EDA copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to C:\\Users\\Dhruv\n",
      "[nltk_data]     Sharma\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to C:\\Users\\Dhruv\n",
      "[nltk_data]     Sharma\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to C:\\Users\\Dhruv\n",
      "[nltk_data]     Sharma\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.decomposition import NMF\n",
    "from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS\n",
    "from textblob import TextBlob\n",
    "import spacy\n",
    "\n",
    "# Download NLTK resources\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the filre\n",
    "df = pd.read_csv('cyberbullying/youtube_parsed_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>UserIndex</th>\n",
       "      <th>Text</th>\n",
       "      <th>Number of Comments</th>\n",
       "      <th>Number of Subscribers</th>\n",
       "      <th>Membership Duration</th>\n",
       "      <th>Number of Uploads</th>\n",
       "      <th>Profanity in UserID</th>\n",
       "      <th>Age</th>\n",
       "      <th>oh_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>X1</td>\n",
       "      <td>Does N.e.bodyelse Hear her Crazy ass Screamin ...</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>X2</td>\n",
       "      <td>There are so many things that are incorrect wi...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>X3</td>\n",
       "      <td>3:26 hahah my boyfriend showed this song to me...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>X2218</td>\n",
       "      <td>dick beyonce fuck y a ass hole you are truely ...</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>X5</td>\n",
       "      <td>DongHaeTaemin and Kai ;A; luhansehun and bacon...</td>\n",
       "      <td>11</td>\n",
       "      <td>173</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index UserIndex                                               Text  \\\n",
       "0      0        X1  Does N.e.bodyelse Hear her Crazy ass Screamin ...   \n",
       "1      1        X2  There are so many things that are incorrect wi...   \n",
       "2      2        X3  3:26 hahah my boyfriend showed this song to me...   \n",
       "3      3     X2218  dick beyonce fuck y a ass hole you are truely ...   \n",
       "4      4        X5  DongHaeTaemin and Kai ;A; luhansehun and bacon...   \n",
       "\n",
       "   Number of Comments  Number of Subscribers  Membership Duration  \\\n",
       "0                  10                      1                    3   \n",
       "1                   3                      0                    6   \n",
       "2                   7                      0                    3   \n",
       "3                  34                      0                    3   \n",
       "4                  11                    173                    5   \n",
       "\n",
       "   Number of Uploads  Profanity in UserID  Age  oh_label  \n",
       "0                  3                    0   15         0  \n",
       "1                  5                    0   31         0  \n",
       "2                  5                    0   43         1  \n",
       "3                  5                    0   44         1  \n",
       "4                  5                    0   21         0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['UserIndex', 'index'], axis=1)\n",
    "df.rename(columns={'oh_label': 'Label'}, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Text', 'Number of Comments', 'Number of Subscribers',\n",
       "       'Membership Duration', 'Number of Uploads', 'Profanity in UserID',\n",
       "       'Age', 'Label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Comments</th>\n",
       "      <th>Number of Subscribers</th>\n",
       "      <th>Membership Duration</th>\n",
       "      <th>Number of Uploads</th>\n",
       "      <th>Profanity in UserID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3464.000000</td>\n",
       "      <td>3464.000000</td>\n",
       "      <td>3464.000000</td>\n",
       "      <td>3464.000000</td>\n",
       "      <td>3464.000000</td>\n",
       "      <td>3464.000000</td>\n",
       "      <td>3464.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.452367</td>\n",
       "      <td>304.318995</td>\n",
       "      <td>3.714781</td>\n",
       "      <td>10.288395</td>\n",
       "      <td>0.113164</td>\n",
       "      <td>24.879042</td>\n",
       "      <td>0.120381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.862517</td>\n",
       "      <td>15520.532319</td>\n",
