16_TextClassificationSentimentalAnalysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Sahel-Eskandar/DataScience-Projects/blob/main/16_TextClassificationSentimentalAnalysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziJQAjH1bTzf"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "Text classification and sentiment analysis are two related techniques in natural language processing that involve automatically analyzing and categorizing text data based on its content.\n",
        "\n",
        "Text classification, also known as text categorization, is the process of assigning a predefined category or label to a given text document. The goal of text classification is to automatically classify large volumes of text data based on their content, making it easier to organize, search, and analyze. For example, a news article might be classified into categories such as politics, sports, entertainment, etc.\n",
        "\n",
        "Sentiment analysis, also known as opinion mining, is a type of text classification that focuses specifically on identifying the emotional tone of a given piece of text. The goal of sentiment analysis is to automatically determine whether a given text expresses a positive, negative, or neutral sentiment. For example, a customer review of a product might be classified as positive, negative, or neutral based on the language used to describe the product.\n",
        "\n",
        "Both text classification and sentiment analysis are important tools for businesses, organizations, and researchers who want to make sense of large volumes of text data. They can be used to identify trends, monitor social media sentiment, analyze customer feedback, and much more.\n",
        "\n",
        "Text classification and sentiment analysis involve a combination of NLP and data analysis techniques to analyze and categorize large volumes of text data. Following techniques are commonly used:\n",
        "\n",
        "1. Text preprocessing: This involves cleaning and transforming the raw text data to prepare it for analysis. Techniques such as tokenization (breaking text into words or phrases), stopword removal (removing common words that don't carry much meaning), and stemming (reducing words to their base form) can be used to simplify the text data.\n",
        "\n",
        "2. Feature extraction: This involves selecting the most important features (words or phrases) from the preprocessed text data that are likely to be relevant for classification. Techniques such as bag-of-words (counting the frequency of each word in a document), TF-IDF (weighing words by their importance in a document), and word embeddings (representing words as vectors in a high-dimensional space) can be used to extract features.\n",
        "\n",
        "3. Machine learning algorithms: This involves training a machine learning model on the preprocessed and feature-extracted text data to predict the category or sentiment of new text data. Common machine learning algorithms used for text classification and sentiment analysis include Naive Bayes, Support Vector Machines (SVM), Random Forests, and Deep Learning models like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN).This notebook will focus on text-classification and sentiment analysis. We will go through all major NLP and dat analysis techniques, some of which include:\n",
        "    - LSTMs\n",
        "    - Transformers (such as BERT)\n",
        "    - Naive Bayes\n",
        "    - XGBoost \n",
        "\n",
        "4. Evaluation metrics: This involves measuring the performance of the text classification and sentiment analysis models on a test dataset. Metrics such as accuracy, precision, recall, F1 score, and confusion matrix can be used to evaluate the performance of the models.\n",
        "\n",
        "5. Model optimization: This involves fine-tuning the machine learning models to improve their performance on the test dataset. Techniques such as hyperparameter tuning (adjusting model parameters such as learning rate, regularization, etc.), feature selection (selecting the most important features for the model), and ensemble learning (combining multiple models to improve performance) can be used to optimize the models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPWzrwbYbVky"
      },
      "source": [
        "Data Source: \n",
        "\n",
        "kaggle kernels output namansood/nlp-xgboost-bert-glove-rnns-vectorizers -p /path/to/dest\n",
        "\n",
        "https://www.kaggle.com/code/namansood/nlp-xgboost-bert-glove-rnns-vectorizers/data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "id": "2VK8ZjqHbTzj",
        "tags": []
      },
      "outputs": [],
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "tGHolVNhbTzk",
        "tags": []
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "encoding_errors='ignore'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eD253UZqUrZm",
        "outputId": "c84a43be-834a-4a97-b3e0-de090ca9b455"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "!pip install wordcloud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DcBr7GN6UrZn",
        "outputId": "e8466f7e-5eeb-433b-c732-8d25e67edde2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "!pip install nltk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "okKEp6bZUrZn",
        "outputId": "1d90c523-db55-497f-cc97-5947a217e178"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "!pip install spacy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m6k799ROUrZn",
        "outputId": "badf5967-2c3e-4cdc-d21a-8537d85df6c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.8/dist-packages (2.11.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install keras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qbqzE-krUrZn",
        "outputId": "6810110c-bc36-4dea-9516-1e7786b63ed7"
      },
      "outputs": [
        {
          "output_type": "stream",
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          ]
        }
      ],
      "source": [
        "!pip install tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "ZYZQCUolbTzl",
        "tags": []
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import string\n",
        "import numpy as np \n",
        "import random\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "from plotly import graph_objs as go\n",
        "import plotly.express as px\n",
        "import plotly.figure_factory as ff\n",
        "from collections import Counter\n",
        "\n",
        "from PIL import Image\n",
        "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
        "\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "from tqdm import tqdm\n",
        "import os\n",
        "import nltk\n",
        "import spacy\n",
        "import random\n",
        "from spacy.util import compounding\n",
        "from spacy.util import minibatch\n",
        "\n",
        "from collections import defaultdict\n",
        "from collections import Counter\n",
        "\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, classification_report,accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-d9ZGOBUrZn",
        "outputId": "26589425-4b7b-488f-c5f5-b4db7cdaa4fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting Keras-Preprocessing\n",
            "  Downloading Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 KB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.8/dist-packages (from Keras-Preprocessing) (1.15.0)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.8/dist-packages (from Keras-Preprocessing) (1.22.4)\n",
            "Installing collected packages: Keras-Preprocessing\n",
            "Successfully installed Keras-Preprocessing-1.1.2\n"
          ]
        }
      ],
      "source": [
        "!pip install Keras-Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "CQG1qs7wf-8r",
        "tags": []
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.initializers import Constant\n",
