sentiment_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Sentiment Analysis for Social Media Data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports and Initializations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\infor\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\infor\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\infor\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import emoji\n",
    "import string\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "nltk.download('stopwords')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('wordnet')\n",
    "\n",
    "from nltk.corpus import stopwords, wordnet\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from gensim.models import Word2Vec\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "import torch\n",
    "from transformers import BertTokenizer, BertModel\n",
    "\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_folder = 'pickles'\n",
    "sentiment_file = os.path.join( 'content', 'reddit_comments_with_sentiment.csv')\n",
    "url_pattern = re.compile('http\\S+|www\\S+|https\\S+')\n",
    "random_state = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(random_state)\n",
    "en_stopwords = stopwords.words('english')\n",
    "punctuation_list = list(string.punctuation)\n",
    "lemmatizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Comment</th>\n",
       "      <th>upRatio</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donald Trump sells cut-up pieces of suit he wo...</td>\n",
       "      <td>\\nAs a reminder, this subreddit [is for civil ...</td>\n",
       "      <td>0.94</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Donald Trump sells cut-up pieces of suit he wo...</td>\n",
       "      <td>Probably some rag he bought off the rack, not ...</td>\n",
       "      <td>0.94</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Donald Trump sells cut-up pieces of suit he wo...</td>\n",
       "      <td>Just so it's clear, there will certainly be fa...</td>\n",
       "      <td>0.94</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Donald Trump sells cut-up pieces of suit he wo...</td>\n",
       "      <td>That guy was actually in the role of the presi...</td>\n",
       "      <td>0.94</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Donald Trump sells cut-up pieces of suit he wo...</td>\n",
       "      <td>It's literally a cult\\n\\nWhat sickos</td>\n",
       "      <td>0.94</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26694</th>\n",
       "      <td>The TikTok Panic Reached a New Level of Stupid...</td>\n",
       "      <td>Keep my daughter’s name out of your voice!!!</td>\n",
       "      <td>0.79</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26695</th>\n",
       "      <td>The TikTok Panic Reached a New Level of Stupid...</td>\n",
       "      <td>From reporter Jules Roscoe:\\n\\nIn the fourth R...</td>\n",
       "      <td>0.79</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26696</th>\n",
       "      <td>The TikTok Panic Reached a New Level of Stupid...</td>\n",
       "      <td>TikTok is right up there with X for platforms ...</td>\n",
       "      <td>0.79</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26697</th>\n",
       "      <td>The TikTok Panic Reached a New Level of Stupid...</td>\n",
       "      <td>I feel 17% stupider for having read this.</td>\n",
       "      <td>0.79</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26698</th>\n",
       "      <td>The TikTok Panic Reached a New Level of Stupid...</td>\n",
       "      <td>Possibly true. I’ve spent no time on tik tok a...</td>\n",
       "      <td>0.79</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26699 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Title  \\\n",
       "0      Donald Trump sells cut-up pieces of suit he wo...   \n",
       "1      Donald Trump sells cut-up pieces of suit he wo...   \n",
       "2      Donald Trump sells cut-up pieces of suit he wo...   \n",
       "3      Donald Trump sells cut-up pieces of suit he wo...   \n",
       "4      Donald Trump sells cut-up pieces of suit he wo...   \n",
       "...                                                  ...   \n",
       "26694  The TikTok Panic Reached a New Level of Stupid...   \n",
       "26695  The TikTok Panic Reached a New Level of Stupid...   \n",
       "26696  The TikTok Panic Reached a New Level of Stupid...   \n",
       "26697  The TikTok Panic Reached a New Level of Stupid...   \n",
       "26698  The TikTok Panic Reached a New Level of Stupid...   \n",
       "\n",
       "                                                 Comment  upRatio Sentiment  \n",
       "0      \\nAs a reminder, this subreddit [is for civil ...     0.94  negative  \n",
       "1      Probably some rag he bought off the rack, not ...     0.94  negative  \n",
       "2      Just so it's clear, there will certainly be fa...     0.94  positive  \n",
       "3      That guy was actually in the role of the presi...     0.94  negative  \n",
       "4                   It's literally a cult\\n\\nWhat sickos     0.94  negative  \n",