       "      <td>1.392837</td>\n",
       "      <td>28.646525</td>\n",
       "      <td>0.316839</td>\n",
       "      <td>13.286361</td>\n",
       "      <td>0.325454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>912377.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>820.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of Comments  Number of Subscribers  Membership Duration  \\\n",
       "count         3464.000000            3464.000000          3464.000000   \n",
       "mean            15.452367             304.318995             3.714781   \n",
       "std             10.862517           15520.532319             1.392837   \n",
       "min              1.000000               0.000000             2.000000   \n",
       "25%              6.000000               0.000000             3.000000   \n",
       "50%             14.000000               2.000000             3.000000   \n",
       "75%             23.000000               7.000000             4.000000   \n",
       "max             50.000000          912377.000000             9.000000   \n",
       "\n",
       "       Number of Uploads  Profanity in UserID          Age        Label  \n",
       "count        3464.000000          3464.000000  3464.000000  3464.000000  \n",
       "mean           10.288395             0.113164    24.879042     0.120381  \n",
       "std            28.646525             0.316839    13.286361     0.325454  \n",
       "min             1.000000             0.000000    13.000000     0.000000  \n",
       "25%             5.000000             0.000000    18.000000     0.000000  \n",
       "50%             5.000000             0.000000    21.000000     0.000000  \n",
       "75%             5.000000             0.000000    27.000000     0.000000  \n",
       "max           820.000000             1.000000   112.000000     1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text                     [Does N.e.bodyelse Hear her Crazy ass Screamin...\n",
      "Number of Comments       [10, 3, 7, 34, 11, 12, 1, 19, 8, 6, 20, 16, 22...\n",
      "Number of Subscribers    [1, 0, 173, 76, 7, 25, 171, 5, 10, 4, 2, 28, 1...\n",
      "Membership Duration                               [3, 6, 5, 2, 4, 7, 8, 9]\n",
      "Number of Uploads        [3, 5, 1, 2, 34, 11, 4, 18, 8, 159, 6, 23, 191...\n",
      "Profanity in UserID                                                 [0, 1]\n",
      "Age                      [15, 31, 43, 44, 21, 27, 23, 56, 20, 26, 22, 3...\n",
      "Label                                                               [0, 1]\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "unique_values_df = df.apply(pd.Series.unique)\n",
    "print(unique_values_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing values:  Text                     0\n",
      "Number of Comments       0\n",
      "Number of Subscribers    0\n",
      "Membership Duration      0\n",
      "Number of Uploads        0\n",
      "Profanity in UserID      0\n",
      "Age                      0\n",
      "Label                    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing = df.isnull().sum()\n",
    "print(\"missing values: \", missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are no missing values \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "stop_words = set(stopwords.words('english'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def preprocess_text(text):\n",
    "    tokens = word_tokenize(text)\n",
    "    tokens = [token.lower() for token in tokens if token.isalnum()]\n",
    "    tokens = [token for token in tokens if token not in stop_words]\n",
    "    return tokens\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['processed_text'] = df['Text'].apply(preprocess_text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_tokens = [token for sublist in df['processed_text'] for token in sublist]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Calculate word frequency\n",
    "word_freq = pd.Series(all_tokens).value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "like      5151\n",
      "love      3162\n",
      "lol       2463\n",
      "one       2340\n",
      "u         2328\n",
      "get       2264\n",
      "people    2100\n",
      "song      2087\n",
      "would     2054\n",
      "video     2013\n",
      "good      1981\n",
      "know      1858\n",
      "really    1751\n",
      "think     1630\n",
      "3         1463\n",
      "im        1429\n",
      "fuck      1328\n",
      "go        1316\n",
      "make      1310\n",