        "from keras.layers import LSTM, Embedding,BatchNormalization, Dense, TimeDistributed, Dropout, Bidirectional, Flatten, GlobalMaxPool1D\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras_preprocessing.sequence import pad_sequences\n",
        "#from keras.layers.embeddings import Embedding\n",
        "from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
        "from keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "vui-pD2lbTzl",
        "tags": []
      },
      "outputs": [],
      "source": [
        "# Defining the global variables for the color schemes we will incorporate\n",
        "pblue = \"#496595\"\n",
        "pb2 = \"#85a1c1\"\n",
        "pb3 = \"#3f4d63\"\n",
        "pg = \"#c6ccd8\"\n",
        "pb = \"#202022\"\n",
        "pbg = \"#f4f0ea\"\n",
        "\n",
        "pgreen = px.colors.qualitative.Plotly[2]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://raw.githubusercontent.com/Sahel-Eskandar/DataScience-Projects/main/Data/spam.csv'\n",
        "df = pd.read_csv(url, encoding = \"ISO-8859-1\", error_bad_lines=False)"
      ],
      "metadata": {
        "id": "ajp12J6Lblgx"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "oUG06LinbTzm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67bfab14-b867-4f93-8ca3-e56b5989e35c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "v1               0\n",
              "v2               0\n",
              "Unnamed: 2    5522\n",
              "Unnamed: 3    5560\n",
              "Unnamed: 4    5566\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "5b0xb9XXbTzm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a638479-1d4a-4c33-a86e-a80504d76a2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "v1            object\n",
              "v2            object\n",
              "Unnamed: 2    object\n",
              "Unnamed: 3    object\n",
              "Unnamed: 4    object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "hSrHWTdkbTzn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47f045ea-3a3b-406d-e691-033fd67cc9d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "v1               0\n",
              "v2               0\n",
              "Unnamed: 2    5522\n",
              "Unnamed: 3    5560\n",
              "Unnamed: 4    5566\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
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      "cell_type": "code",
      "execution_count": 25,
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              "     v1                                                 v2\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
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              "\n",
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        "df.dropna(axis=1, inplace=True)\n",
        "df.head()"
      ]
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    {
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        {
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            "text/plain": [
              "  label                                               text\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
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              "</table>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4d023fd4-6ee5-4541-95de-6d3afcb61812 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4d023fd4-6ee5-4541-95de-6d3afcb61812');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df.rename(columns={\"v1\":\"label\", \"v2\":\"text\"}, inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Kt39lBWsbTzn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b72f28cd-ef6d-4a4f-f642-02b7fc951999"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "171"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Finding maximum length of text message\n",
        "\n",
        "np.max(df['text'].apply(lambda x: len(x.split())).values)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ygp6ge--bTzo"
      },
      "source": [
        "<h2>Exploratory data analysis</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "1SHEj4pBbTzo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "241f3554-9645-4386-d5b9-a52942cb138c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4825,  747])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Checking balance of dataset\n",
        "grouped_df = df.groupby('label').count().values.flatten()\n",
        "grouped_df"
      ]
    },
    {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   label   5572 non-null   object\n",
            " 1   text    5572 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 87.2+ KB\n"
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      "source": [
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              "  label                                               text\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  U dun say so early hor... U c already then say...\n",
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      "source": [
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Bar(\n",
        "        x=['ham'],\n",
        "        y=[grouped_df[0]],\n",
        "        name='Safe',\n",
        "        text=[grouped_df[0]],\n",
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        "fig.add_trace(go.Bar(\n",
        "        x=['spam'],\n",
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        "\n",
        "fig.update_layout(\n",
        "    title='Class distribution in the dataset')\n",
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    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "s_u1HbpNbTzo"
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      "outputs": [],
      "source": [
        "# Creating series with length as index\n",
        "# Sorting the series by index i.e. length\n",
        "len_df_ham = df[df['label']=='ham'].text.apply(lambda x: len(x.split())).value_counts().sort_index()\n",
        "len_df_spam = df[df['label']=='spam'].text.apply(lambda x: len(x.split())).value_counts().sort_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
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          "data": {
            "text/plain": [
              "1       41\n",
              "2       39\n",
              "3       78\n",
              "4      197\n",
              "5      353\n",
              "      ... \n",
              "103      1\n",
              "109      1\n",
              "125      2\n",
              "162      1\n",
              "171      1\n",
              "Name: text, Length: 82, dtype: int64"
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          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "len_df_ham"
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    {
      "cell_type": "code",
      "execution_count": 34,
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        "outputId": "7e0c6e24-f529-4f7f-c9a7-4280aa345bb1"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2      2\n",
              "3      1\n",
              "4      1\n",
              "5      3\n",
              "6      4\n",
              "7      4\n",
              "8      8\n",
              "9      2\n",
              "10     5\n",
              "11     4\n",
              "12    12\n",
              "13    14\n",
              "14     6\n",
              "15    12\n",
              "16     8\n",
              "17    12\n",
              "18    15\n",
              "19    21\n",
              "20    25\n",
              "21    16\n",
              "22    49\n",
              "23    47\n",
              "24    62\n",
              "25    67\n",
              "26    65\n",
              "27    79\n",
              "28    78\n",
              "29    42\n",
              "30    36\n",
              "31    14\n",
              "32    21\n",
              "33     7\n",
              "34     3\n",
              "35     2\n",
              "Name: text, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "len_df_spam"
      ]
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      "execution_count": 35,
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        "id": "fT6p-eCnbTzp",