       "...                                                  ...      ...       ...  \n",
       "26694       Keep my daughter’s name out of your voice!!!     0.79  positive  \n",
       "26695  From reporter Jules Roscoe:\\n\\nIn the fourth R...     0.79  negative  \n",
       "26696  TikTok is right up there with X for platforms ...     0.79  positive  \n",
       "26697          I feel 17% stupider for having read this.     0.79  negative  \n",
       "26698  Possibly true. I’ve spent no time on tik tok a...     0.79  negative  \n",
       "\n",
       "[26699 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(sentiment_file)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Comment\n",
       "[deleted]    152\n",
       "[removed]     83\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comments_to_exclude = ['[deleted]', '[removed]']\n",
    "df[df['Comment'].str.strip().isin(comments_to_exclude)]['Comment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26464, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sentiment\n",
       "negative    19045\n",
       "positive     5858\n",
       "neutral      1561\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[~df['Comment'].str.strip().isin(comments_to_exclude)]\n",
    "print(df.shape)\n",
    "df['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balancing the Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_negative_indices = np.random.choice(df[df['Sentiment'] == 'negative'].index, 13187, replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13277, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sentiment\n",
       "positive    5858\n",
       "negative    5858\n",
       "neutral     1561\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(index=random_negative_indices)\n",
    "print(df.shape)\n",
    "df['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(input_str: str)->str:\n",
    "    \"\"\"\n",
    "    Returns the input string after applying basic NLP tasks \n",
    "    (Stopword removal, Punctuation removal and lemmatization using POS tagging)\n",
    "    \"\"\"\n",
    "    def get_wordnet_pos(tag):\n",
    "        \"\"\"\n",
    "        Returns wordnet POS tag object.\n",
    "        \"\"\"\n",
    "        if tag.startswith('J'):\n",
    "            return wordnet.ADJ\n",
    "        elif tag.startswith('V'):\n",
    "            return wordnet.VERB\n",
    "        elif tag.startswith('N'):\n",
    "            return wordnet.NOUN\n",
    "        elif tag.startswith('R'):\n",
    "            return wordnet.ADV\n",
    "        else:\n",
    "            return wordnet.NOUN\n",
    "    \n",
    "    # Removing links\n",
    "    input_str = url_pattern.sub('', input_str)\n",
    "\n",
    "    # Handling emojis\n",
    "    input_str = emoji.demojize(input_str)\n",
    "\n",
    "    # Tokenization \n",
    "    tokens = [\n",
    "            word for word in word_tokenize(input_str) \n",
    "            if word.lower() not in en_stopwords       # stopword removal\n",
    "                and word not in punctuation_list      # punctuation removal\n",
    "                and not re.match(r'\\W+$', word)       # removing tokens with only special chars\n",
    "    ]\n",
    "    \n",
    "    pos_tagged = nltk.pos_tag(tokens) # POS tagging\n",
    "    lemmatized_with_pos = [\n",
    "        lemmatizer.lemmatize(word, get_wordnet_pos(pos)) for word, pos in pos_tagged\n",
    "    ]  # Lemmatization\n",
    "    cleansed_sentence = ' '.join(lemmatized_with_pos).lower() # Case folding\n",
    "\n",
    "    return cleansed_sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for comment in df['Comment'].loc[[130, 2099, 2, 2058]]:\n",
    "#     print(comment)\n",
    "#     print('-' * 75)\n",
    "#     print(preprocess_text(comment))\n",
    "#     print('='*200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Processed_Comment'] = df['Comment'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df['Processed_Comment'], df['Sentiment'], test_size=0.25, random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_data = {\n",
    "    'X_train': X_train, 'X_test': X_test, 'y_train': y_train, 'y_test':y_test\n",
    "}\n",
    "with open(os.path.join(pickle_folder, 'processed_data.pickle'), 'wb') as fo:\n",
    "    pickle.dump(processed_data, fo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(os.path.join(pickle_folder, 'processed_data.pickle'), 'rb') as fo:\n",
    "#     processed_data = pickle.load(fo)\n",
    "# X_train, X_test, y_train, y_test = processed_data['X_train'], processed_data['X_test'], processed_data['y_train'], processed_data['y_test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extractions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF-IDF Features: 1000\n"
     ]
    }
   ],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(max_features=1000, ngram_range=(1, 2))\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
    "tfidf_features = tfidf_vectorizer.get_feature_names_out()\n",
