\\U0001f300-\\U0001f64F])|([\\U0001f680-\\U0001f6FF])')\n",
        "    hash_pattern = re.compile(r'#\\w*')\n",
        "    single_letter_pattern = re.compile(r'(?<![\\w\\-])\\w(?![\\w\\-])')\n",
        "    blank_spaces_pattern = re.compile(r'\\s{2,}|\\t')\n",
        "    reserved_pattern = re.compile(r'(RT|rt|FAV|fav|VIA|via)')\n",
        "    mention_pattern = re.compile(r'@\\w*')\n",
        "    CONTRACTION_MAP = {\n",
        "        \"isn't\": \"is not\",\n",
        "        \"aren't\": \"are not\",\n",
        "        \"con't\": \"cannot\",\n",
        "        \"can't've\": \"cannot have\",\n",
        "        \"you'll've\": \"your will have\",\n",
        "        \"you're\": \"you are\",\n",
        "        \"you've\": \"you have\"\n",
        "    }\n",
        "    constraction_pattern = re.compile('({})'.format('|'.join(CONTRACTION_MAP.keys())),\n",
        "                                        flags=re.IGNORECASE | re.DOTALL)\n",
        "    Whitespace = re.compile(u\"[\\s\\u0020\\u00a0\\u1680\\u180e\\u202f\\u205f\\u3000\\u2000-\\u200a]+\", re.UNICODE)\n",
        "    urls = re.sub(pattern=url_pattern, repl='', string=text)\n",
        "    mentions = re.sub(pattern=mention_pattern, repl='', string=urls)\n",
        "    hashtag = re.sub(pattern=hash_pattern, repl='', string=mentions)\n",
        "    reserved = re.sub(pattern=reserved_pattern, repl='', string=hashtag)\n",
        "    reserved = Whitespace.sub(\" \", reserved)\n",
        "    reserved = constraction_pattern.sub(expand_match, reserved)\n",
        "    punct = \"[{}]+\".format(string.punctuation)\n",
        "    punctuation = re.sub(punct, '', reserved)\n",
        "    single = re.sub(pattern=single_letter_pattern, repl='', string=punctuation)\n",
        "    blank = re.sub(pattern=blank_spaces_pattern, repl=' ', string=single)\n",
        "    blank = blank.lower()\n",
        "    # print blank\n",
        "    return blank.split()"
      ],
      "metadata": {
        "id": "EUncE1S3uTrZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3dNwzDi9vgN"
      },
      "source": [
        "def tweet_processing(raw_tweet):\n",
        "    words = clean_tweets(raw_tweet)\n",
        "    stops = set(stopwords.words(\"english\"))\n",
        "    m_w = [w for w in words if not w in stops]\n",
        "    return (\" \".join(m_w))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_df_tweet(data_df):\n",
        "    topic_data = data_df[~data_df['combined_label'].isnull()][['tweets', 'combined_label']] # label2, label3\n",
        "    num_tweets = topic_data[\"tweets\"].size\n",
        "    clean_tweet = []\n",
        "    for i in range(0,num_tweets):\n",
        "        clean_tweet.append(tweet_processing(topic_data[\"tweets\"][i]))\n",
        "    topic_data[\"tweet\"] = clean_tweet\n",
        "    topic_data[\"class\"] = topic_data[\"combined_label\"]\n",
        "    topic_data[\"class\"] = topic_data[\"class\"].astype(int)\n",
        "    return topic_data"
      ],
      "metadata": {
        "id": "7nsAAAM9Bamg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opOra7BL9vgO"
      },
      "source": [
        "# Clean tweets\n",
        "num_tweets = davidson_df[\"tweet\"].size\n",
        "clean_tweet = []\n",
        "for i in range(0,num_tweets):\n",
        "    clean_tweet.append(tweet_processing(davidson_df[\"tweet\"][i]))\n",
        "davidson_df[\"tweet\"] = clean_tweet\n",
        "\n",
        "# num_tweets_test = test_data[\"tweet\"].size\n",
        "# clean_tweet_test = []\n",
        "# for i in range(num_tweets, num_tweets + num_tweets_test):\n",
        "#     clean_tweet_test.append(tweet_processing(test_data[\"tweet\"][i]))\n",
        "# test_data[\"tweet\"]=clean_tweet_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trump_data = process_df_tweet(df_trump)\n",
        "# df_lgbt['combined_label'] = df_lgbt['combined_labels']\n",
        "lgbt_data = process_df_tweet(df_lgbt)\n",
        "covid_data = process_df_tweet(df_covid)"
      ],
      "metadata": {
        "id": "gkDyPleEL_7N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7MS46yNX9vgR"
      },
      "source": [
        "## Deep Learning Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxq30t5I9vgR"
      },
      "source": [
        "Let's make the data ready for training. We need to:\n",
        "\n",
        "- tokenize the sequence in words and assign each word in the vocabulary an index so we can relate it later on.\n",
        "- pad the vectorized sequence to have fixed unit\n",
        "- split the dataset into 80\\% training and 20% testing without shuffling in order to have a better comparison between the models of other collegues (i.e TFIDF and Deephate)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def process_data(data, max_len=50):\n",
        "    tokenizer = Tokenizer()\n",
        "    tokenizer.fit_on_texts(data['tokenized_sents'])\n",
        "    sequences = tokenizer.texts_to_sequences(data['tokenized_sents'])\n",
        "    # Pad the sequence in order to have max length...\n",
        "    x = pad_sequences(sequences, maxlen=max_len)\n",
        "    # Make one hot encoding for labeling part. Should be in input [1 0]\n",
        "    # It is similar to doing sth like that:\n",
        "    # data['hate_speech']=0\n",
        "    # data['offensive_language']=0\n",
        "    # data['neither']=0\n",
        "    # data['hate_speech'] = np.where(data['class'] == 0, 1, 0)\n",
        "    # data['offensive_language'] = np.where(data['class'] == 1, 1, 0)\n",
        "    # data['neither'] = np.where(data['class'] == 2, 1, 0)\n",
        "    # columns=['hate_speech','offensive_language','neither']\n",
        "    # y=data[columns].values\n",
        "    y = to_categorical(data['class'], num_classes=3)\n",
        "    # splitting the Dataset into Train and Test set\n",
        "    x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=0.2,random_state=0, shuffle=False)\n",
        "    return x_train, x_test, y_train, y_test"
      ],
      "metadata": {
        "id": "DRSbNPV5Plgp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D82ATy7d9vgS"
      },
      "source": [
        "Tokenize the dataset and use this one to train the Word 2 vec model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_pad_sequence(data, max_len=50):\n",
        "    tokenizer = Tokenizer()\n",
        "    tokenizer.fit_on_texts(data['tokenized_sents'])\n",
        "    sequences = tokenizer.texts_to_sequences(data['tokenized_sents'])\n",
        "    return pad_sequences(sequences, maxlen=max_len)"
      ],
      "metadata": {
        "id": "jpQMFzJ0E-aU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HkzB-FW9vgS"
      },
      "source": [
        "davidson_df['tokenized_sents'] = davidson_df.apply(lambda column: word_tokenize(column['tweet']), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trump_data['tokenized_sents'] = trump_data.apply(lambda column: word_tokenize(column['tweet']), axis=1)\n",
        "lgbt_data['tokenized_sents'] = lgbt_data.apply(lambda column: word_tokenize(column['tweet']), axis=1)\n",
        "covid_data['tokenized_sents'] = covid_data.apply(lambda column: word_tokenize(column['tweet']), axis=1)"
      ],
      "metadata": {
        "id": "8N4BY6mhRjFU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tokenize the sequence and then we pad. Acccording to the length distribution the max number of tweets was around 30-40. Hence we set it 50 during training to accound for any edge cases."
      ],
      "metadata": {
        "id": "YaNTXbI84QjE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = process_data(davidson_df)"
      ],
      "metadata": {
        "id": "sSOfzAp4f7F6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trump_test = tokenize_pad_sequence(trump_data)\n",
        "lgbt_test = tokenize_pad_sequence(lgbt_data)\n",
        "covid_test = tokenize_pad_sequence(covid_data)"
      ],
      "metadata": {
        "id": "-tznt3GvXri5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Word Embeddings"
      ],
      "metadata": {
        "id": "Cx6gethX4Oag"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jsx8Pk5T9vgT"
      },
      "source": [
        "In KAGGLE Notebook we also made experiments with Embeddings. Here we will directly use word embeddings from Word2Vec model as this model will also be used to label controversial topics. We believe that embeddings provide more insights to the data than an actual normal one-hot encoding.\n",
        "\n",
        "In our experiments we will use WordEmbeddings to train the LSTM and BiLSTM model. To train a word2vec model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CuTOSqRD9vgT"
      },
      "source": [
        "from gensim.models import Word2Vec\n",
        "\n",
        "# define training data from the davidson (data must be tokenized)\n",
        "sentences = davidson_df['tokenized_sents']\n",
        "# train model on davdsion dataset\n",
        "model = Word2Vec(sentences, size=200, window =4, min_count=1, sg=1)\n",
        "words = list(model.wv.vocab)\n",
        "\n",
        "# Store the model somewhere so you don't have to train it every time,\n",
        "# although for davidson dataset it does not take long to train\n",
        "model.save('davidson_Word2Vec.bin')\n",
        "\n",
        "# In case we have a pretrained model we can also use that to get word embeddings\n",
        "# And you can simply load like this. Uncomment the line if needed\n",
        "# model = Word2Vec.load('davidson_Word2Vec.bin')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compare the words with other words in the dataset. For comparison cosine similarity is used between words vector representation."