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        "outputId": "1ac01d79-0b48-49ac-8528-5fcc56b20f39"
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of SMS lengths\"},\"xaxis\":{\"range\":[0,80]}},                        {\"responsive\": true}                    ).then(function(){\n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# X-axis consists of the length of the msgs\n",
        "# Y-axis consists of the frequency of those lengths\n",
        "\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "x=len_df_ham.index,\n",
        "y=len_df_ham.values,\n",
        "name='Safe',\n",
        "fill='tozeroy',\n",
        "marker_color=pblue))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "x=len_df_spam.index,\n",
        "y=len_df_spam.values,\n",
        "name='Spam',\n",
        "fill='tozeroy',\n",
        "marker_color=pg\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Frequency of SMS lengths')\n",
        "fig.update_xaxes(range=[0, 80])\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9LwluEfUbTzp"
      },
      "source": [
        "We can see that the safe SMS messages are much shorter than the spam messages."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FH6lVarIbTzp"
      },
      "source": [
        "<h2>Data preprocessing</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "nIEGDD3abTzp"
      },
      "outputs": [],
      "source": [
        "def cleaning(text):\n",
        "    text = str(text).lower()\n",
        "    text = re.sub('\\[.*?\\]', '', text)\n",
        "    text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
        "    text = re.sub('<.*?>+', '', text)\n",
        "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
        "    text = re.sub('\\n', '', text)\n",
        "    text = re.sub('\\w*\\d\\w*', '', text)\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "uMrZll7mbTzp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aac0dda5-6b4e-4648-a5bc-a9960d29b3a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       Go until jurong point, crazy.. Available only ...\n",
              "1                           Ok lar... Joking wif u oni...\n",
              "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3       U dun say so early hor... U c already then say...\n",
              "4       Nah I don't think he goes to usf, he lives aro...\n",
              "                              ...                        \n",
              "5567    This is the 2nd time we have tried 2 contact u...\n",
              "5568                Will Ì_ b going to esplanade fr home?\n",
              "5569    Pity, * was in mood for that. So...any other s...\n",
              "5570    The guy did some bitching but I acted like i'd...\n",
              "5571                           Rofl. Its true to its name\n",
              "Name: text, Length: 5572, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df['text']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "pjA1Mca9bTzp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38e50faf-089f-4bae-fc7a-d0ed78a3152c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       go until jurong point crazy available only in ...\n",
              "1                                 ok lar joking wif u oni\n",
              "2       free entry in  a wkly comp to win fa cup final...\n",
              "3             u dun say so early hor u c already then say\n",
              "4       nah i dont think he goes to usf he lives aroun...\n",
              "                              ...                        \n",
              "5567    this is the  time we have tried  contact u u h...\n",
              "5568                  will ì b going to esplanade fr home\n",
              "5569    pity  was in mood for that soany other suggest...\n",
              "5570    the guy did some bitching but i acted like id ...\n",
              "5571                            rofl its true to its name\n",
              "Name: text, Length: 5572, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "df['text'] = df['text'].apply(cleaning)\n",
        "df['text']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CIReUaWfUrZp",
        "outputId": "9ac7a67f-be80-4ab2-c777-4134c1e62f72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "9Q4qZD12bTzp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "925b3abd-de5a-483e-e94e-6fa0caab5c81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       go jurong point crazy available bugis n great ...\n",
              "1                                   ok lar joking wif oni\n",
              "2       free entry  wkly comp win fa cup final tkts  m...\n",
              "3                           dun say early hor already say\n",
              "4             nah dont think goes usf lives around though\n",
              "                              ...                        \n",
              "5567     time tried  contact å£ pound prize  claim eas...\n",
              "5568                          ì b going esplanade fr home\n",
              "5569                         pity  mood soany suggestions\n",
              "5570    guy bitching acted like id interested buying s...\n",
              "5571                                       rofl true name\n",
              "Name: text, Length: 5572, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "# Removing stop words\n",
        "stop_words = stopwords.words('english')\n",
        "more = ['u', 'im', 'c']\n",
        "stop_words = stop_words + more\n",
        "\n",
        "\n",
        "def sw_rem(text):\n",
        "    text = ' '.join(word for word in text.split(' ') if word not in stop_words)\n",
        "    return text\n",
        "\n",
        "df['text'] = df['text'].apply(sw_rem)\n",
        "df['text']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mtqTbkZbTzp"
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        "<h2>Stemming and lemmatization</h2>\n",
        "\n",
        "Documents and other forms of text use different forms of the same words, such as play, playing, played. There are families of derivationally related words that have similar meanings. Our main task with stemming and lemmatization is to reduce all these derived words into the parent/family word, therefore reducing the total vocabulary while retaining information.\n",
        "\n",
        "* **Stemming** - Omits the ends of words to achieve the goal correctly, this works **most of the times** and can also remove the derivational suffix\n",
        "\n",
        "* **Lemmatization** - Working with a vocabulary and morphological analysis of wrods, removing inflectional endings only and returning the base and dictionary form of a word.\n",
        "\n",
        "As we do not require much emphasis on words, we will focus more on stemming than lemmatization,.\n",
        "\n",
        "<h3>Stemming algorithms</h3>\n",
        "\n",
        "We have multiple algorithms to achieve our stemming goals, some of them are as follows:\n",
        "\n",
        "* PorterStemmer - Fast and efficient. Strips off the end (suffix) to produce the stems. It does not follow linguistics but rather a set of 05 rules for diferent cases. \n",
        "\n",
        "* SnowballStemmer - Generate a set of rules for any language. These are useful for non-english stemming tasks.\n",
        "\n",
        "* LancasterStemmer - Iterative algorithm, uses about 120 rules, it tries to find an applicable rule by the last character of each word. The last character may be omitted or replaced."
      ]
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    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
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      "source": [
        "stems = nltk.SnowballStemmer('english')\n",
        "\n",
        "def stemming(text):\n",
        "    text = ' '.join(stems.stem(word) for word in text.split())\n",
        "    return text"
      ]
    },