    "print(f'TF-IDF Features: {len(tfidf_features)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.059651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.071932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9952</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9953</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9954</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9955</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9956</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9957 rows × 1000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      000        10  100   20  2016  2020  2024   30   40   50  ...     wrong  \\\n",
       "0     0.0  0.385376  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  ...  0.000000   \n",
       "1     0.0  0.000000  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  ...  0.000000   \n",
       "2     0.0  0.000000  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  ...  0.000000   \n",
       "3     0.0  0.000000  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  ...  0.000000   \n",
       "4     0.0  0.000000  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  ...  0.076819   \n",
       "...   ...       ...  ...  ...   ...   ...   ...  ...  ...  ...  ...       ...   \n",
       "9952  0.0  0.000000  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  ...  0.000000   \n",
       "9953  0.0  0.000000  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  ...  0.000000   \n",
       "9954  0.0  0.000000  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  ...  0.000000   \n",
       "9955  0.0  0.000000  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  ...  0.000000   \n",
       "9956  0.0  0.000000  0.0  0.0   0.0   0.0   0.0  0.0  0.0  0.0  ...  0.000000   \n",
       "\n",
       "      wtf   ya      yeah      year  year ago       yes  yet  young  zero  \n",
       "0     0.0  0.0  0.170106  0.000000       0.0  0.000000  0.0    0.0   0.0  \n",
       "1     0.0  0.0  0.000000  0.000000       0.0  0.000000  0.0    0.0   0.0  \n",
       "2     0.0  0.0  0.000000  0.000000       0.0  0.000000  0.0    0.0   0.0  \n",
       "3     0.0  0.0  0.000000  0.000000       0.0  0.000000  0.0    0.0   0.0  \n",
       "4     0.0  0.0  0.000000  0.059651       0.0  0.071932  0.0    0.0   0.0  \n",
       "...   ...  ...       ...       ...       ...       ...  ...    ...   ...  \n",
       "9952  0.0  0.0  0.000000  0.000000       0.0  0.000000  0.0    0.0   0.0  \n",
       "9953  0.0  0.0  0.000000  0.000000       0.0  0.000000  0.0    0.0   0.0  \n",
       "9954  0.0  0.0  0.000000  0.000000       0.0  0.000000  0.0    0.0   0.0  \n",
       "9955  0.0  0.0  0.000000  0.000000       0.0  0.000000  0.0    0.0   0.0  \n",
       "9956  0.0  0.0  0.000000  0.000000       0.0  0.000000  0.0    0.0   0.0  \n",
       "\n",
       "[9957 rows x 1000 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_vectors_tfidf = pd.DataFrame.sparse.from_spmatrix(X_train_tfidf)\n",
    "df_train_vectors_tfidf.columns = tfidf_features\n",
    "df_train_vectors_tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(pickle_folder, 'tfidf_vectorizer.pickle'), 'wb') as fo:\n",
    "    pickle.dump(tfidf_vectorizer, fo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(os.path.join(pickle_folder, 'tfidf_vectorizer.pickle'), 'rb') as fo:\n",
    "    # tfidf_vectorizer = pickle.load(fo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_texts = [word_tokenize(text) for text in X_train]\n",
    "\n",
    "word2vec_model = Word2Vec(\n",
    "    sentences=tokenized_texts, vector_size=1000, window=5, min_count=1, workers=4\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(pickle_folder, 'word2vec_model.pickle'), 'wb') as fo:\n",
    "    pickle.dump(word2vec_model, fo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(os.path.join(pickle_folder, 'word2vec_model.pickle'), 'rb') as fo:\n",
    "#     word2vec_model = pickle.load(fo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_word2vec_features(text):\n",
    "    words = word_tokenize(text)\n",
    "    vectors = [word2vec_model.wv[word] for word in words if word in word2vec_model.wv]\n",
    "    if vectors:\n",
    "        return sum(vectors) / len(vectors)\n",
    "    else:\n",
    "        return [0] * 1000  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_word2vec = [get_word2vec_features(text) for text in X_train]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_word2vec = [get_word2vec_features(text) for text in X_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Context based Embeddings - BERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
    "model = BertModel.from_pretrained(\"bert-base-uncased\").to('cuda:0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(pickle_folder, 'bert_tokenizer.pickle'), 'wb') as fo:\n",
    "    pickle.dump(tokenizer, fo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(pickle_folder, 'bert_model.pickle'), 'wb') as fo:\n",
    "    pickle.dump(model, fo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bert_embeddings(texts, batch_size=16):\n",
    "    if not isinstance(texts, list):\n",