      ],
      "metadata": {
        "id": "LtMVb26r0RXU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model.most_similar('lmao')"
      ],
      "metadata": {
        "id": "bwS6gHASSDoG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FB6oYaz_9vgU"
      },
      "source": [
        "Now it is time to create an embedding matrix based on the word embeddings we have."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reference: https://github.com/pinkeshbadjatiya/twitter-hatespeech\n",
        "from string import punctuation\n",
        "from collections import defaultdict\n",
        "def get_vocab(data):\n",
        "    # Processing\n",
        "    vocab, reverse_vocab = {}, {}\n",
        "    freq = defaultdict(int)\n",
        "    vocab_index = 1\n",
        "    for text in data:\n",
        "        for word in text:\n",
        "            if word not in vocab:\n",
        "                vocab[word] = vocab_index\n",
        "                reverse_vocab[vocab_index] = word       # generate reverse vocab as well\n",
        "                vocab_index += 1\n",
        "            freq[word] += 1\n",
        "    vocab['UNK'] = len(vocab) + 1\n",
        "    reverse_vocab[len(vocab)] = 'UNK'\n",
        "    return vocab, reverse_vocab, freq"
      ],
      "metadata": {
        "id": "CtJzqyixHetC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocabulary, reverse_vocabulary, frequencies = get_vocab(davidson_df['tokenized_sents'])"
      ],
      "metadata": {
        "id": "Ev-bVYLaBplu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M7XtHah59vgU",
        "outputId": "3031ffca-9b5b-4188-ce77-38ab1db09f32"
      },
      "source": [
        "vocab_size = len(vocabulary)\n",
        "print(vocab_size)\n",
        "embedding_matrix = np.zeros((vocab_size,200))\n",
        "\n",
        "for i in range(0,len(words)):\n",
        "    embedding_vector = model.wv[words[i]]\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix[i] = embedding_vector\n",
        "embedding_matrix[vocab_size-1]= np.random.normal(scale=0.6, size=(200, ))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20675\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yMbeImam9vgY"
      },
      "source": [
        "Let's define our models and later to train them\n",
        "\n",
        "Since davidson has 3 main classes we will use a softmax activation fucntion.\n",
        "Then a dense layer with 3 units is defined. The model prodcues probabilistic values and later we can compare to define what is the class that is being outputed based on a threshold value e.g 0.5 or so\n",
        "\n",
        "Categorical cross entropy loss accounts for different classes and is used instead of binary cross entropy since there are multiple labels being classified."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_lstm(vocab_size, embedding_matrix, input_size=50, embedding_size=200, trainable=True, hidden_unit=50):\n",
        "    # For davidson dataset I tried another approach.\n",
        "    # You can set trainable to False in order not to train embedding layer\n",
        "    inp = Input(shape=(input_size,))\n",
        "    if trainable is True:\n",
        "        x = Embedding(vocab_size, embedding_size, weights=[embedding_matrix], trainable=True)(inp)\n",
        "    else:\n",
        "        x = Embedding(vocab_size,200,weights=[embedding_matrix], trainable=False)(inp)\n",
        "    x = (LSTM(hidden_unit, return_sequences=True, dropout=0.2, recurrent_dropout=0.4))(x)\n",
        "    x = GlobalMaxPool1D()(x)\n",
        "    x = Dense(50)(x)\n",
        "    x = LeakyReLU(alpha=0.02)(x)\n",
        "    x = Dropout(0.2)(x)\n",
        "    x = Dense(3, activation='softmax')(x)\n",
        "    x = LeakyReLU(alpha=0.02)(x)\n",
        "    model = Model(inputs=inp, outputs=x)\n",
        "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    model.summary()\n",
        "    return model"
      ],
      "metadata": {
        "id": "zPtXHVgf5jaF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_bilstm(vocab_size, embedding_matrix, input_size=50, embedding_size=200, trainable=True, hidden_unit=50):\n",
        "    # For davidson dataset I tried another approach.\n",
        "    inp_bi = Input(shape=(input_size,))\n",
        "    if trainable is True:\n",
        "        x_bi = Embedding(vocab_size,embedding_size,weights=[embedding_matrix], trainable=True)(inp_bi)\n",
        "    else:\n",
        "        x_bi = Embedding(vocab_size,200,weights=[embedding_matrix], trainable=False)(inp_bi)\n",
        "    x_bi = Bidirectional(LSTM(hidden_unit, return_sequences=True, dropout=0.2, recurrent_dropout=0.4))(x_bi)\n",
        "    x_bi = GlobalMaxPool1D()(x_bi)\n",
        "    x_bi = Dense(50)(x_bi)\n",
        "    x_bi=LeakyReLU(alpha=0.02)(x_bi)\n",
        "    x_bi = Dropout(0.2)(x_bi)\n",
        "    x_bi = Dense(3, activation='softmax')(x_bi)\n",
        "    # x_bi=LeakyReLU(alpha=0.02)(x_bi)\n",
        "    model_bi = Model(inputs=inp_bi, outputs=x_bi)\n",
        "    model_bi.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    model_bi.summary()\n",
        "    return model_bi"
      ],
      "metadata": {
        "id": "8-FH1UMX7R8A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can define our training pipeline"
      ],
      "metadata": {
        "id": "UuniRuzw-WP4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import class_weight\n",
        "def train_model(model, x_train, y_train, x_test, y_test, x_val=None, y_val=None, set_batch_size=64, num_epochs=10, scale_loss=False, early_stopping=True):\n",
        "    # Stop if the model overfits...\n",
        "    if early_stopping is True:\n",
        "        stop = EarlyStopping(\n",
        "            monitor='val_accuracy',\n",
        "            mode='max',\n",
        "            patience=5\n",
        "        )\n",
        "    # Save model logs during training to the specific folder\n",
        "    checkpoint= ModelCheckpoint(\n",
        "        filepath='./',\n",
        "        save_weights_only=True,\n",
        "        monitor='val_accuracy',\n",
        "        mode='max',\n",
        "        save_best_only=True\n",
        "    )\n",
        "    # Lets specify some weights for our class function\n",
        "    # since the dataset is not really balanced and may cause overfitting\n",
        "    class_weights = None\n",
        "    if scale_loss is True:\n",
        "        class_weights = {}\n",
        "        class_weights[0] = np.where(y_train == 0)[0].shape[0]/float(len(y_train))\n",
        "        class_weights[1] = np.where(y_train == 1)[0].shape[0]/float(len(y_train))\n",
        "        class_weights[2] = np.where(y_train == 2)[0].shape[0]/float(len(y_train))\n",
        "    callback = []\n",
        "    if early_stopping is True:\n",
        "        callback = [stop, checkpoint]\n",
        "    else:\n",
        "        callaback = [checkpoint]\n",
        "    # training the model\n",
        "    history = model.fit(x_train,\n",
        "                        y_train,\n",
        "                        class_weight=class_weights,\n",
        "                        validation_split=0.2,\n",
        "                        epochs=num_epochs,\n",
        "                        batch_size=set_batch_size,\n",
        "                        callbacks=callback)\n",
        "    # Visualizing training accuracy and loss per epoch to eliminate the potential threat of overfitting\n",
        "    plt.figure(figsize=(12, 8))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(np.arange(len(history.history['accuracy'])), history.history['accuracy'], 'b', label='training acc')\n",
        "    plt.plot(np.arange(len(history.history['accuracy'])), history.history['val_accuracy'], 'c', label='val acc')\n",
        "    plt.title('Accuracy Plot')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.legend(loc='best')\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(np.arange(len(history.history['accuracy'])), history.history['loss'], 'r', label='training loss')\n",
        "    plt.plot(np.arange(len(history.history['accuracy'])), history.history['val_loss'], 'g', label='val loss')\n",
        "    plt.legend(loc='best')\n",
        "    plt.title('Loss Plot')\n",
        "    plt.ylabel('Error/Loss')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.show()\n",
        "    # Evaluate the model on the test set and check the accuracy\n",
        "    evaluation = model.evaluate(x_test, y_test)\n",
        "    # predicting and getting accuracy\n",
        "    y_pred = model.predict(x_test)\n",
        "    y_pred = np.argmax(y_pred, axis=1)\n",
        "    # y_pred = [1 if prob==True else 0 for prob in y_pred]\n",
        "    print(classification_report(np.argmax(y_test, axis=1), y_pred))\n",
        "    precision_bi, recall_bi, fscore_bi, support_bi = precision_recall_fscore_support(np.argmax(y_test, axis=1), y_pred)\n",
        "    print('\\nAccuracy', accuracy_score(np.argmax(y_test, axis=1),y_pred))\n",
        "    print('LSTM: non hate - hate')\n",
        "    print('precision: {}'.format(precision_bi))\n",
        "    print('recall: {}'.format(recall_bi))\n",
        "    print('fscore: {}'.format(fscore_bi))\n",
        "    print('support: {}'.format(support_bi))\n",
        "    # getting confusion matrix\n",
        "    print('\\nConfusion Matrix: \\n', confusion_matrix(np.argmax(y_test, axis=1), y_pred))\n",
        "    return model"
      ],
      "metadata": {
        "id": "BQJ5UTbUaxVG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Training Results and some experiments\n",
        "> Disclaimer: Notice that the results can change due to different number of parameter thay you may use. We have used different batch sizes and different epoch counts. Here are just samples from the training"
      ],
      "metadata": {
        "id": "m-IxjHvvE0xM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_model = get_lstm(vocab_size, embedding_matrix=embedding_matrix, input_size=x_train.shape[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_G-iGYbBgE5",
        "outputId": "9336e3a4-4337-4d84-802c-e9f02083321e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_4 (InputLayer)        [(None, 50)]              0         \n",
            "                                                                 \n",
            " embedding_3 (Embedding)     (None, 50, 200)           4135000   \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 50, 50)            50200     \n",
            "                                                                 \n",
            " global_max_pooling1d_3 (Glo  (None, 50)               0         \n",
            " balMaxPooling1D)                                                \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 50)                2550      \n",
            "                                                                 \n",