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              "  label                                               text\n",
              "0   ham  go jurong point crazi avail bugi n great world...\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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      "source": [
        "df['text'] = df['text'].apply(stemming)\n",
        "df.head()"
      ]
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    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
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      "outputs": [],
      "source": [
        "# Creating a pipeline\n",
        "\n",
        "def pipeline(text):\n",
        "    text = cleaning(text)\n",
        "    text = ' ' .join(word for word in text.split(' ') if word not in stop_words)\n",
        "    text = ' '.join(stems.stem(word) for word in text.split(' '))\n",
        "    return text"
      ]
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              "\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d8e255df-9b07-4013-9b03-e84bab6ff6fb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d8e255df-9b07-4013-9b03-e84bab6ff6fb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# Encoding the categorical target variable\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "le.fit(df['label'])\n",
        "\n",
        "df['label_num'] = le.transform(df['label'])\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Me7mobO4bTzq"
      },
      "source": [
        "<h2>Visualizing tokens</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "U0dml2-qbTzq"
      },
      "outputs": [],
      "source": [
        "# This will combine all the text values for safe sms\n",
        "#' '.join(text for text in df[df['label']=='ham'].text)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "import requests\n",
        "from io import BytesIO\n",
        "\n",
        "url1 = 'https://raw.githubusercontent.com/Sahel-Eskandar/DataScience-Projects/main/Data/upvote.png'\n",
        "\n",
        "response1 = requests.get(url1)\n",
        "img = Image.open(BytesIO(response1.content))"
      ],
      "metadata": {
        "id": "M1W4w8YJenPc"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "GEaBZ0Z0bTzq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "54974ec4-2a5c-4a6a-96be-e746f1e015c3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Extracting the twitter word cloud mask\n",
        "upvote_mask = np.array(img)\n",
        "wc = WordCloud(background_color='white', max_words=200, mask=upvote_mask)\n",
        "\n",
        "wc.generate(' '.join(text for text in df[df['label']=='ham'].text))\n",
        "plt.figure(figsize=(15, 10))\n",
        "plt.title('Top words for safe messages', fontdict={'size':22})\n",
        "plt.imshow(wc)\n",
        "plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url2 = 'https://raw.githubusercontent.com/Sahel-Eskandar/DataScience-Projects/main/Data/dislike.png'\n",
        "\n",
        "response2 = requests.get(url2)\n",
        "img2 = Image.open(BytesIO(response2.content))"
      ],
      "metadata": {
        "id": "Yzn2WiXnfIwg"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "efHuklgJbTzq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "60104f5e-f1dd-4d98-ed0f-fa1a5fdd6bbe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Extracting the twitter word cloud mask\n",
        "dislike_mask = np.array(img2)\n",
        "wc = WordCloud(background_color='white', max_words=200, mask=dislike_mask)\n",
        "\n",
        "wc.generate(' '.join(text for text in df[df['label']=='spam'].text))\n",
        "plt.figure(figsize=(15, 10))\n",
        "plt.title('Top words for Spam messages', fontdict={'size':22})\n",
        "plt.imshow(wc)\n",
        "plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHQbjdNubTzq"
      },
      "source": [
        "<h2>Vectorization</h2>\n",
        "\n",
        "We currently have each text record in string format. We need to convert each of those records into a vector that our models can work with. We will first do this using the bag-of-words model.\n",
        "\n",
        "We will use two major approaches here\n",
        "\n",
        "* **CountVectorizer** - Working on frequency of each word in the given string.\n",
        "\n",
        "* **Term frequency-inverse document frqeuency TFIDF** - Works on frequency divided by the appearance of the given word in the total documents."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "2H_Q-onxbTzq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67836da0-c485-46d8-f015-e79e417e092a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 5572)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "x = df['text']\n",
        "y = df['label_num']\n",
        "\n",
        "len(x), len(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "wmhFTemSbTzr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f334216-a49a-4426-d9de-4af7193e9dfc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4179 4179\n",
            "1393 1393\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y)\n",
        "print(len(x_train), len(y_train))\n",
        "print(len(x_test), len(y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "CZeSfKTDbTzr"
      },
      "outputs": [],
      "source": [
        "# First working with count vectorizer\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "# instantiate the vectorizer\n",
        "count = CountVectorizer()\n",
        "count.fit(x)\n",
        "\n",
        "x_train_num = count.transform(x_train)\n",
        "x_test_num = count.transform(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGfRWGYWbTzr"
      },
      "source": [
        "The CountVectorizer model can be tuned in a variety of ways:\n",
        "\n",
        "* Stop words - Extremely common words can be omitted by the model by setting this parameter to the language corresponding to the text.\n",
        "\n",
        "* ngram_range - It pairs up words together as features. If we consider bigrams and we have a sentence \"I am happy\", we will have two features - [\"I am\", \"am happy\"]. We can define a range of ngrams, so if we have the same sentence with a range from 1 to 2, our features will be:  `[\"I\", \"am\", \"happy\", \"I am\", \"am happy\"]`. This increase is features helps to fine tune the model.\n",
        "\n",
        "* min_df, max_df - Minimum and maximum frequencies of words of n-grams that can be used as features. If either of the conditions are not met, the feature will be omitted.\n",
        "\n",
        "* max_features - Choose the most frequent words and drop everything else."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "hpaGBv5xbTzr"
      },
      "outputs": [],
      "source": [
        "# Example of a tuned model\n",
        "count_tuned = CountVectorizer(stop_words='english', ngram_range=(1,2), min_df=0.1, max_df=0.7, max_features=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "y2_h-DwobTzr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb9b0f96-8148-4449-e994-8fed3e142090"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<4179x6696 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 32374 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "# Working with TF-IDF now\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "# We are using transformer here\n",
        "# If we use vectorizer, we can directly use the text\n",
        "tfidf = TfidfTransformer()\n",
        "\n",
        "tfidf.fit(x_train_num)\n",
        "x_train_tfidf = tfidf.transform(x_train_num)\n",
        "\n",
        "x_train_tfidf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "stMzLo3NbTzr"
      },
      "source": [
        "<h2>Working with Embeddings - GloVe</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "cz2aUBrVbTzr"
      },
      "outputs": [],
      "source": [
        "text = df['text']\n",
        "label = df['label_num']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "Ke4YuaK3bTzr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b16031e-b805-46b8-d267-c4f4b0664a19"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6724"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "# Calculating the total vocabulary\n",