    "        texts = texts.to_list()\n",
    "    \n",
    "    def batchify(data, batch_size):\n",
    "        for i in range(0, len(data), batch_size):\n",
    "            yield data[i:i + batch_size]\n",
    "    \n",
    "    embeddings = []\n",
    "    with torch.no_grad():\n",
    "        for batch_texts in batchify(texts, batch_size):\n",
    "            # Tokenize and pad the input texts\n",
    "            tokenized_texts = tokenizer(batch_texts, padding=True, truncation=True, return_tensors=\"pt\", max_length=128).to('cuda:0')\n",
    "\n",
    "            # Forward pass to obtain BERT embeddings\n",
    "            outputs = model(**tokenized_texts)\n",
    "            batch_embeddings = outputs.last_hidden_state.mean(dim=1).cpu().numpy()\n",
    "\n",
    "            # Store the embeddings\n",
    "            embeddings.extend(batch_embeddings)\n",
    "\n",
    "    # Convert list of embeddings to a numpy array\n",
    "    embeddings = np.array(embeddings)\n",
    "\n",
    "    return embeddings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_bert = get_bert_embeddings(X_train.to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_bert = get_bert_embeddings(X_test.to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_metrics(y_actual, y_predicted, title=''):\n",
    "    \"\"\" Function to calculate and display the metrics for a classification problem \"\"\"\n",
    "    \n",
    "    plt.figure(figsize=(4.5, 3))\n",
    "    _ = ConfusionMatrixDisplay.from_predictions(y_actual, y_predicted)\n",
    "    if title:\n",
    "        plt.title(title)\n",
    "    plt.show()\n",
    "    plt.savefig(os.path.join('plots', title))\n",
    "    print(classification_report(y_actual, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(X_train, y_train, model_name='SVM'):\n",
    "    models = {\n",
    "        'SVM': SVC(), \n",
    "        'Naive-Bayes': MultinomialNB(),\n",
    "        'MLP': MLPClassifier()\n",
    "    }\n",
    "\n",
    "    param_grid = {\n",
    "        'SVM': {\n",
    "            'C': [0.1, 1, 10],  \n",
    "            'gamma': [1, 0.1, 0.01], \n",
    "            'kernel': ['rbf']\n",
    "        },\n",
    "        'Naive-Bayes': {\n",
    "            'alpha': [0.1, 0.5, 1.0],\n",
    "            'fit_prior': [True, False]\n",
    "        },\n",
    "        'MLP': {\n",
    "            'hidden_layer_sizes': [(64, 32), (32, 16), (16, 8)],\n",
    "            'learning_rate_init': [0.01, 0.1]\n",
    "        }\n",
    "    }  \n",
    "  \n",
    "    scoring_metric = 'accuracy' #'f1' if model_name == 'SVM' else 'accuracy'\n",
    "    grid = GridSearchCV(models[model_name], param_grid[model_name], scoring=scoring_metric, cv=3, verbose=3) \n",
    "    grid.fit(X_train, y_train) \n",
    "\n",
    "    return grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Building for SVM using TF-IDF feature extraction\n",
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n",
      "[CV 1/3] END ........C=0.1, gamma=1, kernel=rbf;, score=0.565 total time=   4.5s\n",
      "[CV 2/3] END ........C=0.1, gamma=1, kernel=rbf;, score=0.583 total time=   4.5s\n",
      "[CV 3/3] END ........C=0.1, gamma=1, kernel=rbf;, score=0.584 total time=   4.4s\n",
      "[CV 1/3] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.565 total time=   4.2s\n",
      "[CV 2/3] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.545 total time=   4.4s\n",
      "[CV 3/3] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.600 total time=   4.2s\n",
      "[CV 1/3] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.441 total time=   4.2s\n",
      "[CV 2/3] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.441 total time=   4.2s\n",
      "[CV 3/3] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.441 total time=   4.1s\n",
      "[CV 1/3] END ..........C=1, gamma=1, kernel=rbf;, score=0.668 total time=   4.3s\n",
      "[CV 2/3] END ..........C=1, gamma=1, kernel=rbf;, score=0.671 total time=   4.0s\n",
      "[CV 3/3] END ..........C=1, gamma=1, kernel=rbf;, score=0.666 total time=   4.4s\n",
      "[CV 1/3] END ........C=1, gamma=0.1, kernel=rbf;, score=0.651 total time=   4.6s\n",
      "[CV 2/3] END ........C=1, gamma=0.1, kernel=rbf;, score=0.658 total time=   4.1s\n",
      "[CV 3/3] END ........C=1, gamma=0.1, kernel=rbf;, score=0.644 total time=   4.1s\n",
      "[CV 1/3] END .......C=1, gamma=0.01, kernel=rbf;, score=0.596 total time=   4.6s\n",
      "[CV 2/3] END .......C=1, gamma=0.01, kernel=rbf;, score=0.579 total time=   4.8s\n",
      "[CV 3/3] END .......C=1, gamma=0.01, kernel=rbf;, score=0.618 total time=   5.0s\n",
      "[CV 1/3] END .........C=10, gamma=1, kernel=rbf;, score=0.668 total time=   5.5s\n",
      "[CV 2/3] END .........C=10, gamma=1, kernel=rbf;, score=0.669 total time=   5.9s\n",
      "[CV 3/3] END .........C=10, gamma=1, kernel=rbf;, score=0.651 total time=   5.4s\n",
      "[CV 1/3] END .......C=10, gamma=0.1, kernel=rbf;, score=0.671 total time=   3.9s\n",
      "[CV 2/3] END .......C=10, gamma=0.1, kernel=rbf;, score=0.671 total time=   4.1s\n",