            " leaky_re_lu_4 (LeakyReLU)   (None, 50)                0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 50)                0         \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 3)                 153       \n",
            "                                                                 \n",
            " leaky_re_lu_5 (LeakyReLU)   (None, 3)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,187,903\n",
            "Trainable params: 4,187,903\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_model_trained = train_model(lstm_model,\n",
        "                                 x_train,\n",
        "                                 y_train,\n",
        "                                 x_test,\n",
        "                                 y_test,\n",
        "                                 set_batch_size=256,\n",
        "                                 scale_loss=False,\n",
        "                                 num_epochs=6)\n",
        "#saving model\n",
        "lstm_model_trained.save('model_lstm.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GCXkROVcCoYL",
        "outputId": "e1d19d6d-6bff-4ade-e583-fa7f3723e042"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/6\n",
            "62/62 [==============================] - 29s 416ms/step - loss: 0.7208 - accuracy: 0.7576 - val_loss: 0.6409 - val_accuracy: 0.7857\n",
            "Epoch 2/6\n",
            "62/62 [==============================] - 25s 397ms/step - loss: 0.6710 - accuracy: 0.7656 - val_loss: 0.5164 - val_accuracy: 0.8379\n",
            "Epoch 3/6\n",
            "62/62 [==============================] - 25s 401ms/step - loss: 0.4674 - accuracy: 0.8445 - val_loss: 0.3096 - val_accuracy: 0.9037\n",
            "Epoch 4/6\n",
            "62/62 [==============================] - 24s 394ms/step - loss: 0.3104 - accuracy: 0.8968 - val_loss: 0.2923 - val_accuracy: 0.9087\n",
            "Epoch 5/6\n",
            "62/62 [==============================] - 25s 402ms/step - loss: 0.2507 - accuracy: 0.9144 - val_loss: 0.3157 - val_accuracy: 0.9057\n",
            "Epoch 6/6\n",
            "62/62 [==============================] - 24s 393ms/step - loss: 0.2112 - accuracy: 0.9258 - val_loss: 0.3472 - val_accuracy: 0.8956\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "155/155 [==============================] - 2s 15ms/step - loss: 0.3805 - accuracy: 0.8796\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.15      0.11      0.12       234\n",
            "           1       0.93      0.95      0.94      3928\n",
            "           2       0.79      0.75      0.77       795\n",
            "\n",
            "    accuracy                           0.88      4957\n",
            "   macro avg       0.62      0.60      0.61      4957\n",
            "weighted avg       0.87      0.88      0.87      4957\n",
            "\n",
            "\n",
            "Accuracy 0.8795642525721202\n",
            "LSTM: non hate - hate\n",
            "precision: [0.14792899 0.9271556  0.78856383]\n",
            "recall: [0.10683761 0.95264766 0.74591195]\n",
            "fscore: [0.12406948 0.93972878 0.76664512]\n",
            "support: [ 234 3928  795]\n",
            "\n",
            "Confusion Matrix: \n",
            " [[  25  168   41]\n",
            " [  68 3742  118]\n",
            " [  76  126  593]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wZJpoHbu9vgY",
        "outputId": "eb6c14ae-3ffe-4227-bb74-73213e5190e7"
      },
      "source": [
        "# vocab_size = lunique words in the corpus genrated from word2vec genism\n",
        "lstm_embedding_model = get_lstm(vocab_size, embedding_matrix=embedding_matrix, input_size=x_train.shape[1], trainable=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 50)]              0         \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, 50, 200)           4135000   \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 50, 50)            50200     \n",
            "                                                                 \n",
            " global_max_pooling1d (Globa  (None, 50)               0         \n",
            " lMaxPooling1D)                                                  \n",
            "                                                                 \n",
            " dense (Dense)               (None, 50)                2550      \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 50)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 50)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 153       \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 3)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,187,903\n",
            "Trainable params: 52,903\n",
            "Non-trainable params: 4,135,000\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9WLyIBm9vgY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "992cbcb1-7d3a-497a-97b1-37888e440bfe"
      },
      "source": [
        "lstm_model_embbedding_trained = train_model(lstm_embedding_model,\n",
        "                                 x_train,\n",
        "                                 y_train,\n",
        "                                 x_test,\n",
        "                                 y_test,\n",
        "                                 set_batch_size=32,\n",
        "                                 scale_loss=False,\n",
        "                                 num_epochs=100,\n",
        "                                 early_stopping=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "496/496 [==============================] - 45s 85ms/step - loss: 0.6853 - accuracy: 0.7653 - val_loss: 0.5953 - val_accuracy: 0.7920\n",
            "Epoch 2/100\n",
            "496/496 [==============================] - 43s 87ms/step - loss: 0.6710 - accuracy: 0.7686 - val_loss: 0.5913 - val_accuracy: 0.7950\n",
            "Epoch 3/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.6687 - accuracy: 0.7672 - val_loss: 0.5861 - val_accuracy: 0.7955\n",
            "Epoch 4/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.6659 - accuracy: 0.7686 - val_loss: 0.5988 - val_accuracy: 0.7965\n",
            "Epoch 5/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6638 - accuracy: 0.7697 - val_loss: 0.5933 - val_accuracy: 0.7882\n",
            "Epoch 6/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6644 - accuracy: 0.7703 - val_loss: 0.5856 - val_accuracy: 0.7932\n",
            "Epoch 7/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.6631 - accuracy: 0.7705 - val_loss: 0.5767 - val_accuracy: 0.7985\n",
            "Epoch 8/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6612 - accuracy: 0.7691 - val_loss: 0.5803 - val_accuracy: 0.7963\n",
            "Epoch 9/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.6564 - accuracy: 0.7701 - val_loss: 0.5653 - val_accuracy: 0.7965\n",
            "Epoch 10/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.6541 - accuracy: 0.7693 - val_loss: 0.5578 - val_accuracy: 0.7988\n",
            "Epoch 11/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6475 - accuracy: 0.7701 - val_loss: 0.5528 - val_accuracy: 0.8023\n",
            "Epoch 12/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6415 - accuracy: 0.7701 - val_loss: 0.5393 - val_accuracy: 0.8001\n",
            "Epoch 13/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.6378 - accuracy: 0.7721 - val_loss: 0.5294 - val_accuracy: 0.8053\n",
            "Epoch 14/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6339 - accuracy: 0.7714 - val_loss: 0.5291 - val_accuracy: 0.8036\n",
            "Epoch 15/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6293 - accuracy: 0.7723 - val_loss: 0.5234 - val_accuracy: 0.8021\n",
            "Epoch 16/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6269 - accuracy: 0.7728 - val_loss: 0.5341 - val_accuracy: 0.8048\n",
            "Epoch 17/100\n",
            "496/496 [==============================] - 44s 88ms/step - loss: 0.6237 - accuracy: 0.7734 - val_loss: 0.5143 - val_accuracy: 0.8099\n",
            "Epoch 18/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6185 - accuracy: 0.7767 - val_loss: 0.5066 - val_accuracy: 0.8064\n",
            "Epoch 19/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.6185 - accuracy: 0.7777 - val_loss: 0.5085 - val_accuracy: 0.8096\n",
            "Epoch 20/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6123 - accuracy: 0.7806 - val_loss: 0.5011 - val_accuracy: 0.8273\n",
            "Epoch 21/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6074 - accuracy: 0.7811 - val_loss: 0.4943 - val_accuracy: 0.8313\n",
            "Epoch 22/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.6069 - accuracy: 0.7808 - val_loss: 0.4862 - val_accuracy: 0.8308\n",
            "Epoch 23/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.6021 - accuracy: 0.7861 - val_loss: 0.4878 - val_accuracy: 0.8326\n",
            "Epoch 24/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.6007 - accuracy: 0.7842 - val_loss: 0.4875 - val_accuracy: 0.8326\n",
            "Epoch 25/100\n",
            "496/496 [==============================] - 41s 84ms/step - loss: 0.5949 - accuracy: 0.7849 - val_loss: 0.4818 - val_accuracy: 0.8273\n",
            "Epoch 26/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.5951 - accuracy: 0.7852 - val_loss: 0.4838 - val_accuracy: 0.8222\n",
            "Epoch 27/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.5882 - accuracy: 0.7874 - val_loss: 0.4743 - val_accuracy: 0.8379\n",
            "Epoch 28/100\n",
            "496/496 [==============================] - 41s 84ms/step - loss: 0.5829 - accuracy: 0.7904 - val_loss: 0.4633 - val_accuracy: 0.8303\n",
            "Epoch 29/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5805 - accuracy: 0.7907 - val_loss: 0.4625 - val_accuracy: 0.8442\n",
            "Epoch 30/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.5725 - accuracy: 0.7919 - val_loss: 0.4531 - val_accuracy: 0.8485\n",
            "Epoch 31/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5717 - accuracy: 0.7936 - val_loss: 0.4655 - val_accuracy: 0.8459\n",
            "Epoch 32/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.5662 - accuracy: 0.7914 - val_loss: 0.4470 - val_accuracy: 0.8480\n",
            "Epoch 33/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5627 - accuracy: 0.7968 - val_loss: 0.4485 - val_accuracy: 0.8469\n",
            "Epoch 34/100\n",
            "496/496 [==============================] - 41s 84ms/step - loss: 0.5609 - accuracy: 0.7945 - val_loss: 0.4372 - val_accuracy: 0.8502\n",
            "Epoch 35/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.5603 - accuracy: 0.7962 - val_loss: 0.4434 - val_accuracy: 0.8427\n",
            "Epoch 36/100\n",
            "496/496 [==============================] - 41s 84ms/step - loss: 0.5575 - accuracy: 0.7991 - val_loss: 0.4463 - val_accuracy: 0.8522\n",