        "tk = Tokenizer()\n",
        "tk.fit_on_texts(text)\n",
        "\n",
        "vocab = len(tk.word_index)+1\n",
        "vocab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7Rd4foTbTzr"
      },
      "source": [
        "Now we will proceed with converting the text to numerical values and also padding the vectors so each of them are of equal length. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "BnSQtL-WbTzr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c26315af-7734-4b32-ea82-295013961b4d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "# Maximum length\n",
        "max_len = np.max(df['text'].apply(lambda x: len(x.split())).values)\n",
        "max_len"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "gaCkx6MxbTzr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68ae242e-07fd-4053-9173-28250d325406"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       go jurong point crazi avail bugi n great world...\n",
              "1                                     ok lar joke wif oni\n",
              "2       free entri wkli comp win fa cup final tkts may...\n",
              "3                             dun say ear hor alreadi say\n",
              "4               nah dont think goe usf live around though\n",
              "                              ...                        \n",
              "5567    time tri contact å£ pound prize claim easi cal...\n",
              "5568                              ì b go esplanad fr home\n",
              "5569                              piti mood soani suggest\n",
              "5570    guy bitch act like id interest buy someth el n...\n",
              "5571                                       rofl true name\n",
              "Name: text, Length: 5572, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "5TT47dLwbTzr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6cf9b600-a91c-4ad4-c5e6-af1ac6798e86"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   2, 3180,  274, ...,    0,    0,    0],\n",
              "       [   8,  236,  527, ...,    0,    0,    0],\n",
              "       [   9,  356,  588, ...,    0,    0,    0],\n",
              "       ...,\n",
              "       [6722, 1002, 6723, ...,    0,    0,    0],\n",
              "       [ 138, 1251, 1603, ...,    0,    0,    0],\n",
              "       [1987,  378,  170, ...,    0,    0,    0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "def embedding(text):\n",
        "    return tk.texts_to_sequences(text)\n",
        "\n",
        "train_padded = pad_sequences(embedding(text), 80, padding='post')\n",
        "train_padded"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7rthEL5XbTzs"
      },
      "source": [
        "<h2>GloVe Embeddings</h2>\n",
        "\n",
        "These embeddings are based on the principle that we can derive sematic relationships between words from their co-occurence matrix. This embedding focuses on words co-occurrences over the whole corpus. \n",
        "\n",
        "They are a form of word representation that try to merge human understanding of languages into their structure. They have a learned representation in an n-dimension space, where words with similar meanings have similar embeddings. Two similar words are represented by almost similar vectors that are at a small distance in the vector space.\n",
        "\n",
        "When using a vector space, all the words are represented as vectors in a predefined N-dimension vector space. Each word is mapped to a vector and the vector values are learned in a way that resembles a neural network."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DN6dVnDebTzs"
      },
      "outputs": [],
      "source": [
        "# Using our helper functions for GloVe\n",
        "\n",
        "embedding_dict = dict()\n",
        "embedding_dim = 100\n",
        "\n",
        "# Each word is represented in one line in the text file\n",
        "# Format - Word val1 val2 val3......val-n for n-dimension vector space\n",
        "\n",
        "with open('../data/glove.6B.100d.txt') as fp:\n",
        "    for line in fp.readlines():\n",
        "        records = line.split()\n",
        "        word = records[0]\n",
        "        vector = np.asarray(records[1:], dtype='float32')\n",
        "        embedding_dict[word] = vector"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lCbG_BxLbTzs"
      },
      "outputs": [],
      "source": [
        "# Creating a matrix for each word as index (word numerical value extracted from tokenizer\n",
        "# with N-features (corresponding to GloVe)\n",
        "# We will replace the matrix elements by the words and their embeddings\n",
        "\n",
        "# Our embeddings will also consist embeddings for padding\n",
        "embedding_matrix = np.zeros((vocab, embedding_dim))\n",
        "\n",
        "for word, index in tk.word_index.items():\n",
        "    embed_vector = embedding_dict.get(word)\n",
        "    if embed_vector is not None:\n",
        "        embedding_matrix[index] = embed_vector\n",
        "        \n",
        "embedding_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jzkSV1dxbTzs"
      },
      "outputs": [],
      "source": [
        "# We will be creating seaborn and plotly confusion matrices\n",
        "import plotly.figure_factory as ff\n",
        "x_axes = ['Safe','Spam']\n",
        "y_axes = ['Spam', 'Safe']\n",
        "\n",
        "def conf_matrix(z, x=x_axes, y=y_axes):\n",
        "    z = np.flip(z, 0)\n",
        "    # Change each element of z to string \n",
        "    # This allows them to be used as annotations\n",
        "    z_str = [[str(y) for y in x] for x in z]\n",
        "    fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z_str)\n",
        "    \n",
        "    fig.update_layout(title_text='Confusion matrix', xaxis=dict(title='Predicted Value'),\n",
        "                     yaxis=dict(title='Real value'))\n",
        "    \n",
        "    fig['data'][0]['showscale'] = True\n",
        "    return fig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vrx_tFXIbTzs"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "categories=['Safe', 'Spam']\n",
        "def seaborn_conf(y, ypred):\n",
        "    y_true = [\"Safe\", \"Spam\"]\n",
        "    y_pred = [\"Safe\", \"Spam\"]\n",
        "    cf = confusion_matrix(y, ypred)\n",
        "    df_cm = pd.DataFrame(cf, columns=np.unique(y_true), index = np.unique(y_true))\n",
        "    plt.figure(figsize=(8,6))\n",
        "    sns.heatmap(df_cm, annot=True, fmt='g')\n",
        "    plt.title('Confusion matrix')\n",
        "    plt.xlabel('Predicted value')\n",
        "    plt.ylabel('Real value')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCJ0ZULybTzs"
      },
      "source": [
        "<h2>Model creation and prediction</h2>\n",
        "\n",
        "We will first start with the **naive bayes classifier** which comes from a family of simple \"probabilistic classifiers\" based on application of Bayes theroem with strong independent assumptions between features.\n",
        "\n",
        "The model is highly scalable, with number of parameters being linear with number of variables. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SzBNsBkFbTzs"
      },
      "outputs": [],
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "nb = MultinomialNB()\n",
        "\n",
        "# Train the model - CountVectorizer model\n",
        "nb.fit(x_train_num, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FGhn_HVSbTzs"
      },
      "outputs": [],
      "source": [
        "# Class and probability predictions\n",
        "yp_class = nb.predict(x_test_num)\n",
        "yp_prob = nb.predict_proba(x_test_num)[:, 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ClNtr8a4bTzs"
      },
      "outputs": [],
      "source": [
        "from sklearn import metrics\n",