      "got       1307\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(word_freq.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of Word Frequency\n",
    "plt.figure(figsize=(10, 6))\n",
    "word_freq.head(20).plot(kind='bar', color='skyblue')\n",
    "plt.title('Top 20 Most Frequent Words')\n",
    "plt.xlabel('Words')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Topic Modeling using Latent Dirichlet Allocation (LDA)\n",
    "# Convert text data to document-term matrix\n",
    "vectorizer = CountVectorizer(max_df=0.95, min_df=2, stop_words='english')\n",
    "doc_term_matrix = vectorizer.fit_transform(df['Text'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LatentDirichletAllocation(n_components=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LatentDirichletAllocation</label><div class=\"sk-toggleable__content\"><pre>LatentDirichletAllocation(n_components=5, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LatentDirichletAllocation(n_components=5, random_state=42)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize LDA model\n",
    "num_topics = 5  # Number of topics to extract\n",
    "lda_model = LatentDirichletAllocation(n_components=num_topics, random_state=42)\n",
    "# Fit LDA model to the document-term matrix\n",
    "lda_model.fit(doc_term_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to display topics and associated words\n",
    "def display_topics(model, feature_names, num_top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(f\"Topic {topic_idx + 1}:\")\n",
    "        print(\" \".join([feature_names[i] for i in topic.argsort()[:-num_top_words - 1:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 10 words for each topic:\n",
      "Topic 1:\n",
      "love ur song good video amazing really ich die voice\n",
      "Topic 2:\n",
      "like just love lol song video know good im really\n",
      "Topic 3:\n",
      "que la en te el si es para se est\n",
      "Topic 4:\n",
      "people just like don know think god good way make\n",
      "Topic 5:\n",
      "like just watch make good fuck don video fucking shit\n"
     ]
    }
   ],
   "source": [
    "# Display the topics and associated words\n",
    "num_top_words = 10\n",
    "feature_names = vectorizer.get_feature_names_out()  # Retrieve feature names\n",
    "print(f\"\\nTop {num_top_words} words for each topic:\")\n",
    "display_topics(lda_model, feature_names, num_top_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Function to perform sentiment analysis on text data\n",
    "def analyze_sentiment(text):\n",
    "    analysis = TextBlob(text)\n",
    "    # Get the sentiment polarity (-1 to 1)\n",
    "    sentiment_polarity = analysis.sentiment.polarity\n",
    "    # Return the sentiment polarity\n",
    "    return sentiment_polarity\n",
    "\n",
    "# Apply sentiment analysis to each text entry in the DataFrame\n",
    "df['Sentiment_Polarity'] = df['Text'].apply(analyze_sentiment)\n",
    "\n",
    "# Classify sentiment labels based on polarity values\n",
    "def classify_sentiment(polarity):\n",
    "    if polarity > 0:\n",
    "        return 'Positive'\n",
    "    elif polarity < 0:\n",
    "        return 'Negative'\n",
    "    else:\n",
    "        return 'Neutral'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                   Text  Sentiment_Polarity  \\\n",
      "0     Does N.e.bodyelse Hear her Crazy ass Screamin ...           -0.040000   \n",
      "1     There are so many things that are incorrect wi...           -0.110606   \n",
      "2     3:26 hahah my boyfriend showed this song to me...            0.433333   \n",
      "3     dick beyonce fuck y a ass hole you are truely ...           -0.186975   \n",
      "4     DongHaeTaemin and Kai ;A; luhansehun and bacon...            0.482143   \n",
      "...                                                 ...                 ...   \n",
      "3459  She pretty I love this song I miss the old kel...            0.304464   \n",
      "3460  Status-Online Im ZxkillergirlzX! I'm Zxkillerg...            0.000000   \n",
      "3461  JR so cute EXO M Better I agree like yeah yeah...            0.370833   \n",
      "3462                                                ! !            0.000000   \n",
      "3463  great video and MERRY CHRISTMAS from greece :*...            0.332612   \n",
      "\n",
      "     Sentiment_Label  \n",
      "0           Negative  \n",
      "1           Negative  \n",
      "2           Positive  \n",
      "3           Negative  \n",
      "4           Positive  \n",