            "Epoch 37/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.5552 - accuracy: 0.8004 - val_loss: 0.4508 - val_accuracy: 0.8535\n",
            "Epoch 38/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.5520 - accuracy: 0.7991 - val_loss: 0.4379 - val_accuracy: 0.8492\n",
            "Epoch 39/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.5489 - accuracy: 0.8023 - val_loss: 0.4278 - val_accuracy: 0.8538\n",
            "Epoch 40/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.5450 - accuracy: 0.8009 - val_loss: 0.4409 - val_accuracy: 0.8555\n",
            "Epoch 41/100\n",
            "496/496 [==============================] - 41s 84ms/step - loss: 0.5468 - accuracy: 0.7997 - val_loss: 0.4472 - val_accuracy: 0.8530\n",
            "Epoch 42/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.5485 - accuracy: 0.7988 - val_loss: 0.4276 - val_accuracy: 0.8545\n",
            "Epoch 43/100\n",
            "496/496 [==============================] - 41s 82ms/step - loss: 0.5469 - accuracy: 0.7987 - val_loss: 0.4390 - val_accuracy: 0.8548\n",
            "Epoch 44/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.5419 - accuracy: 0.8027 - val_loss: 0.4273 - val_accuracy: 0.8535\n",
            "Epoch 45/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5380 - accuracy: 0.8037 - val_loss: 0.4180 - val_accuracy: 0.8543\n",
            "Epoch 46/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5396 - accuracy: 0.7987 - val_loss: 0.4213 - val_accuracy: 0.8545\n",
            "Epoch 47/100\n",
            "496/496 [==============================] - 41s 84ms/step - loss: 0.5391 - accuracy: 0.8038 - val_loss: 0.4217 - val_accuracy: 0.8500\n",
            "Epoch 48/100\n",
            "496/496 [==============================] - 44s 89ms/step - loss: 0.5344 - accuracy: 0.8021 - val_loss: 0.4203 - val_accuracy: 0.8495\n",
            "Epoch 49/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5379 - accuracy: 0.8029 - val_loss: 0.4301 - val_accuracy: 0.8580\n",
            "Epoch 50/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5355 - accuracy: 0.8013 - val_loss: 0.4144 - val_accuracy: 0.8522\n",
            "Epoch 51/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5319 - accuracy: 0.8023 - val_loss: 0.4158 - val_accuracy: 0.8591\n",
            "Epoch 52/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5294 - accuracy: 0.8061 - val_loss: 0.4105 - val_accuracy: 0.8543\n",
            "Epoch 53/100\n",
            "496/496 [==============================] - 41s 84ms/step - loss: 0.5289 - accuracy: 0.8072 - val_loss: 0.4071 - val_accuracy: 0.8570\n",
            "Epoch 54/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5257 - accuracy: 0.8061 - val_loss: 0.4393 - val_accuracy: 0.8348\n",
            "Epoch 55/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5271 - accuracy: 0.8046 - val_loss: 0.4079 - val_accuracy: 0.8591\n",
            "Epoch 56/100\n",
            "496/496 [==============================] - 41s 84ms/step - loss: 0.5258 - accuracy: 0.8045 - val_loss: 0.4126 - val_accuracy: 0.8583\n",
            "Epoch 57/100\n",
            "496/496 [==============================] - 41s 84ms/step - loss: 0.5243 - accuracy: 0.8084 - val_loss: 0.4180 - val_accuracy: 0.8583\n",
            "Epoch 58/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.5255 - accuracy: 0.8067 - val_loss: 0.4164 - val_accuracy: 0.8583\n",
            "Epoch 59/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5254 - accuracy: 0.8064 - val_loss: 0.4106 - val_accuracy: 0.8596\n",
            "Epoch 60/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5219 - accuracy: 0.8054 - val_loss: 0.4140 - val_accuracy: 0.8548\n",
            "Epoch 61/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5223 - accuracy: 0.8081 - val_loss: 0.4129 - val_accuracy: 0.8588\n",
            "Epoch 62/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.5222 - accuracy: 0.8074 - val_loss: 0.4272 - val_accuracy: 0.8580\n",
            "Epoch 63/100\n",
            "496/496 [==============================] - 44s 88ms/step - loss: 0.5173 - accuracy: 0.8088 - val_loss: 0.4077 - val_accuracy: 0.8580\n",
            "Epoch 64/100\n",
            "496/496 [==============================] - 41s 83ms/step - loss: 0.5207 - accuracy: 0.8100 - val_loss: 0.3990 - val_accuracy: 0.8613\n",
            "Epoch 65/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5176 - accuracy: 0.8098 - val_loss: 0.3945 - val_accuracy: 0.8613\n",
            "Epoch 66/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5146 - accuracy: 0.8101 - val_loss: 0.4063 - val_accuracy: 0.8540\n",
            "Epoch 67/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5130 - accuracy: 0.8092 - val_loss: 0.3924 - val_accuracy: 0.8601\n",
            "Epoch 68/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5166 - accuracy: 0.8084 - val_loss: 0.4042 - val_accuracy: 0.8583\n",
            "Epoch 69/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5095 - accuracy: 0.8088 - val_loss: 0.4015 - val_accuracy: 0.8633\n",
            "Epoch 70/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5137 - accuracy: 0.8092 - val_loss: 0.3950 - val_accuracy: 0.8611\n",
            "Epoch 71/100\n",
            "496/496 [==============================] - 43s 86ms/step - loss: 0.5111 - accuracy: 0.8107 - val_loss: 0.4004 - val_accuracy: 0.8628\n",
            "Epoch 72/100\n",
            "496/496 [==============================] - 43s 86ms/step - loss: 0.5111 - accuracy: 0.8104 - val_loss: 0.4016 - val_accuracy: 0.8616\n",
            "Epoch 73/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5094 - accuracy: 0.8078 - val_loss: 0.4010 - val_accuracy: 0.8621\n",
            "Epoch 74/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5098 - accuracy: 0.8094 - val_loss: 0.4062 - val_accuracy: 0.8535\n",
            "Epoch 75/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.5084 - accuracy: 0.8117 - val_loss: 0.4017 - val_accuracy: 0.8548\n",
            "Epoch 76/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5044 - accuracy: 0.8112 - val_loss: 0.4006 - val_accuracy: 0.8669\n",
            "Epoch 77/100\n",
            "496/496 [==============================] - 43s 87ms/step - loss: 0.5069 - accuracy: 0.8122 - val_loss: 0.3956 - val_accuracy: 0.8638\n",
            "Epoch 78/100\n",
            "496/496 [==============================] - 43s 88ms/step - loss: 0.5042 - accuracy: 0.8120 - val_loss: 0.3968 - val_accuracy: 0.8543\n",
            "Epoch 79/100\n",
            "496/496 [==============================] - 43s 87ms/step - loss: 0.5075 - accuracy: 0.8080 - val_loss: 0.3948 - val_accuracy: 0.8646\n",
            "Epoch 80/100\n",
            "496/496 [==============================] - 44s 88ms/step - loss: 0.5029 - accuracy: 0.8132 - val_loss: 0.3899 - val_accuracy: 0.8628\n",
            "Epoch 81/100\n",
            "496/496 [==============================] - 45s 90ms/step - loss: 0.5024 - accuracy: 0.8127 - val_loss: 0.4001 - val_accuracy: 0.8656\n",
            "Epoch 82/100\n",
            "496/496 [==============================] - 43s 86ms/step - loss: 0.5025 - accuracy: 0.8120 - val_loss: 0.3833 - val_accuracy: 0.8649\n",
            "Epoch 83/100\n",
            "496/496 [==============================] - 42s 84ms/step - loss: 0.4988 - accuracy: 0.8142 - val_loss: 0.3925 - val_accuracy: 0.8621\n",
            "Epoch 84/100\n",
            "496/496 [==============================] - 42s 86ms/step - loss: 0.4981 - accuracy: 0.8152 - val_loss: 0.4061 - val_accuracy: 0.8638\n",
            "Epoch 85/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5008 - accuracy: 0.8135 - val_loss: 0.3863 - val_accuracy: 0.8616\n",
            "Epoch 86/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5003 - accuracy: 0.8118 - val_loss: 0.3810 - val_accuracy: 0.8666\n",
            "Epoch 87/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.4992 - accuracy: 0.8146 - val_loss: 0.3876 - val_accuracy: 0.8707\n",
            "Epoch 88/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.5017 - accuracy: 0.8122 - val_loss: 0.3885 - val_accuracy: 0.8588\n",
            "Epoch 89/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.4982 - accuracy: 0.8116 - val_loss: 0.3878 - val_accuracy: 0.8669\n",
            "Epoch 90/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.4961 - accuracy: 0.8162 - val_loss: 0.3822 - val_accuracy: 0.8643\n",
            "Epoch 91/100\n",
            "496/496 [==============================] - 43s 86ms/step - loss: 0.4969 - accuracy: 0.8177 - val_loss: 0.3876 - val_accuracy: 0.8611\n",
            "Epoch 92/100\n",
            "496/496 [==============================] - 42s 86ms/step - loss: 0.4964 - accuracy: 0.8148 - val_loss: 0.3848 - val_accuracy: 0.8618\n",
            "Epoch 93/100\n",
            "496/496 [==============================] - 43s 86ms/step - loss: 0.4960 - accuracy: 0.8143 - val_loss: 0.3876 - val_accuracy: 0.8679\n",
            "Epoch 94/100\n",
            "496/496 [==============================] - 43s 87ms/step - loss: 0.4977 - accuracy: 0.8149 - val_loss: 0.3871 - val_accuracy: 0.8643\n",
            "Epoch 95/100\n",
            "496/496 [==============================] - 43s 86ms/step - loss: 0.4922 - accuracy: 0.8171 - val_loss: 0.3901 - val_accuracy: 0.8596\n",
            "Epoch 96/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.4952 - accuracy: 0.8154 - val_loss: 0.3813 - val_accuracy: 0.8671\n",
            "Epoch 97/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.4915 - accuracy: 0.8165 - val_loss: 0.4020 - val_accuracy: 0.8656\n",
            "Epoch 98/100\n",
            "496/496 [==============================] - 43s 87ms/step - loss: 0.4906 - accuracy: 0.8160 - val_loss: 0.3920 - val_accuracy: 0.8613\n",
            "Epoch 99/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.4924 - accuracy: 0.8153 - val_loss: 0.3910 - val_accuracy: 0.8628\n",
            "Epoch 100/100\n",
            "496/496 [==============================] - 42s 85ms/step - loss: 0.4872 - accuracy: 0.8189 - val_loss: 0.3839 - val_accuracy: 0.8669\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "155/155 [==============================] - 3s 16ms/step - loss: 0.4330 - accuracy: 0.8396\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       234\n",
            "           1       0.89      0.93      0.91      3928\n",
            "           2       0.60      0.63      0.61       795\n",
            "\n",
            "    accuracy                           0.84      4957\n",
            "   macro avg       0.50      0.52      0.51      4957\n",
            "weighted avg       0.80      0.84      0.82      4957\n",
            "\n",
            "\n",
            "Accuracy 0.8396207383498083\n",
            "LSTM: non hate - hate\n",
            "precision: [0.         0.88735465 0.602657  ]\n",
            "recall: [0.         0.93253564 0.62767296]\n",
