Hate Speech Detection (Final).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6bf7c472",
   "metadata": {},
   "source": [
    "# IMPORTING LIBRARY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7da18e0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # read csv\n",
    "import re # detect username, url, html entity\n",
    "import string\n",
    "import nltk\n",
    "from nltk.corpus import stopwords # remove stopwords\n",
    "from sklearn.feature_extraction.text import CountVectorizer # convert text data to token count\n",
    "from sklearn.model_selection import train_test_split # split dataset into training and test\n",
    "from sklearn.tree import DecisionTreeClassifier # build model\n",
    "from sklearn.metrics import confusion_matrix # for matrix output\n",
    "from sklearn.metrics import accuracy_score # check accuracy score\n",
    "\n",
    "# for graphical representation of test output\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18657465",
   "metadata": {},
   "source": [
    "# READ THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b98aa1e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"twitter.csv\")\n",
    "data.isnull().sum()/data.shape[0] * 100\n",
    "data['labels'] = data['class'].map({0:\"Hate Speech\",\n",
    "                                    1:\"Offensive Language\",\n",
    "                                    2:\"No Hate or Offensive Language\"})\n",
    "data = data[['tweet', 'class', 'labels']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c84d98c",
   "metadata": {},
   "source": [
    "# FUNCTION TO CLEAN THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e02da17a",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "# add rt to remove retweet in dataset (noise)\n",
    "stop_words.add('rt')\n",
    "\n",
    "stemmer = nltk.SnowballStemmer('english')\n",
    "\n",
    "# remove new lines\n",
    "def remove_new_lines(raw_text):\n",
    "    regex = r\"\\n\"\n",
    "    text = re.sub(regex, \"\", raw_text)\n",
    "    return text\n",
    "\n",
    "# remove punctuations\n",
    "def remove_punctuations(raw_text):\n",
    "    pun_regex = r\"[%s]\"%re.escape(string.punctuation)\n",
    "    text = re.sub(pun_regex, \"\", raw_text)\n",
    "    return text\n",
    "\n",
    "# remove html entity:\n",
    "def remove_entity(raw_text):\n",
    "    entity_regex = r\"&[^\\s;]+;\"\n",
    "    text = re.sub(entity_regex, \"\", raw_text)\n",
    "    return text\n",
    "\n",
    "# change the user tags\n",
    "def change_user(raw_text):\n",
    "    regex = r\"@([^ ]+)\"\n",
    "    text = re.sub(regex, \"user\", raw_text)\n",
    "\n",
    "    return text\n",
    "\n",
    "# remove urls\n",
    "def remove_url(raw_text):\n",
    "    url_regex = r\"(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:'\\\".,<>?«»“”‘’]))\"\n",
    "    text = re.sub(url_regex, '', raw_text)\n",
    "\n",
    "    return text\n",
    "\n",
    "# remove unnecessary symbols\n",
    "def remove_noise_symbols(raw_text):\n",
    "    text = raw_text.replace('\"', '')\n",
    "    text = text.replace(\"'\", '')\n",
    "    text = text.replace(\"!\", '')\n",
    "    text = text.replace(\"`\", '')\n",
    "    text = text.replace(\"..\", '')\n",
    "\n",
    "    return text\n",
    "\n",
    "# remove stopwords\n",
    "def remove_stopwords(raw_text):\n",
    "    text = [word for word in raw_text.split(' ')]\n",
    "    text = \" \".join(text)\n",
    "    return text\n",
    "\n",
    "## this function in to clean all the dataset by utilizing all the function above\n",
    "def preprocess(datas):\n",
    "    clean = []\n",
    "    # change the @xxx into \"user\"\n",
    "    clean = [change_user(text) for text in datas]\n",
    "    # remove emojis (specifically unicode emojis)\n",
    "    clean = [remove_entity(text) for text in clean]\n",
    "    # remove new lines\n",
    "    clean = [remove_new_lines(text) for text in clean]\n",
    "    #remove_punctuations\n",
    "    clean = [remove_punctuations(text) for text in clean]\n",
    "    # remove urls\n",
    "    clean = [remove_url(text) for text in clean]\n",
    "    # remove trailing stuff\n",
    "    clean = [remove_noise_symbols(text) for text in clean]\n",
    "    # remove stopwords\n",
    "    clean = [remove_stopwords(text) for text in clean]\n",
    "    return clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01be630d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['tweet'] = preprocess(data['tweet'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ac7e84",
   "metadata": {},
   "source": [
    "# SPLITTING THE DATASET INTO TEST AND VALIDATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c462186b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num test tweet: 8675\n",
      "num train tweet: 16108\n"
     ]
    }
   ],
   "source": [
    "x, y = data['tweet'], data['labels']\n",
    "\n",
    "# to convert tweet(text documents) into a matrix of token counts\n",
    "cv = CountVectorizer()\n",
    "x = cv.fit_transform(x)\n",
    "\n",
    "# function to split dataset into training and testing subsets\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.35, random_state=42)\n",
    "\n",
    "print(f\"num test tweet: {y_test.shape[0]}\")\n",
    "print(f\"num train tweet: {y_train.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67827171",
   "metadata": {},
   "source": [
    "# Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ff913f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "DecisionTreeClassifier()"
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "# to train a decision tree model on training data\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)"
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  },
  {
   "cell_type": "markdown",
   "id": "89efbd22",
   "metadata": {},
   "source": [
    "# Conclusion Matrix and accuracy"
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "id": "f70e3ba7",
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    "y_pred = dt.predict(X_test)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "accuracy_score(y_test, y_pred)"
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  },
  {
   "cell_type": "markdown",
   "id": "2784c55b",
   "metadata": {},
   "source": [
    "# Graphical Representation of test data"
   ]
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   "id": "2ff144f0",
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, fmt='.1f', cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7875be30",
   "metadata": {},
   "source": [
    "# Test RUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c2fd1ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Offensive Language', 'Offensive Language', 'Offensive Language',\n",
       "       'No Hate or Offensive Language', 'No Hate or Offensive Language',\n",
       "       'No Hate or Offensive Language', 'Hate Speech', 'Hate Speech',\n",
       "       'Hate Speech', 'Hate Speech', 'Offensive Language', 'Hate Speech'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = [\"Shut UP\",\n",
    "        \"Bitch\",\n",
    "        \"Let's unite and kill all the people who are protesting against the government\",\n",
    "        \"Do your Work Properly\",\n",
    "        \"You are a coward\",\n",
    "        \"I love you\",\n",
    "        \"@Blackman38Tide: @WhaleLookyHere @HowdyDowdy11 queer gaywad\",\n",
    "        \"@CB_Baby24: @white_thunduh alsarabsss hes a beaner smh you can tell hes a mexican\",\n",
    "        \"@DevilGrimz: @VigxRArts you're fucking gay, blacklisted hoe Holding out for #TehGodClan anyway http://t.co/xUCcwoetmn\",\n",
    "        \"@MarkRoundtreeJr: LMFAOOOO I HATE BLACK PEOPLE https://t.co/RNvD2nLCDR This is why there's black people and niggers\",\n",
    "        \"\"\"@NoChillPaz: At least I'm not a nigger http://t.co/RGJa7CfoiT\n",
    "\n",
    "        Lmfao\"\"\",\n",
    "        \"@NotoriousBM95: @_WhitePonyJr_ Ariza is a snake and a coward but at least he isn't a cripple like your hero Roach lmaoo\"\n",
    "        ]\n",
    "test = preprocess(test)\n",
    "data1 = cv.transform(test).toarray()\n",
    "dt.predict(data1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}