      "[CV 3/3] END .......C=10, gamma=0.1, kernel=rbf;, score=0.656 total time=   4.0s\n",
      "[CV 1/3] END ......C=10, gamma=0.01, kernel=rbf;, score=0.654 total time=   4.4s\n",
      "[CV 2/3] END ......C=10, gamma=0.01, kernel=rbf;, score=0.649 total time=   4.4s\n",
      "[CV 3/3] END ......C=10, gamma=0.01, kernel=rbf;, score=0.641 total time=   4.3s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 450x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.68      0.73      0.70      1465\n",
      "     neutral       0.66      0.12      0.20       386\n",
      "    positive       0.67      0.77      0.72      1469\n",
      "\n",
      "    accuracy                           0.68      3320\n",
      "   macro avg       0.67      0.54      0.54      3320\n",
      "weighted avg       0.67      0.68      0.65      3320\n",
      "\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "Model Building for SVM using Word2Vec feature extraction\n",
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n",
      "[CV 1/3] END ........C=0.1, gamma=1, kernel=rbf;, score=0.531 total time= 1.1min\n",
      "[CV 2/3] END ........C=0.1, gamma=1, kernel=rbf;, score=0.528 total time= 1.0min\n",
      "[CV 3/3] END ........C=0.1, gamma=1, kernel=rbf;, score=0.512 total time= 1.1min\n",
      "[CV 1/3] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.507 total time= 1.1min\n",
      "[CV 2/3] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.517 total time= 1.0min\n",
      "[CV 3/3] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.511 total time= 1.4min\n",
      "[CV 1/3] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.486 total time= 1.0min\n",
      "[CV 2/3] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.497 total time= 1.0min\n",
      "[CV 3/3] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.485 total time=  57.7s\n",
      "[CV 1/3] END ..........C=1, gamma=1, kernel=rbf;, score=0.539 total time=  54.9s\n",
      "[CV 2/3] END ..........C=1, gamma=1, kernel=rbf;, score=0.534 total time=  53.4s\n",
      "[CV 3/3] END ..........C=1, gamma=1, kernel=rbf;, score=0.526 total time=  52.3s\n",
      "[CV 1/3] END ........C=1, gamma=0.1, kernel=rbf;, score=0.518 total time= 1.2min\n",
      "[CV 2/3] END ........C=1, gamma=0.1, kernel=rbf;, score=0.521 total time= 1.2min\n",
      "[CV 3/3] END ........C=1, gamma=0.1, kernel=rbf;, score=0.518 total time= 1.3min\n",
      "[CV 1/3] END .......C=1, gamma=0.01, kernel=rbf;, score=0.491 total time=  59.2s\n",
      "[CV 2/3] END .......C=1, gamma=0.01, kernel=rbf;, score=0.505 total time= 1.1min\n",
      "[CV 3/3] END .......C=1, gamma=0.01, kernel=rbf;, score=0.499 total time= 1.1min\n",
      "[CV 1/3] END .........C=10, gamma=1, kernel=rbf;, score=0.545 total time= 1.1min\n",
      "[CV 2/3] END .........C=10, gamma=1, kernel=rbf;, score=0.543 total time= 1.1min\n",
      "[CV 3/3] END .........C=10, gamma=1, kernel=rbf;, score=0.527 total time=  59.5s\n",
      "[CV 1/3] END .......C=10, gamma=0.1, kernel=rbf;, score=0.533 total time=  59.8s\n",
      "[CV 2/3] END .......C=10, gamma=0.1, kernel=rbf;, score=0.536 total time= 1.1min\n",
      "[CV 3/3] END .......C=10, gamma=0.1, kernel=rbf;, score=0.519 total time= 1.0min\n",
      "[CV 1/3] END ......C=10, gamma=0.01, kernel=rbf;, score=0.501 total time= 1.1min\n",
      "[CV 2/3] END ......C=10, gamma=0.01, kernel=rbf;, score=0.512 total time= 1.1min\n",
      "[CV 3/3] END ......C=10, gamma=0.01, kernel=rbf;, score=0.504 total time= 1.1min\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 450x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.54      0.71      0.61      1465\n",
      "     neutral       0.61      0.09      0.16       386\n",
      "    positive       0.57      0.53      0.55      1469\n",
      "\n",
      "    accuracy                           0.55      3320\n",
      "   macro avg       0.57      0.44      0.44      3320\n",
      "weighted avg       0.56      0.55      0.53      3320\n",
      "\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "Model Building for SVM using BERT feature extraction\n",
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n",
      "[CV 1/3] END ........C=0.1, gamma=1, kernel=rbf;, score=0.459 total time=  58.2s\n",
      "[CV 2/3] END ........C=0.1, gamma=1, kernel=rbf;, score=0.455 total time=  54.5s\n",
      "[CV 3/3] END ........C=0.1, gamma=1, kernel=rbf;, score=0.457 total time=  54.3s\n",
      "[CV 1/3] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.603 total time= 1.9min\n",
      "[CV 2/3] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.606 total time= 1.2min\n",
      "[CV 3/3] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.590 total time=  43.1s\n",
      "[CV 1/3] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.661 total time= 1.4min\n",
      "[CV 2/3] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.658 total time= 1.3min\n",
      "[CV 3/3] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.633 total time= 1.3min\n",
      "[CV 1/3] END ..........C=1, gamma=1, kernel=rbf;, score=0.475 total time= 1.7min\n",