            "fscore: [0.         0.90938431 0.61491066]\n",
            "support: [ 234 3928  795]\n",
            "\n",
            "Confusion Matrix: \n",
            " [[   0  169   65]\n",
            " [   1 3663  264]\n",
            " [   0  296  499]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This is an old run of the method training LSTM without training embedding layer.\n",
        "# It achieved around 90% accuracy and the training has a tendency to go down."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "LMq7dKMhVVws",
        "outputId": "cf9e1d7a-3904-4fc2-da3e-aa8aa4e465f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-3gwMf49vgY"
      },
      "source": [
        "__Training a BiLSTM model__"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71b0a58c-d5cb-44cc-fd75-00aaa5dee595",
        "id": "zZ9VtikNjSJy"
      },
      "source": [
        "# vocab_size = lunique words in the corpus genrated from word2vec genism\n",
        "bilstm_model = get_bilstm(vocab_size, embedding_matrix=embedding_matrix, input_size=x_train.shape[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 50)]              0         \n",
            "                                                                 \n",
            " embedding_2 (Embedding)     (None, 50, 200)           4135000   \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 50, 100)          100400    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " global_max_pooling1d_2 (Glo  (None, 100)              0         \n",
            " balMaxPooling1D)                                                \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 50)                5050      \n",
            "                                                                 \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 50)                0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 50)                0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 3)                 153       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,240,603\n",
            "Trainable params: 4,240,603\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f94b6597-1116-494a-9872-79c3f89c8c6b",
        "id": "fBPkbgKAjSJz"
      },
      "source": [
        "bilstm_model_trained = train_model(bilstm_model,\n",
        "                                 x_train,\n",
        "                                 y_train,\n",
        "                                 x_test,\n",
        "                                 y_test,\n",
        "                                 set_batch_size=254,\n",
        "                                 scale_loss=False,\n",
        "                                 num_epochs=6,\n",
        "                                 early_stopping=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/6\n",
            "63/63 [==============================] - 55s 757ms/step - loss: 0.7009 - accuracy: 0.7641 - val_loss: 0.5935 - val_accuracy: 0.7857\n",
            "Epoch 2/6\n",
            "63/63 [==============================] - 48s 755ms/step - loss: 0.5101 - accuracy: 0.8164 - val_loss: 0.3394 - val_accuracy: 0.9085\n",
            "Epoch 3/6\n",
            "63/63 [==============================] - 48s 767ms/step - loss: 0.3620 - accuracy: 0.8830 - val_loss: 0.3042 - val_accuracy: 0.9027\n",
            "Epoch 4/6\n",
            "63/63 [==============================] - 48s 756ms/step - loss: 0.2952 - accuracy: 0.9011 - val_loss: 0.2784 - val_accuracy: 0.9097\n",
            "Epoch 5/6\n",
            "63/63 [==============================] - 47s 742ms/step - loss: 0.2471 - accuracy: 0.9177 - val_loss: 0.2722 - val_accuracy: 0.9105\n",
            "Epoch 6/6\n",
            "63/63 [==============================] - 47s 743ms/step - loss: 0.1702 - accuracy: 0.9421 - val_loss: 0.2849 - val_accuracy: 0.9105\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "155/155 [==============================] - 4s 27ms/step - loss: 0.3373 - accuracy: 0.8925\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.37      0.23      0.28       234\n",
            "           1       0.94      0.94      0.94      3928\n",
            "           2       0.77      0.84      0.81       795\n",
            "\n",
            "    accuracy                           0.89      4957\n",
            "   macro avg       0.69      0.67      0.68      4957\n",
            "weighted avg       0.88      0.89      0.89      4957\n",
            "\n",
            "\n",
            "Accuracy 0.8924752874722615\n",
            "LSTM: non hate - hate\n",
            "precision: [0.36551724 0.93746835 0.774942  ]\n",
            "recall: [0.22649573 0.94271894 0.84025157]\n",
            "fscore: [0.27968338 0.94008632 0.8062764 ]\n",
            "support: [ 234 3928  795]\n",
            "\n",
            "Confusion Matrix: \n",
            " [[  53  135   46]\n",
            " [  77 3703  148]\n",
            " [  15  112  668]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# bilstm_model_trained.save('model_bilstm.h5')"
      ],
      "metadata": {
        "id": "eM5xVo7bPO2t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# This is an old run of the method training BiLSTM without training embedding layer.\n",
        "# It achieved around 90% accuracy and the training has a tendency to go down."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "QjOWUtT7ZSYN",
        "outputId": "f9fb66ea-5985-41fe-d7c8-bd472ed9dd0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's also test the bilstm without changing the embedding weights for davidson"
      ],
      "metadata": {
        "id": "DrSPQXdjxPhK"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb03c575-6f3d-4230-cb53-e0f58d4f2f65",
        "id": "i_payLphxNnR"
      },
      "source": [
        "# vocab_size = lunique words in the corpus genrated from word2vec genism\n",
        "bilstm_model_embeddings = get_bilstm(vocab_size, embedding_matrix=embedding_matrix, input_size=x_train.shape[1], trainable=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 50)]              0         \n",
            "                                                                 \n",
            " embedding_4 (Embedding)     (None, 50, 200)           4135000   \n",
            "                                                                 \n",
            " bidirectional_2 (Bidirectio  (None, 50, 100)          100400    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " global_max_pooling1d_4 (Glo  (None, 100)              0         \n",
            " balMaxPooling1D)                                                \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 50)                5050      \n",
            "                                                                 \n",
            " leaky_re_lu_6 (LeakyReLU)   (None, 50)                0         \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 50)                0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 3)                 153       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,240,603\n",
            "Trainable params: 105,603\n",
            "Non-trainable params: 4,135,000\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "65523742-117e-4355-81f7-dfbd8e87deb3",
        "id": "7Z0qrRK8xNnT"
      },
      "source": [
        "bilstm_model_embedding_trained = train_model(bilstm_model_embeddings,\n",
        "                                 x_train,\n",
        "                                 y_train,\n",
        "                                 x_test,\n",
        "                                 y_test,\n",
        "                                 set_batch_size=256,\n",
        "                                 scale_loss=False,\n",
        "                                 num_epochs=100,\n",
        "                                 early_stopping=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "62/62 [==============================] - 40s 648ms/step - loss: 0.6618 - accuracy: 0.7689 - val_loss: 0.5894 - val_accuracy: 0.7945\n",
            "Epoch 2/100\n",
            "62/62 [==============================] - 40s 641ms/step - loss: 0.6609 - accuracy: 0.7701 - val_loss: 0.5894 - val_accuracy: 0.7978\n",
            "Epoch 3/100\n",
            "62/62 [==============================] - 40s 647ms/step - loss: 0.6591 - accuracy: 0.7710 - val_loss: 0.5853 - val_accuracy: 0.7945\n",
            "Epoch 4/100\n",
            "62/62 [==============================] - 40s 648ms/step - loss: 0.6576 - accuracy: 0.7709 - val_loss: 0.5876 - val_accuracy: 0.7970\n",
            "Epoch 5/100\n",
            "62/62 [==============================] - 40s 638ms/step - loss: 0.6536 - accuracy: 0.7710 - val_loss: 0.5717 - val_accuracy: 0.7968\n",
            "Epoch 6/100\n",
            "62/62 [==============================] - 40s 648ms/step - loss: 0.6492 - accuracy: 0.7726 - val_loss: 0.5612 - val_accuracy: 0.7995\n",
            "Epoch 7/100\n",
            "62/62 [==============================] - 40s 641ms/step - loss: 0.6454 - accuracy: 0.7707 - val_loss: 0.5755 - val_accuracy: 0.7995\n",
            "Epoch 8/100\n",
            "62/62 [==============================] - 40s 646ms/step - loss: 0.6414 - accuracy: 0.7722 - val_loss: 0.5529 - val_accuracy: 0.8013\n",
            "Epoch 9/100\n",
            "62/62 [==============================] - 40s 640ms/step - loss: 0.6367 - accuracy: 0.7741 - val_loss: 0.5507 - val_accuracy: 0.8018\n",
            "Epoch 10/100\n",
            "62/62 [==============================] - 41s 662ms/step - loss: 0.6343 - accuracy: 0.7749 - val_loss: 0.5382 - val_accuracy: 0.8071\n",
            "Epoch 11/100\n",
            "62/62 [==============================] - 40s 653ms/step - loss: 0.6321 - accuracy: 0.7752 - val_loss: 0.5336 - val_accuracy: 0.8048\n",
            "Epoch 12/100\n",
            "62/62 [==============================] - 40s 646ms/step - loss: 0.6295 - accuracy: 0.7766 - val_loss: 0.5330 - val_accuracy: 0.8106\n",
            "Epoch 13/100\n",
            "62/62 [==============================] - 40s 645ms/step - loss: 0.6287 - accuracy: 0.7782 - val_loss: 0.5373 - val_accuracy: 0.8157\n",
            "Epoch 14/100\n",
            "62/62 [==============================] - 40s 643ms/step - loss: 0.6264 - accuracy: 0.7772 - val_loss: 0.5253 - val_accuracy: 0.8192\n",
            "Epoch 15/100\n",
            "62/62 [==============================] - 40s 641ms/step - loss: 0.6237 - accuracy: 0.7774 - val_loss: 0.5189 - val_accuracy: 0.8142\n",
            "Epoch 16/100\n",
            "62/62 [==============================] - 40s 645ms/step - loss: 0.6236 - accuracy: 0.7776 - val_loss: 0.5221 - val_accuracy: 0.8174\n",
            "Epoch 17/100\n",
            "62/62 [==============================] - 39s 635ms/step - loss: 0.6181 - accuracy: 0.7816 - val_loss: 0.5264 - val_accuracy: 0.8139\n",
            "Epoch 18/100\n",
