nlp_grid_search.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.svm import SVC\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "import nltk\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "abbr = {\n",
    "    'ninstagram'        : 'instagram',\n",
    "    'instagramgram'     : 'instagram',\n",
    "    'ig'                : 'instagram',\n",
    "    'strainstagramht'   : 'instagram',\n",
    "    'insta'             : 'instagram',\n",
    "    'rinstagramht'      : 'instagram',\n",
    "    'ninstagramguh'     : 'instagram',\n",
    "    'instagramz'        : 'instagram',\n",
    "    'sinstagramn'       : 'instagram',\n",
    "    'binstagramgest'    : 'instagram',\n",
    "    'pinstagram'        : 'instagram',\n",
    "    'linstagramht'      : 'instagram',\n",
    "    'ninstagramg'       : 'instagram',\n",
    "    'instagramh'        : 'instagram',\n",
    "    'instagramnor'      : 'instagram',\n",
    "    'ninstagramht'      : 'instagram',\n",
    "    'ninstagramgramga'  : 'instagram',\n",
    "    'finstagramht'      : 'instagram',\n",
    "    'binstagram'        : 'instagram',\n",
    "    'hinstagramh'       : 'instagram',\n",
    "    'ninstagramga'      : 'instagram',\n",
    "    'toninstagramht'    : 'instagram',\n",
    "    'minstagramht'      : 'instagram',\n",
    "    'minstagramt'       : 'instagram',\n",
    "    'dwn'               : 'down',\n",
    "    'dawn'              : 'down',\n",
    "    'ta'                : 'that',\n",
    "    'dat'               : 'that',\n",
    "    'dawg'              : 'dude',\n",
    "    'smh'               : 'head',\n",
    "    'fr'                : 'real',\n",
    "    'plz'               : 'please',\n",
    "    'tf'                : 'wtf',\n",
    "    'theyr'             : 'are',\n",
    "    'bc'                : 'because',\n",
    "    'af'                : 'lot',\n",
    "    'u'                 : 'you',\n",
    "    'ppl'               : 'people',\n",
    "    'dm'                : 'message',\n",
    "    'bf'                : 'friend',\n",
    "    'gt'                : 'getting',\n",
    "    'ya'                : 'yes',\n",
    "    'na'                : 'no',\n",
    "    'ur'                : 'your',\n",
    "    'tryna'             : 'to',\n",
    "    'lmfao'             : 'lmao',\n",
    "    'ive'               : 'have'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_points(line):\n",
    "    l = list(line)\n",
    "\n",
    "    for i in range(len(l)):\n",
    "        if i < len(l) - 2 and l[i] == '.' and 'A' <= l[i+2] <= 'Z':\n",
    "            l[i] = ' '\n",
    "\n",
    "    line = ' '.join(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_repetition(s):\n",
    "    # Use regular expression to find repeated substrings\n",
    "    pattern = re.compile(r'(.+?)\\1{%d,}' % 2)\n",
    "    match = pattern.search(s)\n",
    "\n",
    "    # Reduce repetition to two occurrences\n",
    "    while match:\n",
    "        repeated_substring = match.group(1)\n",
    "        s = s.replace(match.group(), repeated_substring, 1)\n",
    "        match = pattern.search(s)\n",
    "\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import wordnet\n",
    "import nltk\n",
    "import re\n",
    "\n",
    "\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "def get_wordnet_pos(word):\n",
    "    \"\"\"Map POS tag to first character lemmatize() accepts\"\"\"\n",
    "    tag = nltk.pos_tag([word])[0][1][0].upper()\n",
    "    tag_dict = {\"J\": wordnet.ADJ,\n",
    "                \"N\": wordnet.NOUN,\n",
    "                \"V\": wordnet.VERB,\n",
    "                \"R\": wordnet.ADV}\n",
    "\n",
    "    return tag_dict.get(tag, wordnet.NOUN)\n",
    "\n",
    "def preprocess(sentence, get='stems'):\n",
    "    sent = re.sub(r'@([a-zA-Z0-9_]+)', 'username', sentence).replace('username:', '') # replace first username\n",
    "    sent = re.sub(r'http?://\\S+', 'weblink', sent)\n",
    "    sent = re.sub(r'&amp', '&', sent)\n",
    "\n",
    "    if sent and sent[0] == '.':\n",
    "        sent = sent[1:]\n",
    "\n",
    "    remove_points(sent)\n",
    "    sent = sent   .replace('RT', '').replace('!', ' ').replace('\"', '').replace(\"\\n\", ' ')\\\n",
    "                        .replace(';', ' ').replace('-', ' ').replace(' and ', ' & ').replace('\\'', '')\\\n",
    "                        .replace('?', '.').replace(',', '').replace('~', ' ').replace('|', ' ').replace('°', ' ')\\\n",
    "                        .replace('`', ' ').replace('~', ' ').replace('*', ' ').replace('+', ' ').replace('/', ' ')\\\n",
    "                        .replace(' # ', ' ').replace('http', ' ').replace('t.co', ' ').replace('\\\\', ' ').replace('&#', ' ')\n",
    "    for _ in range(4):\n",
    "        sent = sent.replace('  ', ' ').replace('..', '.').replace(' .', '.') # remove multiple points & space\n",
    "    \n",
    "    if sent and sent[0] == ' ':\n",
    "        sent = sent[1:]\n",
    "\n",
    "    sent = sent.lower()\n",
    "\n",
    "    for old in abbr:\n",
    "        new = abbr[old]\n",
    "        sent = sent.replace( ' ' + old + ' ', ' ' + new + ' ' ) # add some space arround the world to avoid matching a part of a word\n",