      "[CV 2/3] END ..........C=1, gamma=1, kernel=rbf;, score=0.460 total time= 2.1min\n",
      "[CV 3/3] END ..........C=1, gamma=1, kernel=rbf;, score=0.460 total time= 1.6min\n",
      "[CV 1/3] END ........C=1, gamma=0.1, kernel=rbf;, score=0.682 total time= 1.2min\n",
      "[CV 2/3] END ........C=1, gamma=0.1, kernel=rbf;, score=0.675 total time= 1.5min\n",
      "[CV 3/3] END ........C=1, gamma=0.1, kernel=rbf;, score=0.667 total time= 1.2min\n",
      "[CV 1/3] END .......C=1, gamma=0.01, kernel=rbf;, score=0.682 total time=  36.7s\n",
      "[CV 2/3] END .......C=1, gamma=0.01, kernel=rbf;, score=0.676 total time=  35.9s\n",
      "[CV 3/3] END .......C=1, gamma=0.01, kernel=rbf;, score=0.666 total time=  33.6s\n",
      "[CV 1/3] END .........C=10, gamma=1, kernel=rbf;, score=0.481 total time= 1.1min\n",
      "[CV 2/3] END .........C=10, gamma=1, kernel=rbf;, score=0.462 total time= 2.2min\n",
      "[CV 3/3] END .........C=10, gamma=1, kernel=rbf;, score=0.461 total time= 2.1min\n",
      "[CV 1/3] END .......C=10, gamma=0.1, kernel=rbf;, score=0.684 total time= 1.8min\n",
      "[CV 2/3] END .......C=10, gamma=0.1, kernel=rbf;, score=0.676 total time= 1.4min\n",
      "[CV 3/3] END .......C=10, gamma=0.1, kernel=rbf;, score=0.666 total time= 1.9min\n",
      "[CV 1/3] END ......C=10, gamma=0.01, kernel=rbf;, score=0.687 total time=  49.5s\n",
      "[CV 2/3] END ......C=10, gamma=0.01, kernel=rbf;, score=0.683 total time=  35.0s\n",
      "[CV 3/3] END ......C=10, gamma=0.01, kernel=rbf;, score=0.666 total time=  48.5s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 450x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.68      0.74      0.71      1465\n",
      "     neutral       0.50      0.23      0.31       386\n",
      "    positive       0.68      0.73      0.70      1469\n",
      "\n",
      "    accuracy                           0.67      3320\n",
      "   macro avg       0.62      0.56      0.57      3320\n",
      "weighted avg       0.66      0.67      0.66      3320\n",
      "\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "========================================================================================================================================================================================================\n",
      "Model Building for Naive-Bayes using TF-IDF feature extraction\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "Exception occured while building naivebayes_tfidf\n",
      "Invalid parameter 'average' for estimator MultinomialNB(alpha=0.1). Valid parameters are: ['alpha', 'class_prior', 'fit_prior', 'force_alpha'].\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "Model Building for Naive-Bayes using Word2Vec feature extraction\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "Exception occured while building naivebayes_word2vec\n",
      "Invalid parameter 'average' for estimator MultinomialNB(alpha=0.1). Valid parameters are: ['alpha', 'class_prior', 'fit_prior', 'force_alpha'].\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "Model Building for Naive-Bayes using BERT feature extraction\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "Exception occured while building naivebayes_bert\n",
      "Invalid parameter 'average' for estimator MultinomialNB(alpha=0.1). Valid parameters are: ['alpha', 'class_prior', 'fit_prior', 'force_alpha'].\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "========================================================================================================================================================================================================\n",
      "Model Building for MLP using TF-IDF feature extraction\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "[CV 1/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.01;, score=0.612 total time=   3.3s\n",
      "[CV 2/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.01;, score=0.636 total time=   7.7s\n",
      "[CV 3/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.01;, score=0.609 total time=   6.7s\n",
      "[CV 1/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.1;, score=0.599 total time=   3.0s\n",
      "[CV 2/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.1;, score=0.624 total time=   6.3s\n",
      "[CV 3/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.1;, score=0.599 total time=   5.1s\n",
      "[CV 1/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.01;, score=0.631 total time=   0.8s\n",
      "[CV 2/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.01;, score=0.626 total time=   1.2s\n",
      "[CV 3/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.01;, score=0.613 total time=   1.7s\n",
      "[CV 1/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.1;, score=0.616 total time=   1.0s\n",
      "[CV 2/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.1;, score=0.618 total time=   1.1s\n",
      "[CV 3/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.1;, score=0.604 total time=   0.8s\n",