        "print(metrics.accuracy_score(y_test, yp_class))\n",
        "seaborn_conf(y_test, yp_class)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ikZX1rnObTzs"
      },
      "outputs": [],
      "source": [
        "metrics.roc_auc_score(y_test, yp_prob)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bl6Fl4dnbTzs"
      },
      "source": [
        "<h2>Working with Naive Bayes + TF-IDF</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RvYYbgVcbTzs"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "pipe = Pipeline([('bow', CountVectorizer()), \n",
        "                 ('tfid', TfidfTransformer()),  \n",
        "                 ('model', MultinomialNB())])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8qADl2TTbTzs"
      },
      "outputs": [],
      "source": [
        "pipe.fit(x_train, y_train)\n",
        "yp_class = pipe.predict(x_test)\n",
        "print(metrics.accuracy_score(y_test, yp_class))\n",
        "seaborn_conf(y_test, yp_class)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XI7iV7bbTzt"
      },
      "source": [
        "<h2>XGBoost</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "AMpF3sBFUrZs"
      },
      "outputs": [],
      "source": [
        "!pip install xgboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uPISmiZsbTzt"
      },
      "outputs": [],
      "source": [
        "import xgboost as xgb\n",
        "pipe = Pipeline([\n",
        "    ('bow', CountVectorizer()), \n",
        "    ('tfid', TfidfTransformer()),  \n",
        "    ('model', xgb.XGBClassifier(\n",
        "        learning_rate=0.1,\n",
        "        max_depth=6,\n",
        "        n_estimators=90,\n",
        "        use_label_encoder=False,\n",
        "        eval_metric='auc'\n",
        "    ))\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UDCumywBbTzt"
      },
      "outputs": [],
      "source": [
        "pipe.fit(x_train, y_train)\n",
        "yp_class_test = pipe.predict(x_test)\n",
        "yp_class_train = pipe.predict(x_train)\n",
        "\n",
        "print('Training accuracy score: {}'.format(metrics.accuracy_score(y_train, yp_class_train)))\n",
        "print('Testing accuracy score: {}'.format(metrics.accuracy_score(y_test, yp_class_test)))\n",
        "\n",
        "seaborn_conf(y_test, yp_class_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLVWMr-bbTzt"
      },
      "source": [
        "<h2>LSTMs and GloVE embeddings</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mMroy8EwbTzt"
      },
      "outputs": [],
      "source": [
        "train_padded.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eg7rW6vQbTzt"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(train_padded, label, test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_zSu_ACsbTzt"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(input_dim=embedding_matrix.shape[0], \n",
        "                   output_dim=embedding_matrix.shape[1],\n",
        "                   weights=[embedding_matrix],\n",
        "                   input_length=max_len\n",
        "                   )\n",
        "         )\n",
        "model.add(Bidirectional(LSTM(max_len, return_sequences=True, recurrent_dropout=0.15)))\n",
        "model.add(GlobalMaxPool1D())\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(max_len, activation='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(max_len, activation='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2WgO2yjxbTzt"
      },
      "outputs": [],
      "source": [
        "# Defining Callbacks\n",
        "# Checkpoints in case our model stops training due to some circumstance - saving progress\n",
        "checkpoints = ModelCheckpoint('ck_model.h5', monitor='val_loss', verbose=1, save_best_only=True)\n",
        "# Reducing the learning rate if no improvement in validation loss over 5 epochs\n",
        "# This is to train the model better\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', vactor=0.1, verbose=1, patience=5, min_lr=0.0001)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2yPzfX0ubTzt"
      },
      "outputs": [],
      "source": [
        "history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test), verbose=1, callbacks=[reduce_lr, checkpoints])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KanoH59zbTzt"
      },
      "outputs": [],
      "source": [
        "# Plotting the results\n",
        "def learning_curve(history, arr):\n",
        "    fig, ax=plt.subplots(1, 2, figsize=(20, 5))\n",
        "    for idx in range(2):\n",
        "        ax[idx].plot(history.history[arr[idx][0]])\n",
        "        ax[idx].plot(history.history[arr[idx][1]])\n",
        "        ax[idx].legend([arr[idx][0], arr[idx][1]])\n",
        "        ax[idx].set_xlabel('Epochs')\n",
        "        ax[idx].set_ylabel('Value')\n",
        "        ax[idx].set_title(arr[idx][0]+' X '+ arr[idx][1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OCM5efEGbTzt"
      },
      "outputs": [],
      "source": [
        "learning_curve(history, [['loss', 'val_loss'], ['accuracy', 'val_accuracy']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D1URS30kbTzt"
      },
      "outputs": [],
      "source": [
        "yp = model.predict(x_test)\n",
        "yp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jBdGjLRHbTzt"
      },
      "outputs": [],
      "source": [
        "yp = (model.predict(x_test)>0.5).astype('int32')\n",
        "yp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r4FwSEr4bTzu"
      },
      "outputs": [],
      "source": [
        "seaborn_conf(y_test, yp)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rIqqPvcbTzu"
      },
      "source": [
        "<h2>Transformers - BERT</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aLT-LVM2bTzu"
      },
      "source": [
        "BERT has revolutionized the world of NLP by providing state-of-the-art results on many NLP tasks. BERT stands for Bidirectional Encoder Representation from Transformer. It is the state-of-the-art embedding model published by Google. It has created a major breakthrough in the field of NLP by providing greater results in many NLP tasks, such as question answering, text generation, sentence classification, and many more besides. One of the major reasons for the success of BERT is that it is a context-based embedding model, unlike other popular embedding models, such as word2vec, which are context-free."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uf4vgWgjbTzu"
      },
      "outputs": [],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "znGH9meZbTzu"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "import transformers\n",
        "from tqdm.notebook import tqdm\n",
        "from tokenizers import BertWordPieceTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aGokzdJ9bTzu"
      },
      "outputs": [],
      "source": [
        "from transformers import BertTokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')\n",
        "\n",
        "def bert_encode(data, maximum_length):\n",
        "    input_ids=[]\n",
        "    attention_masks=[]\n",
        "    for text in data:\n",
        "        encoded = tokenizer.encode_plus(text, add_special_tokens=True, max_length = maximum_length, pad_to_max_length=True, return_attention_mask=True)\n",
        "        input_ids.append(encoded['input_ids'])\n",
        "        attention_masks.append(encoded['attention_mask'])\n",
        "    return np.array(input_ids), np.array(attention_masks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EU2JCaKIbTzu"
      },
      "outputs": [],
      "source": [
        "np.max(df['text'].apply(lambda x: len(x.split())).values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5gVUFY2TbTzu"
      },
      "outputs": [],
      "source": [
        "bt_text = df['text']\n",
        "bt_label = df['label_num']\n",
        "\n",
        "bt_ids, bt_masks = bert_encode(bt_text, 80)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nv9Vcr15bTzu"
      },
      "outputs": [],
      "source": [