            "62/62 [==============================] - 40s 652ms/step - loss: 0.6141 - accuracy: 0.7844 - val_loss: 0.5166 - val_accuracy: 0.8144\n",
            "Epoch 19/100\n",
            "62/62 [==============================] - 41s 656ms/step - loss: 0.6144 - accuracy: 0.7820 - val_loss: 0.5103 - val_accuracy: 0.8172\n",
            "Epoch 20/100\n",
            "62/62 [==============================] - 42s 671ms/step - loss: 0.6130 - accuracy: 0.7831 - val_loss: 0.5206 - val_accuracy: 0.8119\n",
            "Epoch 21/100\n",
            "62/62 [==============================] - 41s 655ms/step - loss: 0.6127 - accuracy: 0.7813 - val_loss: 0.5187 - val_accuracy: 0.8220\n",
            "Epoch 22/100\n",
            "62/62 [==============================] - 42s 675ms/step - loss: 0.6088 - accuracy: 0.7842 - val_loss: 0.5085 - val_accuracy: 0.8162\n",
            "Epoch 23/100\n",
            "62/62 [==============================] - 42s 675ms/step - loss: 0.6088 - accuracy: 0.7840 - val_loss: 0.5095 - val_accuracy: 0.8235\n",
            "Epoch 24/100\n",
            "62/62 [==============================] - 41s 663ms/step - loss: 0.6067 - accuracy: 0.7842 - val_loss: 0.5045 - val_accuracy: 0.8144\n",
            "Epoch 25/100\n",
            "62/62 [==============================] - 43s 690ms/step - loss: 0.6041 - accuracy: 0.7843 - val_loss: 0.5065 - val_accuracy: 0.8195\n",
            "Epoch 26/100\n",
            "62/62 [==============================] - 41s 660ms/step - loss: 0.6037 - accuracy: 0.7859 - val_loss: 0.4957 - val_accuracy: 0.8263\n",
            "Epoch 27/100\n",
            "62/62 [==============================] - 40s 645ms/step - loss: 0.6034 - accuracy: 0.7847 - val_loss: 0.5013 - val_accuracy: 0.8245\n",
            "Epoch 28/100\n",
            "62/62 [==============================] - 40s 645ms/step - loss: 0.6058 - accuracy: 0.7847 - val_loss: 0.5021 - val_accuracy: 0.8197\n",
            "Epoch 29/100\n",
            "62/62 [==============================] - 40s 645ms/step - loss: 0.6014 - accuracy: 0.7858 - val_loss: 0.4899 - val_accuracy: 0.8263\n",
            "Epoch 30/100\n",
            "62/62 [==============================] - 44s 707ms/step - loss: 0.5987 - accuracy: 0.7853 - val_loss: 0.5031 - val_accuracy: 0.8172\n",
            "Epoch 31/100\n",
            "62/62 [==============================] - 42s 682ms/step - loss: 0.5997 - accuracy: 0.7852 - val_loss: 0.4960 - val_accuracy: 0.8212\n",
            "Epoch 32/100\n",
            "62/62 [==============================] - 41s 659ms/step - loss: 0.5954 - accuracy: 0.7873 - val_loss: 0.5017 - val_accuracy: 0.8162\n",
            "Epoch 33/100\n",
            "62/62 [==============================] - 40s 647ms/step - loss: 0.5946 - accuracy: 0.7869 - val_loss: 0.4922 - val_accuracy: 0.8215\n",
            "Epoch 34/100\n",
            "62/62 [==============================] - 40s 651ms/step - loss: 0.5927 - accuracy: 0.7890 - val_loss: 0.4879 - val_accuracy: 0.8265\n",
            "Epoch 35/100\n",
            "62/62 [==============================] - 40s 653ms/step - loss: 0.5908 - accuracy: 0.7880 - val_loss: 0.4944 - val_accuracy: 0.8217\n",
            "Epoch 36/100\n",
            "62/62 [==============================] - 40s 644ms/step - loss: 0.5891 - accuracy: 0.7916 - val_loss: 0.4852 - val_accuracy: 0.8273\n",
            "Epoch 37/100\n",
            "62/62 [==============================] - 40s 639ms/step - loss: 0.5878 - accuracy: 0.7895 - val_loss: 0.4916 - val_accuracy: 0.8227\n",
            "Epoch 38/100\n",
            "62/62 [==============================] - 39s 636ms/step - loss: 0.5852 - accuracy: 0.7904 - val_loss: 0.4872 - val_accuracy: 0.8250\n",
            "Epoch 39/100\n",
            "62/62 [==============================] - 40s 649ms/step - loss: 0.5830 - accuracy: 0.7888 - val_loss: 0.4948 - val_accuracy: 0.8280\n",
            "Epoch 40/100\n",
            "62/62 [==============================] - 40s 646ms/step - loss: 0.5869 - accuracy: 0.7892 - val_loss: 0.4836 - val_accuracy: 0.8331\n",
            "Epoch 41/100\n",
            "62/62 [==============================] - 40s 650ms/step - loss: 0.5809 - accuracy: 0.7905 - val_loss: 0.4889 - val_accuracy: 0.8265\n",
            "Epoch 42/100\n",
            "62/62 [==============================] - 41s 655ms/step - loss: 0.5747 - accuracy: 0.7948 - val_loss: 0.4714 - val_accuracy: 0.8311\n",
            "Epoch 43/100\n",
            "62/62 [==============================] - 40s 644ms/step - loss: 0.5750 - accuracy: 0.7939 - val_loss: 0.4814 - val_accuracy: 0.8268\n",
            "Epoch 44/100\n",
            "62/62 [==============================] - 40s 650ms/step - loss: 0.5765 - accuracy: 0.7929 - val_loss: 0.4728 - val_accuracy: 0.8338\n",
            "Epoch 45/100\n",
            "62/62 [==============================] - 42s 675ms/step - loss: 0.5749 - accuracy: 0.7928 - val_loss: 0.4758 - val_accuracy: 0.8376\n",
            "Epoch 46/100\n",
            "62/62 [==============================] - 40s 651ms/step - loss: 0.5716 - accuracy: 0.7939 - val_loss: 0.4642 - val_accuracy: 0.8323\n",
            "Epoch 47/100\n",
            "62/62 [==============================] - 42s 672ms/step - loss: 0.5706 - accuracy: 0.7948 - val_loss: 0.4647 - val_accuracy: 0.8459\n",
            "Epoch 48/100\n",
            "62/62 [==============================] - 40s 652ms/step - loss: 0.5642 - accuracy: 0.7964 - val_loss: 0.4565 - val_accuracy: 0.8399\n",
            "Epoch 49/100\n",
            "62/62 [==============================] - 40s 647ms/step - loss: 0.5646 - accuracy: 0.7965 - val_loss: 0.4713 - val_accuracy: 0.8285\n",
            "Epoch 50/100\n",
            "62/62 [==============================] - 40s 644ms/step - loss: 0.5635 - accuracy: 0.7959 - val_loss: 0.4582 - val_accuracy: 0.8381\n",
            "Epoch 51/100\n",
            "62/62 [==============================] - 40s 644ms/step - loss: 0.5604 - accuracy: 0.7993 - val_loss: 0.4548 - val_accuracy: 0.8432\n",
            "Epoch 52/100\n",
            "62/62 [==============================] - 40s 644ms/step - loss: 0.5578 - accuracy: 0.8009 - val_loss: 0.4504 - val_accuracy: 0.8419\n",
            "Epoch 53/100\n",
            "62/62 [==============================] - 40s 643ms/step - loss: 0.5544 - accuracy: 0.8042 - val_loss: 0.4559 - val_accuracy: 0.8354\n",
            "Epoch 54/100\n",
            "62/62 [==============================] - 40s 644ms/step - loss: 0.5516 - accuracy: 0.8037 - val_loss: 0.4470 - val_accuracy: 0.8480\n",
            "Epoch 55/100\n",
            "62/62 [==============================] - 40s 645ms/step - loss: 0.5486 - accuracy: 0.8026 - val_loss: 0.4470 - val_accuracy: 0.8449\n",
            "Epoch 56/100\n",
            "62/62 [==============================] - 40s 644ms/step - loss: 0.5483 - accuracy: 0.8016 - val_loss: 0.4408 - val_accuracy: 0.8573\n",
            "Epoch 57/100\n",
            "62/62 [==============================] - 40s 649ms/step - loss: 0.5477 - accuracy: 0.8040 - val_loss: 0.4401 - val_accuracy: 0.8447\n",
            "Epoch 58/100\n",
            "62/62 [==============================] - 41s 658ms/step - loss: 0.5493 - accuracy: 0.8028 - val_loss: 0.4408 - val_accuracy: 0.8548\n",
            "Epoch 59/100\n",
            "62/62 [==============================] - 41s 658ms/step - loss: 0.5446 - accuracy: 0.8057 - val_loss: 0.4444 - val_accuracy: 0.8555\n",
            "Epoch 60/100\n",
            "62/62 [==============================] - 40s 643ms/step - loss: 0.5455 - accuracy: 0.8028 - val_loss: 0.4353 - val_accuracy: 0.8553\n",
            "Epoch 61/100\n",
            "62/62 [==============================] - 40s 648ms/step - loss: 0.5460 - accuracy: 0.8032 - val_loss: 0.4606 - val_accuracy: 0.8265\n",
            "Epoch 62/100\n",
            "62/62 [==============================] - 40s 649ms/step - loss: 0.5409 - accuracy: 0.8071 - val_loss: 0.4380 - val_accuracy: 0.8591\n",
            "Epoch 63/100\n",
            "62/62 [==============================] - 40s 647ms/step - loss: 0.5379 - accuracy: 0.8041 - val_loss: 0.4474 - val_accuracy: 0.8616\n",
            "Epoch 64/100\n",
            "62/62 [==============================] - 40s 651ms/step - loss: 0.5371 - accuracy: 0.8057 - val_loss: 0.4311 - val_accuracy: 0.8550\n",
            "Epoch 65/100\n",
            "62/62 [==============================] - 39s 637ms/step - loss: 0.5382 - accuracy: 0.8066 - val_loss: 0.4232 - val_accuracy: 0.8568\n",
            "Epoch 66/100\n",
            "62/62 [==============================] - 40s 653ms/step - loss: 0.5356 - accuracy: 0.8057 - val_loss: 0.4228 - val_accuracy: 0.8598\n",
            "Epoch 67/100\n",
            "62/62 [==============================] - 40s 643ms/step - loss: 0.5346 - accuracy: 0.8086 - val_loss: 0.4233 - val_accuracy: 0.8608\n",
            "Epoch 68/100\n",
            "62/62 [==============================] - 41s 659ms/step - loss: 0.5318 - accuracy: 0.8056 - val_loss: 0.4250 - val_accuracy: 0.8588\n",
            "Epoch 69/100\n",
            "62/62 [==============================] - 41s 662ms/step - loss: 0.5351 - accuracy: 0.8052 - val_loss: 0.4244 - val_accuracy: 0.8626\n",
            "Epoch 70/100\n",
            "62/62 [==============================] - 41s 654ms/step - loss: 0.5290 - accuracy: 0.8077 - val_loss: 0.4244 - val_accuracy: 0.8575\n",
            "Epoch 71/100\n",
            "62/62 [==============================] - 41s 658ms/step - loss: 0.5275 - accuracy: 0.8105 - val_loss: 0.4238 - val_accuracy: 0.8623\n",
            "Epoch 72/100\n",
            "62/62 [==============================] - 40s 648ms/step - loss: 0.5304 - accuracy: 0.8091 - val_loss: 0.4194 - val_accuracy: 0.8626\n",
            "Epoch 73/100\n",
            "62/62 [==============================] - 40s 650ms/step - loss: 0.5232 - accuracy: 0.8103 - val_loss: 0.4234 - val_accuracy: 0.8611\n",
            "Epoch 74/100\n",
            "62/62 [==============================] - 41s 665ms/step - loss: 0.5253 - accuracy: 0.8105 - val_loss: 0.4162 - val_accuracy: 0.8631\n",
            "Epoch 75/100\n",
            "62/62 [==============================] - 40s 649ms/step - loss: 0.5259 - accuracy: 0.8115 - val_loss: 0.4215 - val_accuracy: 0.8626\n",
            "Epoch 76/100\n",
            "62/62 [==============================] - 40s 651ms/step - loss: 0.5194 - accuracy: 0.8120 - val_loss: 0.4216 - val_accuracy: 0.8603\n",
            "Epoch 77/100\n",
            "62/62 [==============================] - 40s 648ms/step - loss: 0.5180 - accuracy: 0.8115 - val_loss: 0.4155 - val_accuracy: 0.8585\n",
            "Epoch 78/100\n",
            "62/62 [==============================] - 40s 647ms/step - loss: 0.5200 - accuracy: 0.8123 - val_loss: 0.4160 - val_accuracy: 0.8616\n",
            "Epoch 79/100\n",
            "62/62 [==============================] - 40s 646ms/step - loss: 0.5186 - accuracy: 0.8129 - val_loss: 0.4113 - val_accuracy: 0.8618\n",
            "Epoch 80/100\n",