      "...              ...  \n",
      "3459        Positive  \n",
      "3460         Neutral  \n",
      "3461        Positive  \n",
      "3462         Neutral  \n",
      "3463        Positive  \n",
      "\n",
      "[3464 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Apply sentiment label classification to each sentiment polarity\n",
    "df['Sentiment_Label'] = df['Sentiment_Polarity'].apply(classify_sentiment)\n",
    "\n",
    "# Display the DataFrame with sentiment analysis results\n",
    "print(df[['Text', 'Sentiment_Polarity', 'Sentiment_Label']])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding the number of positive negative and neutral sentiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Positive Sentiments: 2808\n",
      "Number of Neutral Sentiments: 166\n",
      "Number of Negative Sentiments: 490\n"
     ]
    }
   ],
   "source": [
    "# Count the occurrences of each sentiment label\n",
    "sentiment_counts = df['Sentiment_Label'].value_counts()\n",
    "\n",
    "# Print the counts of positive, neutral, and negative sentiments\n",
    "print(\"Number of Positive Sentiments:\", sentiment_counts.get('Positive', 0))\n",
    "print(\"Number of Neutral Sentiments:\", sentiment_counts.get('Neutral', 0))\n",
    "print(\"Number of Negative Sentiments:\", sentiment_counts.get('Negative', 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                   Text  Sentiment_Polarity  \\\n",
      "0     Does N.e.bodyelse Hear her Crazy ass Screamin ...           -0.040000   \n",
      "1     There are so many things that are incorrect wi...           -0.110606   \n",
      "2     3:26 hahah my boyfriend showed this song to me...            0.433333   \n",
      "3     dick beyonce fuck y a ass hole you are truely ...           -0.186975   \n",
      "4     DongHaeTaemin and Kai ;A; luhansehun and bacon...            0.482143   \n",
      "...                                                 ...                 ...   \n",
      "3459  She pretty I love this song I miss the old kel...            0.304464   \n",
      "3460  Status-Online Im ZxkillergirlzX! I'm Zxkillerg...            0.000000   \n",
      "3461  JR so cute EXO M Better I agree like yeah yeah...            0.370833   \n",
      "3462                                                ! !            0.000000   \n",
      "3463  great video and MERRY CHRISTMAS from greece :*...            0.332612   \n",
      "\n",
      "     Sentiment_Label  Sentiment  \n",
      "0           Negative          1  \n",
      "1           Negative          1  \n",
      "2           Positive          0  \n",
      "3           Negative          1  \n",
      "4           Positive          0  \n",
      "...              ...        ...  \n",
      "3459        Positive          0  \n",
      "3460         Neutral          1  \n",
      "3461        Positive          0  \n",
      "3462         Neutral          1  \n",
      "3463        Positive          0  \n",
      "\n",
      "[3464 rows x 4 columns]\n",
      "Number of Positive or Neutral Sentiments: 2808\n",
      "Number of Negative Sentiments: 656\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Classify sentiment labels based on polarity values\n",
    "def classify_sentiment(polarity):\n",
    "    if polarity > 0:\n",
    "        return 0  # Positive or Neutral sentiment\n",
    "    else:\n",
    "        return 1  # Negative sentiment\n",
    "\n",
    "# Apply sentiment label classification to each sentiment polarity\n",
    "df['Sentiment'] = df['Sentiment_Polarity'].apply(classify_sentiment)\n",
    "\n",
    "# Display the DataFrame with sentiment analysis results and the new sentiment column\n",
    "print(df[['Text', 'Sentiment_Polarity', 'Sentiment_Label', 'Sentiment']])\n",
    "\n",
    "# Count the occurrences of each sentiment label\n",
    "sentiment_counts = df['Sentiment'].value_counts()\n",
    "\n",
    "# Print the counts of positive and negative sentiments\n",
    "print(\"Number of Positive or Neutral Sentiments:\", sentiment_counts.get(0, 0))\n",
    "print(\"Number of Negative Sentiments:\", sentiment_counts.get(1, 0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Assuming 'dataset' is your DataFrame and 'Text' is the column containing text data\n",
    "text_data = df['Text']\n",
    "\n",
    "# Create a TF-IDF vectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "\n",
    "# Fit and transform the text data to TF-IDF matrix\n",
    "tfidf_matrix = tfidf_vectorizer.fit_transform(text_data)\n",
    "\n",