    "\n",
    "    sent = reduce_repetition(sent)\n",
    "\n",
    "    if get == 'sentence':\n",
    "        return sent\n",
    "    \n",
    "    tokens = nltk.word_tokenize(sent)\n",
    "\n",
    "    for i in range(len(tokens) - 1, 0, -1):\n",
    "        if len(tokens[i].strip()) == 0 or (len(tokens[i].strip()) == 1 and tokens[i].strip() != 'a' and tokens[i].strip() != 'i' and tokens[i].strip() != '&'):\n",
    "            tokens.pop(i)\n",
    "\n",
    "    if get == 'tokens':\n",
    "        return tokens\n",
    "\n",
    "    stems = [lemmatizer.lemmatize(tok, get_wordnet_pos(tok)) for tok in tokens]\n",
    "\n",
    "    return stems\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path = './archive/train.csv'\n",
    "data_column  = 'tweet'\n",
    "SVM\t\t\t = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(dataset_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['processed_text'] = df[data_column].apply(preprocess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "import numpy as np\n",
    "\n",
    "# Train a Word2Vec model on your tokens\n",
    "word2vec_model = Word2Vec(sentences=df['processed_text'], vector_size=100, window=5, min_count=1, workers=4)\n",
    "\n",
    "# Generate averaged word vector features\n",
    "def document_vector(word2vec_model, doc):\n",
    "    # remove out-of-vocabulary words\n",
    "    doc = [word for word in doc if word in word2vec_model.wv]\n",
    "    return np.mean(word2vec_model.wv[doc], axis=0)\n",
    "\n",
    "X_word2vec = np.array([document_vector(word2vec_model, doc) for doc in df['processed_text']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['processed_text_str'] = df['processed_text'].apply(lambda x: \" \".join(x))\n",
    "\n",
    "# Then apply TF-IDF vectorization on the resulting strings\n",
    "tfidf_vectorizer = TfidfVectorizer(max_features=5000)\n",
    "X_tfidf = tfidf_vectorizer.fit_transform(df['processed_text_str']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_tweet(row):\n",
    "    \"\"\"\n",
    "    - Hate: If hate_speech_count is the highest count.\n",
    "    - Offensive: If offensive_language_count is the highest count.\n",
    "    - Hate & Offensive: If hate_speech_count and offensive_language_count are equal and higher than neither_count.\n",
    "    - Neither: If neither_count is the highest count.\n",
    "    \"\"\"\n",
    "    if row['hate_speech_count'] > row['offensive_language_count'] and row['hate_speech_count'] > row['neither_count']:\n",
    "        return 'hate'\n",
    "    elif row['offensive_language_count'] > row['hate_speech_count'] and row['offensive_language_count'] > row['neither_count']:\n",
    "        return 'offensive'\n",
    "    elif row['hate_speech_count'] == row['offensive_language_count'] and row['hate_speech_count'] > row['neither_count']:\n",
    "        return 'hate_offensive'\n",
    "    else:\n",
    "        return 'neither'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['label'] = df.apply(label_tweet, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "y = label_encoder.fit_transform(df['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'hate': 0, 'neither': 1, 'offensive': 2}\n"
     ]
    }
   ],
   "source": [
    "label_mapping = dict(zip(label_encoder.classes_, label_encoder.transform(label_encoder.classes_)))\n",
    "print(label_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_tfidf, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'max_depth': 15, 'n_estimators': 100}\n",
      "Best cross-validation score: 0.78\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Grid search for parameter optimization\n",
    "param_grid = {'n_estimators': [50, 100, 200, 300], 'max_depth': [5, 10, 15, 20]}\n",
    "grid_search = GridSearchCV(RandomForestClassifier(), param_grid, cv=5)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Best parameters: \", grid_search.best_params_)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))\n",
    "\n",
    "# Train model with best parameters\n",
    "best_rf_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average cross-validation score: 0.78\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cross_val_scores = cross_val_score(best_rf_model, X_tfidf, y, cv=5)\n",
    "print(\"Average cross-validation score: {:.2f}\".format(np.mean(cross_val_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "def plot_confusion_matrix(cm, class_names):\n",
    "\n",
    "    figure = plt.figure(figsize=(8, 8))\n",
    "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=plt.cm.Blues, cbar=False,\n",
    "                xticklabels=class_names, yticklabels=class_names)\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()\n",
    "    return figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# True labels, already known as y_test from your train_test_split operation\n",
    "true_labels = y_test\n",
    "\n",
    "# Predicted labels, which you get by making predictions on your test set\n",
    "predicted_labels = best_rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(true_labels, predicted_labels)\n",
    "plot_confusion_matrix(cm, label_mapping.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}