            "62/62 [==============================] - 40s 643ms/step - loss: 0.5160 - accuracy: 0.8152 - val_loss: 0.4235 - val_accuracy: 0.8654\n",
            "Epoch 81/100\n",
            "62/62 [==============================] - 40s 645ms/step - loss: 0.5211 - accuracy: 0.8126 - val_loss: 0.4153 - val_accuracy: 0.8621\n",
            "Epoch 82/100\n",
            "62/62 [==============================] - 42s 677ms/step - loss: 0.5237 - accuracy: 0.8120 - val_loss: 0.4209 - val_accuracy: 0.8636\n",
            "Epoch 83/100\n",
            "62/62 [==============================] - 40s 652ms/step - loss: 0.5172 - accuracy: 0.8133 - val_loss: 0.4192 - val_accuracy: 0.8603\n",
            "Epoch 84/100\n",
            "62/62 [==============================] - 40s 650ms/step - loss: 0.5135 - accuracy: 0.8140 - val_loss: 0.4127 - val_accuracy: 0.8631\n",
            "Epoch 85/100\n",
            "62/62 [==============================] - 40s 647ms/step - loss: 0.5098 - accuracy: 0.8142 - val_loss: 0.4092 - val_accuracy: 0.8618\n",
            "Epoch 86/100\n",
            "62/62 [==============================] - 40s 648ms/step - loss: 0.5123 - accuracy: 0.8122 - val_loss: 0.4082 - val_accuracy: 0.8628\n",
            "Epoch 87/100\n",
            "62/62 [==============================] - 40s 651ms/step - loss: 0.5110 - accuracy: 0.8132 - val_loss: 0.4088 - val_accuracy: 0.8649\n",
            "Epoch 88/100\n",
            "62/62 [==============================] - 41s 656ms/step - loss: 0.5142 - accuracy: 0.8141 - val_loss: 0.4104 - val_accuracy: 0.8585\n",
            "Epoch 89/100\n",
            "62/62 [==============================] - 41s 670ms/step - loss: 0.5112 - accuracy: 0.8170 - val_loss: 0.4114 - val_accuracy: 0.8618\n",
            "Epoch 90/100\n",
            "62/62 [==============================] - 42s 677ms/step - loss: 0.5105 - accuracy: 0.8136 - val_loss: 0.4115 - val_accuracy: 0.8580\n",
            "Epoch 91/100\n",
            "62/62 [==============================] - 40s 650ms/step - loss: 0.5080 - accuracy: 0.8178 - val_loss: 0.4070 - val_accuracy: 0.8646\n",
            "Epoch 92/100\n",
            "62/62 [==============================] - 41s 654ms/step - loss: 0.5090 - accuracy: 0.8157 - val_loss: 0.4130 - val_accuracy: 0.8548\n",
            "Epoch 93/100\n",
            "62/62 [==============================] - 40s 646ms/step - loss: 0.5126 - accuracy: 0.8142 - val_loss: 0.4077 - val_accuracy: 0.8603\n",
            "Epoch 94/100\n",
            "62/62 [==============================] - 41s 659ms/step - loss: 0.5025 - accuracy: 0.8166 - val_loss: 0.4042 - val_accuracy: 0.8641\n",
            "Epoch 95/100\n",
            "62/62 [==============================] - 41s 661ms/step - loss: 0.5057 - accuracy: 0.8165 - val_loss: 0.4076 - val_accuracy: 0.8591\n",
            "Epoch 96/100\n",
            "62/62 [==============================] - 41s 657ms/step - loss: 0.5045 - accuracy: 0.8165 - val_loss: 0.4053 - val_accuracy: 0.8633\n",
            "Epoch 97/100\n",
            "62/62 [==============================] - 43s 701ms/step - loss: 0.5063 - accuracy: 0.8148 - val_loss: 0.4140 - val_accuracy: 0.8636\n",
            "Epoch 98/100\n",
            "62/62 [==============================] - 41s 657ms/step - loss: 0.5035 - accuracy: 0.8189 - val_loss: 0.4069 - val_accuracy: 0.8623\n",
            "Epoch 99/100\n",
            "62/62 [==============================] - 42s 672ms/step - loss: 0.5012 - accuracy: 0.8187 - val_loss: 0.4025 - val_accuracy: 0.8638\n",
            "Epoch 100/100\n",
            "62/62 [==============================] - 41s 658ms/step - loss: 0.4996 - accuracy: 0.8197 - val_loss: 0.4222 - val_accuracy: 0.8661\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "155/155 [==============================] - 4s 28ms/step - loss: 0.4684 - accuracy: 0.8422\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       234\n",
            "           1       0.88      0.94      0.91      3928\n",
            "           2       0.63      0.59      0.61       795\n",
            "\n",
            "    accuracy                           0.84      4957\n",
            "   macro avg       0.50      0.51      0.51      4957\n",
            "weighted avg       0.80      0.84      0.82      4957\n",
            "\n",
            "\n",
            "Accuracy 0.8422432923138995\n",
            "LSTM: non hate - hate\n",
            "precision: [0.         0.87936357 0.63270777]\n",
            "recall: [0.         0.94271894 0.59371069]\n",
            "fscore: [0.         0.9099398  0.61258923]\n",
            "support: [ 234 3928  795]\n",
            "\n",
            "Confusion Matrix: \n",
            " [[   0  185   49]\n",
            " [   0 3703  225]\n",
            " [   0  323  472]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Testing and Controversial Topic Analysis"
      ],
      "metadata": {
        "id": "K0gLYzzJugQS"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qe3NAPvd9vgY"
      },
      "source": [
        "Lets see how the models work in practice. We will test them with our test set. And check the values below."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluation Results on the dataset"
      ],
      "metadata": {
        "id": "YjDNJ4Dmuf5Z"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQd78A329vgY"
      },
      "source": [
        "predicted_lstm = lstm_model_trained.predict(x_test)\n",
        "predicted_lstm_embeddings = lstm_model_embbedding_trained.predict(x_test)\n",
        "predicted_bi = bilstm_model_trained.predict(x_test)\n",
        "predicted_bi_embeddings = bilstm_model_embedding_trained.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GgKE9uZr9vgY"
      },
      "source": [
        "dataframe_lstm = pd.DataFrame.from_records(predicted_lstm)\n",
        "dataframe_lstm_embeddings = pd.DataFrame.from_records(predicted_lstm_embeddings)\n",
        "dataframe_bi = pd.DataFrame.from_records(predicted_bi)\n",
        "dataframe_bi_embeddings = pd.DataFrame.from_records(predicted_bi_embeddings)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_lstm.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "apz4b5RYK4lQ",
        "outputId": "857722a0-7c6e-474e-9471-3348246d767a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yynu3JjF9vga"
      },
      "source": [
        "Get label function creates appropriate labels according to  predictions. Predicted class is stored in a column of dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFgftQI79vga"
      },
      "source": [
        "def get_label(df):\n",
        "    if ((df[0] > df[1]) & (df[0] > df[2])):\n",
        "        return 0\n",
        "    elif ((df[1] > df[0]) & (df[1] > df[2])):\n",
        "        return 1\n",
        "    else:\n",
        "        return 2\n",
        "\n",
        "        # np.argmax(y_pred, axis=1)\n",
        "\n",
        "dataframe_lstm['predClass'] = dataframe_lstm.apply(lambda row: get_label(row), axis=1)\n",
        "dataframe_lstm_embeddings['predClass'] = dataframe_lstm_embeddings.apply(lambda row: get_label(row), axis=1)\n",
        "\n",
        "dataframe_bi['predClass'] = dataframe_bi.apply(lambda row: get_label(row), axis=1)\n",
        "dataframe_bi_embeddings['predClass'] = dataframe_bi_embeddings.apply(lambda row: get_label(row), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l_eXsZtk9vga"
      },
      "source": [
        "__LSTM and BiLSTM score__"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_lstm, recall_lstm, fscore_lstm, support_lstm = precision_recall_fscore_support(np.argmax(y_test, axis=1), dataframe_lstm['predClass'])\n",
        "print('Classes LSTM: hate - offensive - neither')\n",
        "print('precision: {}'.format(precision_lstm))\n",
        "print('recall: {}'.format(recall_lstm))\n",
        "print('fscore: {}'.format(fscore_lstm))\n",
        "print('support: {}'.format(support_lstm))\n",
        "precision_bi, recall_bi, fscore_bi, support_bi = precision_recall_fscore_support(np.argmax(y_test, axis=1), dataframe_bi['predClass'])\n",
        "print('Classes BILSTM: hate - offensive - neither')\n",
        "print('precision: {}'.format(precision_bi))\n",
        "print('recall: {}'.format(recall_bi))\n",
        "print('fscore: {}'.format(fscore_bi))\n",
        "print('support: {}'.format(support_bi))\n",
        "\n",
        "\n",
        "print('\\n\\nComparison to Trained Embeddings\\n\\n')\n",
        "precision_lstm, recall_lstm, fscore_lstm, support_lstm = precision_recall_fscore_support(np.argmax(y_test, axis=1), dataframe_lstm_embeddings['predClass'])\n",
        "print('Classes Embeddings LSTM: hate - offensive - neither')\n",
        "print('precision: {}'.format(precision_lstm))\n",
        "print('recall: {}'.format(recall_lstm))\n",
        "print('fscore: {}'.format(fscore_lstm))\n",
        "print('support: {}'.format(support_lstm))\n",
        "precision_bi, recall_bi, fscore_bi, support_bi = precision_recall_fscore_support(np.argmax(y_test, axis=1), dataframe_bi_embeddings['predClass'])\n",
        "print('Classes Embeddings BILSTM: hate - offensive - neither')\n",
        "print('precision: {}'.format(precision_bi))\n",
        "print('recall: {}'.format(recall_bi))\n",
        "print('fscore: {}'.format(fscore_bi))\n",
        "print('support: {}'.format(support_bi))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XD49Mo-mF64e",
        "outputId": "87bdd55d-5b2f-419b-e91d-d1efc43ccfdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classes LSTM: hate - offensive - neither\n",
            "precision: [0.14792899 0.9271556  0.78856383]\n",
            "recall: [0.10683761 0.95264766 0.74591195]\n",
            "fscore: [0.12406948 0.93972878 0.76664512]\n",
            "support: [ 234 3928  795]\n",
            "Classes BILSTM: hate - offensive - neither\n",
            "precision: [0.36551724 0.93746835 0.774942  ]\n",
            "recall: [0.22649573 0.94271894 0.84025157]\n",
            "fscore: [0.27968338 0.94008632 0.8062764 ]\n",
            "support: [ 234 3928  795]\n",
            "\n",
            "\n",
            "Comparison to Trained Embeddings\n",
            "\n",
            "\n",
            "Classes Embeddings LSTM: hate - offensive - neither\n",
            "precision: [0.         0.88735465 0.602657  ]\n",
            "recall: [0.         0.93253564 0.62767296]\n",
            "fscore: [0.         0.90938431 0.61491066]\n",
            "support: [ 234 3928  795]\n",
            "Classes Embeddings BILSTM: hate - offensive - neither\n",
            "precision: [0.         0.87936357 0.63270777]\n",
            "recall: [0.         0.94271894 0.59371069]\n",
            "fscore: [0.         0.9099398  0.61258923]\n",
            "support: [ 234 3928  795]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(np.argmax(y_test, axis=1), dataframe_lstm['predClass']))\n",