        "from transformers import TFBertModel\n",
        "def create_model(bert_model):\n",
        "    \n",
        "    input_ids = tf.keras.Input(shape=(80,),dtype='int32')\n",
        "    attention_masks = tf.keras.Input(shape=(80,),dtype='int32')\n",
        "\n",
        "    output = bert_model([input_ids,attention_masks])\n",
        "    output = output[1]\n",
        "    output = tf.keras.layers.Dense(32,activation='relu')(output)\n",
        "    output = tf.keras.layers.Dropout(0.2)(output)\n",
        "    output = tf.keras.layers.Dense(1,activation='sigmoid')(output)\n",
        "    \n",
        "    model = tf.keras.models.Model(inputs = [input_ids,attention_masks],outputs = output)\n",
        "    model.compile(Adam(lr=1e-5), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lFBNx-OnbTzu"
      },
      "outputs": [],
      "source": [
        "bert_model = TFBertModel.from_pretrained('bert-base-uncased')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oUQ0B9JDbTzu"
      },
      "outputs": [],
      "source": [
        "model = create_model(bert_model)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2hr4E8LrbTzu"
      },
      "outputs": [],
      "source": [
        "history = model.fit([bt_ids, bt_masks], bt_label, validation_split=0.25, epochs=3, batch_size=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jwi_PISCbTzu"
      },
      "outputs": [],
      "source": [
        "learning_curve(history, [['loss', 'val_loss'],['accuracy', 'val_accuracy']])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uNyb4y6sbTzv"
      },
      "source": [
        "<h2>Working with disaster tweets - Dataset</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v3uw3v5bbTzv"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('../input/nlp-getting-started/train.csv')\n",
        "df_test = pd.read_csv('../input/nlp-getting-started/test.csv')\n",
        "\n",
        "df_train = df_train.dropna(axis=1)\n",
        "df_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JJcC1IWgbTzv"
      },
      "outputs": [],
      "source": [
        "df.groupby('target').count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uc1p_iP7bTzv"
      },
      "outputs": [],
      "source": [
        "# We can use .agg('count').values also\n",
        "class_counts = df.groupby('target').id.count().values\n",
        "class_counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SBrm21-6bTzv"
      },
      "outputs": [],
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Bar(\n",
        "        x=['Fake disaster'],\n",
        "        y=[class_counts[0]],\n",
        "        name='Fake',\n",
        "        text=[class_counts[0]],\n",
        "        textposition='auto',\n",
        "        marker_color=pblue\n",
        ")\n",
        "             )\n",
        "fig.add_trace(go.Bar(\n",
        "        x=['Real disaster'],\n",
        "        y=[class_counts[1]],\n",
        "        name='Real',\n",
        "        text=[class_counts[1]],\n",
        "        textposition='auto',\n",
        "        marker_color=pg\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Class distribution in the dataset')\n",
        "\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r4e5184YbTzv"
      },
      "outputs": [],
      "source": [
        "len_real = df[df['target']==1].text.apply(lambda x: len(x.split())).value_counts().sort_index()\n",
        "len_fake = df[df['target']==0].text.apply(lambda x: len(x.split())).value_counts().sort_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Kj8G-pvbTzv"
      },
      "outputs": [],
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=len_real.index,\n",
        "    y=len_real.values,\n",
        "    name='Real disaster',\n",
        "    fill='tozeroy',\n",
        "    marker_color=pblue,\n",
        "))\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=len_fake.index,\n",
        "    y=len_fake.values,\n",
        "    name='Fake disaster',\n",
        "    fill='tozeroy',\n",
        "    marker_color=pg,\n",
        "))\n",
        "fig.update_layout(\n",
        "    title='<span style=\"font-size:32px; font-family:Times New Roman\">Data Roles in Different Fields</span>'\n",
        ")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UasL5kOibTzv"
      },
      "source": [
        "<h2>Data pre-processing and cleaning</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DIzgUwnebTzv"
      },
      "outputs": [],
      "source": [
        "def remove_url(text):\n",
        "    url = re.compile(r'https?://\\S+|www\\.\\S+')\n",
        "    return url.sub(r'', text)\n",
        "\n",
        "\n",
        "def remove_emoji(text):\n",
        "    emoji_pattern = re.compile(\n",
        "        '['\n",
        "        u'\\U0001F600-\\U0001F64F'  # emoticons\n",
        "        u'\\U0001F300-\\U0001F5FF'  # symbols & pictographs\n",
        "        u'\\U0001F680-\\U0001F6FF'  # transport & map symbols\n",
        "        u'\\U0001F1E0-\\U0001F1FF'  # flags (iOS)\n",
        "        u'\\U00002702-\\U000027B0'\n",
        "        u'\\U000024C2-\\U0001F251'\n",
        "        ']+',\n",
        "        flags=re.UNICODE)\n",
        "    return emoji_pattern.sub(r'', text)\n",
        "\n",
        "\n",
        "def remove_html(text):\n",
        "    html = re.compile(r'<.*?>|&([a-z0-9]+|#[0-9]{1,6}|#x[0-9a-f]{1,6});')\n",
        "    return re.sub(html, '', text)\n",
        "\n",
        "def clean_text(text):\n",
        "    '''Make text lowercase, remove text in square brackets,remove links,remove punctuation\n",
        "    and remove words containing numbers.'''\n",
        "    text = str(text).lower()\n",
        "    text = re.sub('\\[.*?\\]', '', text)\n",
        "    text = re.sub(\n",
        "        'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', \n",
        "        '', \n",
        "        text\n",
        "    )\n",
        "    text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
        "    text = re.sub('<.*?>+', '', text)\n",
        "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
        "    text = re.sub('\\n', '', text)\n",
        "    text = re.sub('\\w*\\d\\w*', '', text)\n",
        "    \n",
        "    text = remove_url(text)\n",
        "    text = remove_emoji(text)\n",
        "    text = remove_html(text)\n",
        "    \n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0SCQj4C0bTzv"
      },
      "outputs": [],
      "source": [
        "# Testing the function\n",
        "remove_emoji(\"Omg another Earthquake 😔😔\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J8tD8IjLbTzv"
      },
      "outputs": [],
      "source": [
        "stopw = stopwords.words('english')\n",
        "more = ['u', 'im', 'c']\n",
        "stopw = stopw + more\n",
        "\n",
        "stemmer = nltk.SnowballStemmer('english')\n",
        "\n",
        "def data_cleaning(text):\n",
        "    text = clean_text(text)\n",
        "    text = ' '.join(stemmer.stem(word) for word in text.split(' ') if word not in stopw)\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BMvdTaUqbTzw"
      },
      "outputs": [],
      "source": [
        "df['cleaned_text'] = df['text'].apply(data_cleaning)\n",
        "df_test['cleaned_text'] = df_test['text'].apply(data_cleaning)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "evHQRSjubTzw"
      },
      "outputs": [],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YImcqD1ebTzw"
      },
      "source": [
        "<h2>WordCloud analysis</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UlTNh6cibTzw"
      },
      "outputs": [],
      "source": [
        "def corpus(df, label):\n",
        "    corpus=[]\n",
        "    for x in df[df['target']==label]['cleaned_text'].str.split():\n",
        "        for i in x:\n",
        "            corpus.append(i)\n",
        "    return corpus"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TAvzb2_WbTzw"
      },
      "outputs": [],
      "source": [
        "corpus_reald = corpus(df, 1)\n",