    "# Convert TF-IDF matrix to a DataFrame (optional)\n",
    "tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=tfidf_vectorizer.get_feature_names_out())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 10 words for each topic:\n",
      "Topic 1:\n",
      "like just people don know video think fucking fuck really\n",
      "Topic 2:\n",
      "love song amazing omg voice cute good great beautiful really\n",
      "Topic 3:\n",
      "que la el es en se lo por los si\n",
      "Topic 4:\n",
      "lol ur xd im lmao dont like funny omg got\n",
      "Topic 5:\n",
      "ich ist das und die du der nicht ein es\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import NMF\n",
    "\n",
    "# Preprocess the text data\n",
    "# Assuming 'Text' is the column name containing text data\n",
    "text_data = df['Text']\n",
    "\n",
    "# Create a TF-IDF vectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2, stop_words='english')\n",
    "\n",
    "# Fit and transform the text data to TF-IDF matrix\n",
    "tfidf_matrix = tfidf_vectorizer.fit_transform(text_data)\n",
    "\n",
    "# Apply NMF to discover topics\n",
    "num_topics = 5  # Define the number of topics\n",
    "nmf_model = NMF(n_components=num_topics, random_state=42)\n",
    "\n",
    "# Fit NMF model to the TF-IDF matrix\n",
    "nmf_model.fit(tfidf_matrix)\n",
    "\n",
    "# Display the top words for each topic\n",
    "def display_topics(model, feature_names, num_top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(f\"Topic {topic_idx + 1}:\")\n",
    "        print(\" \".join([feature_names[i] for i in topic.argsort()[:-num_top_words - 1:-1]]))\n",
    "\n",
    "num_top_words = 10  # Number of top words to display for each topic\n",
    "print(f\"\\nTop {num_top_words} words for each topic:\")\n",
    "display_topics(nmf_model, tfidf_vectorizer.get_feature_names_out(), num_top_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Identifying topics related to criminal behavior:\n",
      "\n",
      "Topic 1:\n",
      "like just people don know video think fucking fuck really\n",
      "\n",
      "Topic 2:\n",
      "love song amazing omg voice cute good great beautiful really\n",
      "\n",
      "Topic 3:\n",
      "que la el es en se lo por los si\n",
      "\n",
      "Topic 4:\n",
      "lol ur xd im lmao dont like funny omg got\n",
      "\n",
      "Topic 5:\n",
      "ich ist das und die du der nicht ein es\n",
      "\n",
      "Top words related to criminal behavior:\n",
      "\n",
      "Topic 1:\n",
      "like just people don know video think fucking fuck really\n",
      "\n",
      "Topic 3:\n",
      "que la el es en se lo por los si\n"
     ]
    }
   ],
   "source": [
    "# Display topics and identify relevant topics related to criminal behavior\n",
    "num_top_words = 10  # Number of top words to display for each topic\n",
    "\n",
    "print(\"Identifying topics related to criminal behavior:\")\n",
    "for topic_idx, topic in enumerate(nmf_model.components_):\n",
    "    topic_words = [tfidf_vectorizer.get_feature_names_out()[i] for i in topic.argsort()[:-num_top_words - 1:-1]]\n",
    "    print(f\"\\nTopic {topic_idx + 1}:\")\n",
    "    print(\" \".join(topic_words))\n",
    "\n",
    "# Identify relevant topics related to criminal behavior based on the context of the words\n",
    "relevant_topics = [0, 2]  # Assuming topics 0 and 2 are related to criminal behavior\n",
    "\n",
    "# Inspect top words associated with relevant topics\n",
    "print(\"\\nTop words related to criminal behavior:\")\n",
    "for topic_idx in relevant_topics:\n",
    "    topic_words = [tfidf_vectorizer.get_feature_names_out()[i] for i in nmf_model.components_[topic_idx].argsort()[:-num_top_words - 1:-1]]\n",
    "    print(f\"\\nTopic {topic_idx + 1}:\")\n",
    "    print(\" \".join(topic_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a list of cursed words\n",
    "cursed_words = ['fuck', 'shit', 'asshole', 'bitch', 'damn', 'crap', 'hell', 'bastard', 'piss', 'dick','pussy', 'mf','mother fucker', 'behenchod']\n",
    "\n",
    "# Function to count cursed words in a text\n",
    "def count_cursed_words(text):\n",
    "    count = 0\n",
    "    for word in text.split():\n",
    "        if word.lower() in cursed_words:\n",
    "            count += 1\n",
    "    return count\n",
    "\n",
    "# Add a new column for the number of cursed words\n",
    "df['No_of_Cursed_Words'] = df['Text'].apply(count_cursed_words)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No_of_Cursed_Words\n",
       "0     2056\n",