        "print('\\nConfusion Matrix: \\n', confusion_matrix(np.argmax(y_test, axis=1), dataframe_lstm['predClass']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2NmcLsKZ4wq",
        "outputId": "3d1da397-a254-4abf-b362-155acc3bfe2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.15      0.11      0.12       234\n",
            "           1       0.93      0.95      0.94      3928\n",
            "           2       0.79      0.75      0.77       795\n",
            "\n",
            "    accuracy                           0.88      4957\n",
            "   macro avg       0.62      0.60      0.61      4957\n",
            "weighted avg       0.87      0.88      0.87      4957\n",
            "\n",
            "\n",
            "Confusion Matrix: \n",
            " [[  25  168   41]\n",
            " [  68 3742  118]\n",
            " [  76  126  593]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(np.argmax(y_test, axis=1), dataframe_lstm_embeddings['predClass']))\n",
        "print('\\nConfusion Matrix: \\n', confusion_matrix(np.argmax(y_test, axis=1), dataframe_lstm_embeddings['predClass']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RA8hvSYiBYd",
        "outputId": "f5a6e11f-234b-4a32-f1de-a2e928e7fc03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       234\n",
            "           1       0.89      0.93      0.91      3928\n",
            "           2       0.60      0.63      0.61       795\n",
            "\n",
            "    accuracy                           0.84      4957\n",
            "   macro avg       0.50      0.52      0.51      4957\n",
            "weighted avg       0.80      0.84      0.82      4957\n",
            "\n",
            "\n",
            "Confusion Matrix: \n",
            " [[   0  169   65]\n",
            " [   1 3663  264]\n",
            " [   0  296  499]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(np.argmax(y_test, axis=1), dataframe_bi['predClass']))\n",
        "print('\\nConfusion Matrix: \\n', confusion_matrix(np.argmax(y_test, axis=1), dataframe_bi['predClass']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SG8NrB9saQOe",
        "outputId": "b7d1c44b-a0dd-4ee3-f382-4e73b1b2abf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.37      0.23      0.28       234\n",
            "           1       0.94      0.94      0.94      3928\n",
            "           2       0.77      0.84      0.81       795\n",
            "\n",
            "    accuracy                           0.89      4957\n",
            "   macro avg       0.69      0.67      0.68      4957\n",
            "weighted avg       0.88      0.89      0.89      4957\n",
            "\n",
            "\n",
            "Confusion Matrix: \n",
            " [[  53  135   46]\n",
            " [  77 3703  148]\n",
            " [  15  112  668]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(np.argmax(y_test, axis=1), dataframe_bi_embeddings['predClass']))\n",
        "print('\\nConfusion Matrix: \\n', confusion_matrix(np.argmax(y_test, axis=1), dataframe_bi_embeddings['predClass']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vsAVyyK4h8Xa",
        "outputId": "25bf6abc-daba-46ab-fbfe-7144d3f2d6ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       234\n",
            "           1       0.88      0.94      0.91      3928\n",
            "           2       0.63      0.59      0.61       795\n",
            "\n",
            "    accuracy                           0.84      4957\n",
            "   macro avg       0.50      0.51      0.51      4957\n",
            "weighted avg       0.80      0.84      0.82      4957\n",
            "\n",
            "\n",
            "Confusion Matrix: \n",
            " [[   0  185   49]\n",
            " [   0 3703  225]\n",
            " [   0  323  472]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Controversial Topic Analysis"
      ],
      "metadata": {
        "id": "g6m6rsdIaxNE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use the following results to compare our model with Deephate based on our labeling and then to choose the best model."
      ],
      "metadata": {
        "id": "YArFlXz7k0D-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def analyze_controversial_topic(data, test, model_lstm, model_bilstm, title='topic'):\n",
        "    predicted_lstm = model_lstm.predict(test)\n",
        "    predicted_bi = model_bilstm.predict(test)\n",
        "    dataframe_lstm = pd.DataFrame.from_records(predicted_lstm)\n",
        "    dataframe_bi = pd.DataFrame.from_records(predicted_bi)\n",
        "    dataframe_lstm['predClass'] = dataframe_lstm.apply(lambda row: get_label(row), axis=1)\n",
        "    dataframe_bi['predClass'] = dataframe_bi.apply(lambda row: get_label(row), axis=1)\n",
        "    print('Controversial Topic Analysis\\n\\n')\n",
        "    print('Labels: ', title)\n",
        "    print('LSTM RESULTS')\n",
        "    print(classification_report(data['class'], dataframe_lstm['predClass']))\n",
        "    print('\\nConfusion Matrix: \\n', confusion_matrix(data['class'], dataframe_lstm['predClass']))\n",
        "    print('BILSTM RESULTS')\n",
        "    print(classification_report(data['class'], dataframe_bi['predClass']))\n",
        "    print('\\nConfusion Matrix: \\n', confusion_matrix(data['class'], dataframe_bi['predClass']))"
      ],
      "metadata": {
        "id": "7GNmA9pW8zqh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Donald Trump"
      ],
      "metadata": {
        "id": "mGj4ESIMcsG-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "analyze_controversial_topic(trump_data, trump_test, model_lstm=lstm_model_trained, model_bilstm=bilstm_model_trained, title='Trump')"
      ],
      "metadata": {
        "id": "N9yLpvv2ZS-A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f34b33fe-7c8d-47af-c33c-a3d1e2280af7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Controversial Topic Analysis\n",
            "\n",
            "\n",
            "Labels:  Trump\n",
            "LSTM RESULTS\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00        11\n",
            "           1       0.28      0.93      0.43        54\n",
            "           2       0.50      0.02      0.05       124\n",
            "\n",
            "    accuracy                           0.28       189\n",
            "   macro avg       0.26      0.32      0.16       189\n",
            "weighted avg       0.41      0.28      0.15       189\n",
            "\n",
            "\n",
            "Confusion Matrix: \n",
            " [[  0  10   1]\n",
            " [  2  50   2]\n",
            " [  1 120   3]]\n",
            "BILSTM RESULTS\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00        11\n",
            "           1       0.30      1.00      0.46        54\n",
            "           2       0.60      0.02      0.05       124\n",
            "\n",
            "    accuracy                           0.30       189\n",
            "   macro avg       0.30      0.34      0.17       189\n",
            "weighted avg       0.48      0.30      0.16       189\n",
            "\n",
            "\n",
            "Confusion Matrix: \n",
            " [[  0   9   2]\n",
            " [  0  54   0]\n",
            " [  4 117   3]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### LGBT"
      ],
      "metadata": {
        "id": "DzVqFr_zcusM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "analyze_controversial_topic(lgbt_data, lgbt_test, model_lstm=lstm_model_trained, model_bilstm=bilstm_model_trained, title='LGBTQ*')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7k70A_Nj1wu",
        "outputId": "ab86021c-533c-4f58-eac1-c5b6117e6cb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Controversial Topic Analysis\n",
            "\n",
            "\n",
            "Labels:  LGBTQ*\n",
            "LSTM RESULTS\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.07      0.12        14\n",
            "           1       0.27      0.97      0.42        40\n",
            "           2       0.33      0.01      0.02        96\n",
            "\n",
            "    accuracy                           0.27       150\n",
            "   macro avg       0.37      0.35      0.19       150\n",
            "weighted avg       0.33      0.27      0.14       150\n",
            "\n",
            "\n",
            "Confusion Matrix: \n",
            " [[ 1 12  1]\n",
            " [ 0 39  1]\n",
            " [ 1 94  1]]\n",
            "BILSTM RESULTS\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.07      0.12        14\n",
            "           1       0.27      0.97      0.42        40\n",
            "           2       1.00      0.01      0.02        96\n",
            "\n",
            "    accuracy                           0.27       150\n",
            "   macro avg       0.59      0.35      0.19       150\n",
            "weighted avg       0.76      0.27      0.14       150\n",
            "\n",
            "\n",
            "Confusion Matrix: \n",
            " [[ 1 13  0]\n",
            " [ 1 39  0]\n",
            " [ 0 95  1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Covid Dataset"
      ],
      "metadata": {
        "id": "C6pilkP3dTx0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "analyze_controversial_topic(covid_data, covid_test, model_lstm=lstm_model_trained, model_bilstm=bilstm_model_trained, title='Covid19')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tVij7CmfkpMM",
        "outputId": "e9331a82-51ea-461f-a803-9a5fcc139730"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Controversial Topic Analysis\n",
            "\n",
            "\n",
            "Labels:  Covid19\n",
            "LSTM RESULTS\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.33      0.02      0.03        57\n",
            "           1       0.30      0.95      0.45        56\n",
            "           2       0.38      0.04      0.07        77\n",
            "\n",
            "    accuracy                           0.30       190\n",
            "   macro avg       0.33      0.33      0.18       190\n",
            "weighted avg       0.34      0.30      0.17       190\n",
            "\n",
            "\n",
            "Confusion Matrix: \n",
            " [[ 1 53  3]\n",
            " [ 1 53  2]\n",
            " [ 1 73  3]]\n",
            "BILSTM RESULTS\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.04      0.07        57\n",
            "           1       0.31      0.96      0.46        56\n",
            "           2       0.22      0.03      0.05        77\n",
            "\n",
            "    accuracy                           0.31       190\n",
            "   macro avg       0.34      0.34      0.19       190\n",
            "weighted avg       0.33      0.31      0.18       190\n",
            "\n",
            "\n",
            "Confusion Matrix: \n",
            " [[ 2 50  5]\n",
            " [ 0 54  2]\n",
            " [ 2 73  2]]\n"
          ]
        }
      ]
    }
  ]
}