        "dic = defaultdict(int)\n",
        "\n",
        "# Creating a dictionary with frequency of words\n",
        "for word in corpus_reald:\n",
        "    dic[word]+=1\n",
        "    \n",
        "# Sorting words by descending frequency\n",
        "top = sorted(dic.items(), key=lambda x: x[1], reverse=True)[:10]\n",
        "top"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aO1RqmmRbTzw"
      },
      "outputs": [],
      "source": [
        "twitter_mask = np.array(Image.open('/kaggle/input/masksforwordclouds/twitter_mask3.jpg'))\n",
        "\n",
        "wc = WordCloud(\n",
        "    background_color='white', \n",
        "    max_words=200, \n",
        "    mask=twitter_mask,\n",
        ")\n",
        "wc.generate(' '.join(text for text in df.loc[df['target'] == 1, 'cleaned_text']))\n",
        "plt.figure(figsize=(18,10))\n",
        "plt.title('Wordcloud for real disasters', \n",
        "          fontdict={'size': 22,  'verticalalignment': 'bottom'})\n",
        "plt.imshow(wc)\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OM1U6i4ubTzw"
      },
      "outputs": [],
      "source": [
        "corpus_faked = corpus(df, 0)\n",
        "dic = defaultdict(int)\n",
        "\n",
        "# Creating a dictionary with frequency of words\n",
        "for word in corpus_faked:\n",
        "    dic[word]+=1\n",
        "    \n",
        "# Sorting words by descending frequency\n",
        "top = sorted(dic.items(), key=lambda x: x[1], reverse=True)[:10]\n",
        "top"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B4xqFATubTzw"
      },
      "outputs": [],
      "source": [
        "twitter_mask = np.array(Image.open('/kaggle/input/masksforwordclouds/twitter_mask3.jpg'))\n",
        "\n",
        "wc = WordCloud(\n",
        "    background_color='white', \n",
        "    max_words=200, \n",
        "    mask=twitter_mask,\n",
        ")\n",
        "wc.generate(' '.join(text for text in df.loc[df['target'] == 0, 'cleaned_text']))\n",
        "plt.figure(figsize=(18,10))\n",
        "plt.title('Wordcloud for fake disasters', \n",
        "          fontdict={'size': 22,  'verticalalignment': 'bottom'})\n",
        "plt.imshow(wc)\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93oumaJObTzw"
      },
      "source": [
        "<h2>Model creation and testing</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tG0vorribTzw"
      },
      "outputs": [],
      "source": [
        "x = df['cleaned_text']\n",
        "y = df['target']\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y)\n",
        "len(x_train), len(y_train), len(x_test), len(y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dkdZoJYVbTzw"
      },
      "outputs": [],
      "source": [
        "pipe = Pipeline([\n",
        "    ('bow', CountVectorizer()), \n",
        "    ('tfid', TfidfTransformer()),  \n",
        "    ('model', xgb.XGBClassifier(\n",
        "        use_label_encoder=False,\n",
        "        eval_metric='auc',\n",
        "    ))\n",
        "])\n",
        "\n",
        "pipe.fit(x_train, y_train)\n",
        "yp_test = pipe.predict(x_test)\n",
        "yp_train = pipe.predict(x_train)\n",
        "\n",
        "print('Training accuracy: {}'.format(metrics.accuracy_score(y_train, yp_train)))\n",
        "print('Testing accuracy: {}'.format(metrics.accuracy_score(y_test, yp_test)))\n",
        "\n",
        "seaborn_conf(y_test, yp_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TercnODvbTzw"
      },
      "source": [
        "<h3>GloVE - LSTM</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rh-1scsQbTzw"
      },
      "outputs": [],
      "source": [
        "training = df['cleaned_text'].values\n",
        "testing = df_test['cleaned_text'].values\n",
        "# Target labels\n",
        "labels = df['target'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EC42lAPWbTzw"
      },
      "outputs": [],
      "source": [
        "# Word tokenizer\n",
        "tk = Tokenizer()\n",
        "tk.fit_on_texts(training)\n",
        "\n",
        "vocab = len(tk.word_index)+1\n",
        "vocab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lf1gQo1mbTzx"
      },
      "outputs": [],
      "source": [
        "def metric_calculation(y_test, y_pred):\n",
        "    print(\"F1-score: \", f1_score(y_pred, y_test))\n",
        "    print(\"Precision: \", precision_score(y_pred, y_test))\n",
        "    print(\"Recall: \", recall_score(y_pred, y_test))\n",
        "    print(\"Acuracy: \", accuracy_score(y_pred, y_test))\n",
        "    print(\"-\"*50)\n",
        "    print(classification_report(y_pred, y_test))\n",
        "    \n",
        "def embeddings(corpus): \n",
        "    return tk.texts_to_sequences(corpus)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2IutjFRjbTzx"
      },
      "outputs": [],
      "source": [
        "len_train = np.max(df['cleaned_text'].apply(lambda x: len(x)))\n",
        "len_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_bkBioLcbTzx"
      },
      "outputs": [],
      "source": [
        "train_padded_sentences = pad_sequences(\n",
        "    embeddings(training), \n",
        "    len_train, \n",
        "    padding='post'\n",
        ")\n",
        "test_padded_sentences = pad_sequences(\n",
        "    embeddings(testing), \n",
        "    len_train,\n",
        "    padding='post'\n",
        ")\n",
        "\n",
        "train_padded_sentences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5SIJE5ZkbTzx"
      },
      "outputs": [],
      "source": [
        "# As we've already created a GloVe dictionary in the SMS-dataset, we will start with the matrix\n",
        "\n",
        "embedding_matrix = np.zeros((vocab, embedding_dim))\n",
        "\n",
        "for word, index in tk.word_index.items():\n",
        "    embedding_vector = embedding_dict.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix[index] = embedding_vector\n",
        "        \n",
        "embedding_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iRNmJYFDbTzx"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(train_padded_sentences, labels, test_size=0.20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BcdIDlzXbTzx"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(input_dim=embedding_matrix.shape[0], \n",
        "                   output_dim=embedding_matrix.shape[1],\n",
        "                   weights=[embedding_matrix],\n",
        "                   input_length=max_len\n",
        "                   )\n",
        "         )\n",
        "model.add(Bidirectional(LSTM(max_len, return_sequences=True, recurrent_dropout=0.15)))\n",
        "model.add(GlobalMaxPool1D())\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(max_len, activation='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(max_len, activation='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pgBblxl8bTzx"
      },
      "outputs": [],
      "source": [
        "# Callbacks\n",
        "checkpoint = ModelCheckpoint('model.h5', monitor = 'val_loss', verbose = 1, save_best_only = True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, verbose = 1, patience = 5,                        min_lr = 0.001)\n",
        "\n",
        "history = model.fit(x_train, y_train, epochs = 7,batch_size = 32,validation_data = (x_test, y_test),verbose = 1,callbacks = [reduce_lr, checkpoint])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iNj_eU4lbTzx"
      },
      "outputs": [],
      "source": [
        "learning_curve(history, [['loss', 'val_loss'],['accuracy', 'val_accuracy']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CeYSI2M0bTzx"
      },
      "outputs": [],
      "source": [
        "preds = model.predict_classes(x_test)\n",
        "metric_calculation(preds, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c0PVci8NbTzx"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
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      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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