       "1      604\n",
       "2      295\n",
       "3      173\n",
       "4      111\n",
       "5       93\n",
       "6       41\n",
       "7       27\n",
       "8       20\n",
       "9       15\n",
       "10       7\n",
       "11       6\n",
       "13       4\n",
       "14       3\n",
       "12       3\n",
       "21       1\n",
       "22       1\n",
       "25       1\n",
       "18       1\n",
       "27       1\n",
       "17       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['No_of_Cursed_Words'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                Text  Number of Comments  \\\n",
      "0  Does N.e.bodyelse Hear her Crazy ass Screamin ...                  10   \n",
      "1  There are so many things that are incorrect wi...                   3   \n",
      "2  3:26 hahah my boyfriend showed this song to me...                   7   \n",
      "3  dick beyonce fuck y a ass hole you are truely ...                  34   \n",
      "4  DongHaeTaemin and Kai ;A; luhansehun and bacon...                  11   \n",
      "\n",
      "   Number of Subscribers  Membership Duration  Number of Uploads  \\\n",
      "0                      1                    3                  3   \n",
      "1                      0                    6                  5   \n",
      "2                      0                    3                  5   \n",
      "3                      0                    3                  5   \n",
      "4                    173                    5                  5   \n",
      "\n",
      "   Profanity in UserID  Age  Label  \\\n",
      "0                    0   15      0   \n",
      "1                    0   31      0   \n",
      "2                    0   43      1   \n",
      "3                    0   44      1   \n",
      "4                    0   21      0   \n",
      "\n",
      "                                      processed_text  Sentiment_Polarity  ...  \\\n",
      "0  [hear, crazy, ass, screamin, hoe, everytime, s...           -0.040000  ...   \n",
      "1  [many, things, incorrect, comment, unbelievabl...           -0.110606  ...   \n",
      "2  [hahah, boyfriend, showed, song, love, tooo, s...            0.433333  ...   \n",
      "3  [dick, beyonce, fuck, ass, hole, truely, dog, ...           -0.186975  ...   \n",
      "4  [donghaetaemin, kai, luhansehun, bacon, xd, ta...            0.482143  ...   \n",
      "\n",
      "    zy  zyan  zyjesz  zyn  zyra  zyzz   zz  zzzzz  zzzzzz  \\\n",
      "0  0.0   0.0     0.0  0.0   0.0   0.0  0.0    0.0     0.0   \n",
      "1  0.0   0.0     0.0  0.0   0.0   0.0  0.0    0.0     0.0   \n",
      "2  0.0   0.0     0.0  0.0   0.0   0.0  0.0    0.0     0.0   \n",
      "3  0.0   0.0     0.0  0.0   0.0   0.0  0.0    0.0     0.0   \n",
      "4  0.0   0.0     0.0  0.0   0.0   0.0  0.0    0.0     0.0   \n",
      "\n",
      "   zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz  \n",
      "0                              0.0  \n",
      "1                              0.0  \n",
      "2                              0.0  \n",
      "3                              0.0  \n",
      "4                              0.0  \n",
      "\n",
      "[5 rows x 47872 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Assuming 'dataset' is your DataFrame and 'Text' is the column containing text data\n",
    "text_data = df['Text']\n",
    "\n",
    "# Create a TF-IDF vectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "\n",
    "# Fit and transform the text data to TF-IDF matrix\n",
    "tfidf_matrix = tfidf_vectorizer.fit_transform(text_data)\n",
    "\n",
    "# Convert TF-IDF matrix to a DataFrame\n",
    "tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=tfidf_vectorizer.get_feature_names_out())\n",
    "\n",
    "# Concatenate the TF-IDF DataFrame with the original dataset\n",
    "dataset_with_tfidf = pd.concat([df, tfidf_df], axis=1)\n",
    "\n",
    "# Display the dataset with TF-IDF scores\n",
    "print(dataset_with_tfidf.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Remove columns containing non-numeric values\n",
    "numeric_df = df.select_dtypes(include='number')\n",
    "\n",
    "# Calculate the correlation matrix\n",
    "corr_matrix = numeric_df.corr()\n",
    "\n",
    "# Create a heatmap\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", annot_kws={\"size\": 8})\n",
    "plt.title('Correlation Matrix Heatmap')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Text', 'Number of Comments', 'Number of Subscribers',\n",
       "       'Membership Duration', 'Number of Uploads', 'Profanity in UserID',\n",
       "       'Age', 'Label', 'processed_text', 'Sentiment_Polarity',\n",
       "       'Sentiment_Label', 'Sentiment', 'No_of_Cursed_Words'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dataset' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[39], line 9\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# Create the bar plot\u001b[39;00m\n\u001b[0;32m      8\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m----> 9\u001b[0m ax \u001b[38;5;241m=\u001b[39m sns\u001b[38;5;241m.\u001b[39mcountplot(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProfanity in UserID\u001b[39m\u001b[38;5;124m'\u001b[39m, hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSentiment\u001b[39m\u001b[38;5;124m'\u001b[39m, data\u001b[38;5;241m=\u001b[39m\u001b[43mdataset\u001b[49m)\n\u001b[0;32m     11\u001b[0m \u001b[38;5;66;03m# Add text annotations above each bar\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mpatches:\n",
      "\u001b[1;31mNameError\u001b[0m: name 'dataset' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set the style of the seaborn plots\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create the bar plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax = sns.countplot(x='Profanity in UserID', hue='Sentiment', data=dataset)\n",
    "\n",
    "# Add text annotations above each bar\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                ha='center', va='center', fontsize=10, color='black', xytext=(0, 5),\n",
    "                textcoords='offset points')\n",
    "\n",
    "plt.title('Distribution of Profanity in User ID by Sentiment')\n",
    "plt.xlabel('Profanity in User ID')\n",
    "plt.ylabel('Count')\n",
    "plt.legend(title='Sentiment', loc='upper right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Text', 'Number of Comments', 'Number of Subscribers',\n",
       "       'Membership Duration', 'Number of Uploads', 'Profanity in UserID',\n",
       "       'Age', 'Label', 'processed_text', 'Sentiment_Polarity',\n",
       "       'Sentiment_Label', 'Sentiment', 'No_of_Cursed_Words'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical columns: ['Number of Comments', 'Number of Subscribers', 'Membership Duration', 'Number of Uploads', 'Profanity in UserID', 'Age', 'Label', 'Sentiment_Polarity', 'Sentiment', 'No_of_Cursed_Words']\n"
     ]
    }
   ],
   "source": [
    "# Get the data types of each column in the dataset\n",
    "column_data_types = df.dtypes\n",
    "\n",
    "# Identify numerical columns (those with numeric data types)\n",
    "numerical_columns = column_data_types[column_data_types != 'object'].index.tolist()\n",
    "\n",
    "# Remove the target variable ('oh_label') from numerical_columns if needed\n",
    "if 'oh_label' in numerical_columns:\n",
    "    numerical_columns.remove('oh_label')\n",
    "\n",
    "print(\"Numerical columns:\", numerical_columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'RandomOverSampler' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[42], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Step 2: Oversample the data using RandomOverSampler to reduce imbalance between Labels (Bullying and non bullying)\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m ros \u001b[38;5;241m=\u001b[39m \u001b[43mRandomOverSampler\u001b[49m(random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[0;32m      3\u001b[0m X \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mText\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m      4\u001b[0m y \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLabel\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\n",
      "\u001b[1;31mNameError\u001b[0m: name 'RandomOverSampler' is not defined"
     ]
    }
   ],
   "source": [
    "# Step 2: Oversample the data using RandomOverSampler to reduce imbalance between Labels (Bullying and non bullying)\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X = df['Text'].values.reshape(-1, 1)\n",
    "y = df['Label'].values\n",
    "X_resampled, y_resampled = ros.fit_resample(X, y)\n",
    "\n",
    "# convert text (object) data to string for w2v\n",
    "X_resampled= [str(obj) for obj in X_resampled]\n",
    "X_resampled = np.array(X_resampled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming 'oh_label' is the target variable and 'Profanity in User ID' is a feature\n",
    "X = df['processed_text']\n",
    "y = df['Label']\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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