      "[CV 1/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.01;, score=0.627 total time=   0.7s\n",
      "[CV 2/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.01;, score=0.622 total time=   0.7s\n",
      "[CV 3/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.01;, score=0.616 total time=   0.8s\n",
      "[CV 1/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.1;, score=0.610 total time=   0.8s\n",
      "[CV 2/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.1;, score=0.613 total time=   0.8s\n",
      "[CV 3/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.1;, score=0.626 total time=   0.6s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 450x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nSkP+OFKBg3v8eWtSI2676xTVws6VdvhyFbzdc3np3tVM/KYDZ7I8rfZ9viucnUdDOZ7uz68ng3h7fRuqBWQSGlDwh0pkk984eKoy725qxdHTAew4Gsq07yJ4uMUvVPDIKYuX47AuTOJnz+KsyvSVdejQgaFDhzJ69GgqVapESEgIEyZMsOxPS0ujf//+BAUF4e/vT8eOHdm9e7fVOSZPnkzVqlXx8/Ojf//+jBkzhubNm1v2b9u2jS5dulClShUCAgJo3749O3futOyvXbs2APfddx8mk8myPmHCBMt5Vq1ahZeXF2lpaVbXHjZsGB07drSsb9y4kdtvvx1vb2/CwsIYOnQoZ8+etft9ckSBzfP5K8GNs0cKfsQyfnUhbacrVW7PK7R99p8mTm1wo/r9tn+5paxzIyfNRPVeuaUSs5QOk8kg9qW9fD43jKRDl3YzunuYyct1wfjbfB3ZWQU/Nze0SL9mcUrRPRe5ge9/q0XCkRqXbeflnkvPm37l2Gk/kjN8AXB3NZOd52rVLjvPDS/3fMJDTpVazM7IbJjsXpxVmadt8+fPx8fHh4SEBKZOncrEiROJj48H4MEHHyQlJYVvvvmGHTt20KJFCzp16kRqaioACxcuZMqUKbzyyivs2LGDmjVrMmvWLKvznzlzhujoaDZu3MgPP/xAgwYN6NatG2fOFPwVsW3bNgDmzp3LiRMnLOt/16lTJwIDA/n8888t2/Lz81m8eDFRUVEAHDp0iK5du9K7d29++uknFi9ezMaNG4mJiSn5N80B1OmfQ7W7c9nYw4dVzf3Y8qAPtR7LIfSewpOb41+541oBgjsXvh/gjy/cqdIuD68Q550y3Bk9+O8k8vNNfLmw8C/C3QkVqVg5h959k3BzM+Prn8sTww8DUKmK/pIvbyKbHKRx8J/MWNfGZpuHWvzC5v+8xw+x79OuXhKDPulBnrkgodmSGEaz6ifpGn4QF5OZqr6ZDGy3HYAqvqrUScko8zE3N910E+PHjwegQYMGvPXWW6xZswZvb2+2bt1KSkoKnp4FZc/XXnuNpUuX8tlnnzFw4EBmzpxJv379eOKJJwAYN24cq1atIjMz03L+v1dWAN59910CAwNZv34999xzD0FBBaX0wMBAQkJCCo3R1dWVPn36sGjRIvr16wfAmjVrSEtLo3fv3gDExcURFRXF8OHDLa9lxowZtG/fnlmzZuHl5XXJebOzs8nOzrasZ2RkFPv9K6+SV7pxYpk7N71yHt/6Zs786sqvr3jiWdWges9LKy9/LHEn9J5cXD0LORmQlWziz01uNHv9fClHLiWpfvgZ7n30GEMfagU27sxIOuTDGy80of+o3+g77DBmM3y5sAapf3pgVh5brgT7ZTK6yyYGfdyDnHzbXx8r9jTgh8QaVPE9x+NtdjG11yr6fngfOflubEkM4821ETwfuYHJPdaQm+fKu5ta0rLmCaeuJJQGs51dS848iV+5SG7+rlq1aqSkpLB7924yMzMvGfdy/vx5Dh06BMD+/ft5+umnrfbfcsstrF271rJ+8uRJXnjhBdatW0dKSgr5+fmcO3eOpKSkYsUZFRXFrbfeyvHjxwkNDWXhwoV0796dwMBAAHbv3s1PP/3EwoULLccYhoHZbCYxMZEmTZpccs64uDhefPHFYsXhKA687kWd/tlU61ZQifFraOb8CROJ73tcktyc3uHK2URXbnrVduLyx1J3PAINqnawXdmR8ueGFmkEVsph/qotlm2ubgb9Y3+j16PHeKJrBADrVgSzbkUwgZVzyDrngoGJ+x4/SvIx77IKXQoRHnKKyj7n+fjf/7Nsc3MxaFHzOA+3/IVbpg7EbLiQme1JZrYnSacD+emPYL4f8V86Nkpk5d4GAHy0rRkfbbuJIN9zZGR5EhpwhmF3JvBHWuF3UErh7H8quJKbUuPu7m61bjKZMJvNZGZmUq1aNdatW3fJMRcSiqKIjo7mr7/+Yvr06dSqVQtPT08iIiLIySleubt169bUq1ePTz75hKeeeoolS5Ywb948y/7MzEyefPJJhg4desmxNWvWLPSczz77LCNHjrSsZ2RkEBYWVmhbR5OfxSV/qJtcwCjkJqdjX7jjH56Pf+PC74AyDPhjqQehPXJxcS+0iZRTa78OYdcPFa22TZq9m7XLQohfWu2S9ml/FdxJ1aXXCXKzXfhxS8VL2kjZSfi9Or3fe8hq28R7viPxr4rM3dK80C9LkwkwgYdr/j/3cCqzYAxW1/CDnEj3ZV9ylVKKXK43ZZ7c2NKiRQuSk5Nxc3OzDPL9p0aNGrFt2zYef/xxy7Z/jpnZtGkT77zzDt26dQPg6NGj/Pnnn1Zt3N3dyc//5/94l4qKimLhwoXUqFEDFxcXunfvbhXv3r17qV+/flFfIp6enpYuN2cT1CGPw+954l3NwLd+Phn7XDmywIPq91lXbfIy4eQqdxrFZtk8V2qCK+ePuVC9twYSl0de3nmE1rxYdQuunkXdRmc4k+7OqWQvzqRbZ6T5eS6c/tODP45UsGy751/H2LcrgKxzrtwckcq/Rx5i3rR6nD2jbLY8OZfjwaE//1FNz3En/bwnh/6sTPXADCKb/MaWxDBOn/Mi2O8sT0TsJDvPle8PXfwjL7rNj2w6XBPDMNGx0WH+HfEjo5fc5dSVhNKQj4l8Oybis+fY8q7cJjedO3cmIiKCXr16MXXqVBo2bMjx48dZvnw59913H61atWLIkCEMGDCAVq1a0bZtWxYvXsxPP/1E3bp1Ledp0KABH374Ia1atSIjI4NRo0bh7W1d6q5duzZr1qyhXbt2eHp6UrFi4X8tRkVFMWHCBKZMmcIDDzxglZg888wz3HrrrcTExNC/f398fHzYu3cv8fHxvPXWW6XzJpVjTZ7L4uBMT/ZO9iIntWASv7AHc6n3VLZVuxPfuGMYENLNduJy7AsPApvn4VtXc9uURw1uOMMrc3dZ1geO/g2A+C9DePOFS7tjC9PoxgwefToR7wr5HE2swFsTG7F2WeFj4KT8yslzpUXYCaJa/4S/VzZ/nfVm59FQohfcx+lzF5PZdnWT6N92J+6u+RxIqczwz7qy6XCtMozcMalbyrZym9yYTCZWrFjB888/zxNPPMGpU6cICQnhjjvuIDg4GChINg4fPkxsbCxZWVk89NBD9O3bl61bt1rO88EHHzBw4EBatGhBWFgYL730ErGxsVbXev311xk5ciTvvfce1atX58iRI4XGVL9+fW655Ra2bt3KtGnTrPbddNNNrF+/nueff57bb78dwzCoV68eDz/8cIm+L47CzQeajMmmyZjsy7YLezCXsAcvX5FpNlWDiMuzn7dXpFvTO4vc/sI4m797/fnwkgxJrqH+i3pa/n0q04eYT7tfpnWBgR/3vGIbEXuYDMNwqvsRunTpQkhICB9++GFZh1JsGRkZBAQEMCmhI16+5TbvlBKyun3tsg5BrqHjjzQu6xDkGsjPzmLvnOdIT0/H3790Bkhf+K4Yl9AZL9+r77rNysxlYpvVpRprWXHob9Bz584xe/ZsIiMjcXV15eOPP2b16tWWeXJERESclbqlbHPo5OZC19WUKVPIysqiUaNGfP7553Tu3LmsQxMRESlVenCmbQ6d3Hh7e7N69eqyDkNERETKEYdObkRERK5XBibMdtzObehWcBERESlP1C1lm/O+MhEREbkuqXIjIiLigMyGya6HjTrzg0qV3IiIiDigfDufCm7PseWd874yERERuS6pciMiIuKA1C1lm5IbERERB2TGBbMdHTD2HFveOe8rExERkeuSKjciIiIOKN8wkW9H15I9x5Z3Sm5EREQckMbc2KbkRkRExAEZdj4V3NAMxSIiIiKOQZUbERERB5SPiXw7Hn5pz7HlnZIbERERB2Q27Bs3YzZKMJhyRt1SIiIi4lRUuREREXFAZjsHFNtzbHmn5EZERMQBmTFhtmPcjD3HlnfOm7aJiIjIdUnJjYiIiAO6MEOxPUuxrpefz9ixY6lTpw7e3t7Uq1ePSZMmYRgXRyYbhsG4ceOoVq0a3t7edO7cmYMHD1qdJzU1laioKPz9/QkMDKRfv35kZmaWyHtygZIbERERB3RhzI09S3G88sorzJo1i7feeot9+/bxyiuvMHXqVGbOnGlpM3XqVGbMmMHs2bNJSEjAx8eHyMhIsrKyLG2ioqLYs2cP8fHxLFu2jA0bNjBw4MASe19AY25ERESkCDZv3kzPnj3p3r07ALVr1+bjjz9m69atQEHVZtq0abzwwgv07NkTgAULFhAcHMzSpUvp06cP+/btY+XKlWzbto1WrVoBMHPmTLp168Zrr71GaGhoicSqyo2IiIgDMmOyPF/qqpZiDihu27Yta9as4cCBAwDs3r2bjRs3cvfddwOQmJhIcnIynTt3thwTEBBAmzZt2LJlCwBbtmwhMDDQktgAdO7cGRcXFxISEux9SyxUuREREXFAhp13Sxn/f2xGRobVdk9PTzw9PS9pP2bMGDIyMmjcuDGurq7k5+czZcoUoqKiAEhOTgYgODjY6rjg4GDLvuTkZKpWrWq1383NjUqVKlnalARVbkRERByQXVWbvz1RPCwsjICAAMsSFxdX6PU+/fRTFi5cyKJFi9i5cyfz58/ntddeY/78+dfyZReJKjciIiLXsaNHj+Lv729ZL6xqAzBq1CjGjBlDnz59AGjatCm///47cXFxREdHExISAsDJkyepVq2a5biTJ0/SvHlzAEJCQkhJSbE6b15eHqmpqZbjS4IqNyIiIg6opO6W8vf3t1psJTfnzp3DxcU6bXB1dcVsNgNQp04dQkJCWLNmjWV/RkYGCQkJREREABAREUFaWho7duywtFm7di1ms5k2bdqU2Hujyo2IiIgD+nvX0tUeXxw9evRgypQp1KxZkxtuuIEff/yRN954g3//+98AmEwmhg8fzuTJk2nQoAF16tRh7NixhIaG0qtXLwCaNGlC165dGTBgALNnzyY3N5eYmBj69OlTYndKgZIbERERKYKZM2cyduxYnn76aVJSUggNDeXJJ59k3LhxljajR4/m7NmzDBw4kLS0NG677TZWrlyJl5eXpc3ChQuJiYmhU6dOuLi40Lt3b2bMmFGisZqMv08tKGUqIyODgIAAJiV0xMtXeaezW92+dlmHINfQ8Ucal3UIcg3kZ2exd85zpKenW41jKUkXvit6rOqHu4/HVZ8n92wOX9/1QanGWlb0DSoiIuKArnW3lCPRgGIRERFxKqrciIiIOCBVbmxTciMiIuKAlNzYpm4pERERcSqq3IiIiDggVW5sU3IjIiLigAyw88GZzkvJjYiIiANS5cY2jbkRERERp6LKjYiIiANS5cY2JTciIiIOSMmNbeqWEhEREaeiyo2IiIgDUuXGNiU3IiIiDsgwTBh2JCj2HFveqVtKREREnIoqNyIiIg7IjMmuSfzsOba8U3IjIiLigDTmxjZ1S4mIiIhTUeVGRETEAWlAsW1KbkRERByQuqVsU3IjIiLigFS5sU1jbkRERMSpqHJTDq29rTJuJveyDkNKmZGbWtYhyDVU/YsjZR2CXAN55mz2XqNrGXZ2Szlz5UbJjYiIiAMyAMOw73hnpW4pERERcSqq3IiIiDggMyZMmqG4UEpuREREHJDulrJN3VIiIiLiVFS5ERERcUBmw4RJk/gVSsmNiIiIAzIMO++WcuLbpdQtJSIiIk5FlRsREREHpAHFtim5ERERcUBKbmxTciMiIuKANKDYNo25EREREaeiyo2IiIgD0t1Stim5ERERcUAFyY09Y25KMJhyRt1SIiIi4lRUuREREXFAulvKNiU3IiIiDsj4/8We452VuqVERETEqahyIyIi4oDULWWbkhsRERFHpH4pm5TciIiIOCI7Kzc4ceVGY25ERETEqahyIyIi4oA0Q7FtSm5EREQckAYU26ZuKREREXEqqtyIiIg4IsNk36BgJ67cKLkRERFxQBpzY5u6pURERMSpqHIjIiLiiDSJn01FSm6++uqrIp/w3nvvvepgREREpGh0t5RtRUpuevXqVaSTmUwm8vPz7YlHRERExC5FSm7MZnNpxyEiIiLF5cRdS/awa8xNVlYWXl5eJRWLiIiIFJG6pWwr9t1S+fn5TJo0ierVq+Pr68vhw4cBGDt2LB988EGJBygiIiKFMEpgcVLFTm6mTJnCvHnzmDp1Kh4eHpbtN954I++//36JBiciIiJSXMVObhYsWMC7775LVFQUrq6ulu3NmjXj119/LdHgRERExBZTCSzOqdhjbv744w/q169/yXaz2Uxubm6JBCUiIiJXoHlubCp25SY8PJzvv//+ku2fffYZN998c4kEJSIiInK1il25GTduHNHR0fzxxx+YzWa++OIL9u/fz4IFC1i2bFlpxCgiIiL/pMqNTcWu3PTs2ZOvv/6a1atX4+Pjw7hx49i3bx9ff/01Xbp0KY0YRURE5J8uPBXcnsVJXdU8N7fffjvx8fElHYuIiIiI3a56Er/t27ezb98+oGAcTsuWLUssKBEREbk8wyhY7DneWRU7uTl27Bj/+te/2LRpE4GBgQCkpaXRtm1bPvnkE2rUqFHSMYqIiMg/acyNTcUec9O/f39yc3PZt28fqamppKamsm/fPsxmM/379y+NGEVERESKrNiVm/Xr17N582YaNWpk2daoUSNmzpzJ7bffXqLBiYiIiA32Dgp24gHFxa7chIWFFTpZX35+PqGhoSUSlIiIiFyeybB/Ka4//viDRx99lMqVK+Pt7U3Tpk3Zvn27Zb9hGIwbN45q1arh7e1N586dOXjwoNU5UlNTiYqKwt/fn8DAQPr160dmZqa9b4eVYic3r776KkOGDLF6Mdu3b2fYsGG89tprJRqciIiI2HCNH5x5+vRp2rVrh7u7O9988w179+7l9ddfp2LFipY2U6dOZcaMGcyePZuEhAR8fHyIjIwkKyvL0iYqKoo9e/YQHx/PsmXL2LBhAwMHDrzad6FQJsO48njpihUrYjJdLF+dPXuWvLw83NwKerUu/NvHx4fU1NQSDfB6kpGRQUBAAHe6P4ibyb2sw5FSZuTmlHUIcg25VVdl+3qQZ85m9fE5pKen4+/vXyrXuPBdETZtIi7eXld9HvP5LI4OH1fkWMeMGcOmTZsKfUoBFFRtQkND+c9//kNsbCwA6enpBAcHM2/ePPr06cO+ffsIDw9n27ZttGrVCoCVK1fSrVs3jh07VmI9QEUaczNt2rQSuZiIiIiUkBIac5ORkWG12dPTE09Pz0uaf/XVV0RGRvLggw+yfv16qlevztNPP82AAQMASExMJDk5mc6dO1uOCQgIoE2bNmzZsoU+ffqwZcsWAgMDLYkNQOfOnXFxcSEhIYH77rvv6l/P3xQpuYmOji6Ri4mIiEgJKaFbwcPCwqw2jx8/ngkTJlzS/PDhw8yaNYuRI0fy3HPPsW3bNoYOHYqHhwfR0dEkJycDEBwcbHVccHCwZV9ycjJVq1a12u/m5kalSpUsbUrCVU/iB5CVlUVOjnVpvbTKcCIiIlLyjh49avXdXVjVBsBsNtOqVSteeuklAG6++WZ++eUXZs+eXe6KIMUeUHz27FliYmKoWrUqPj4+VKxY0WoRERGRa6CEBhT7+/tbLbaSm2rVqhEeHm61rUmTJiQlJQEQEhICwMmTJ63anDx50rIvJCSElJQUq/15eXmkpqZa2pSEYic3o0ePZu3atcyaNQtPT0/ef/99XnzxRUJDQ1mwYEGJBSYiIiKXcY3vlmrXrh379++32nbgwAFq1aoFQJ06dQgJCWHNmjWW/RkZGSQkJBAREQFAREQEaWlp7Nixw9Jm7dq1mM1m2rRpU7yALqPY3VJff/01CxYsoEOHDjzxxBPcfvvt1K9fn1q1arFw4UKioqJKLDgREREpH0aMGEHbtm156aWXeOihh9i6dSvvvvsu7777LgAmk4nhw4czefJkGjRoQJ06dRg7diyhoaH06tULKKj0dO3alQEDBjB79mxyc3OJiYmhT58+JTpXXrGTm9TUVOrWrQsUlLIu3Pp922238dRTT5VYYCIiInIZ13iG4tatW7NkyRKeffZZJk6cSJ06dZg2bZpVUWP06NGcPXuWgQMHkpaWxm233cbKlSvx8rp4y/rChQuJiYmhU6dOuLi40Lt3b2bMmHH1r6MQxU5u6tatS2JiIjVr1qRx48Z8+umn3HLLLXz99deWB2mK3HjLGR548gQNmp6jcnAuLw6oz5ZVF8dkteuaSreoUzRoehb/ivk8ffcNHN5b4ZLzNGmRSfSoYzRufpb8fDi8twLPP9aInOxi96hKKbmxTSYPPn2q4LMOyWPCv2uzZWXA31oYPD7qJF0f+Qtf/3z2bvdhxpgaHE+82K/vF5jH05P/oE2XDAwzbFwRyKyxoWSdc732L0gu64abU+n92GHqN86gclA2k2Jb8MP6i3fHLN/2TaHHfTC9EV98VPCHsa9/DoNG7aXNbSmYDROb14Yw5/UmZJ236x6X687VzjL89+OL65577uGee+6xfU6TiYkTJzJx4kSbbSpVqsSiRYuKf/FiKPY3xBNPPMHu3buBggl93n77bby8vBgxYgSjRo0q8QAdWe3ata/bOYK8KuSTuK8Cb4+tVfh+bzN7tvny35fDCt0PBYnN5PkH2LkhgGH3hjPs3nC+mh/MlaedlGvJq4KZw3u8eOu5GoXuf2jwKXr++xQzx9Rg2D0NyDrnwkuLDuPuaba0eeatJGo1yuLZPnUZF12Hpm0yGf7qsWv1EqQYvLzzSTzgz6yp4YXuf7RrR6vlzYlNMZth83cXB4uOmrSbWnUzeSHmFl4c0ZIbbk5lyHO/XKuXINeBYqfJI0aMsPy7c+fO/Prrr+zYsYP69etz0003lWhw11qHDh1o3rz5dZuQlKTt6wLZvi7Q5v41S6oAEFwj22abgWOT+HJeVT6dVc2y7dhh7xKLUUrG9u/82f6drSkgDHr1P8XH04PZ8m1BNWfq0Jos3r2Htl3TWf9lRcLqZ9G64xliujbg4E8F1bt3XqjOpI8SeXdiKKknNVt3ebJjcxA7NgfZ3H/6L+s7bW694yQ/7ahM8h8Fn21Y7Uxatf2TYY+35bd9BT8Tc14LZ8K07XwwvTGpf179jLvXnRKa58YZ2V3br1WrFvfff7/DJzZFZRgGeXl5ZR2G0wuonEuTFmdJ+8udN77Yy8fbf2Tq4l+5odWZsg5NiiGkZg6Vg/PY+b2fZdu5M678+mMFmrQ8B0CTVmc5k+ZqSWwAdn7vh2GGxjefu+YxS8kJrJRN69tOserLi1W9xk1Pk5nhZklsAH7cWhnDbKLRjWllEKU4oyJVbooz0Gfo0KFXHczldOjQgZtuugkvLy/ef/99PDw8GDRokGUWxbS0NGJjY/nyyy/Jzs6mVatWvPnmmzRr1gyAvn37kpaWxtKlSy3nHD58OLt27WLdunX07duX9evXs379eqZPnw4UTCV95MgR7rzzTlasWMELL7zAzz//zKpVqwgLC2PkyJH88MMPnD17liZNmhAXF2c17bRcvWo1Cyo6jw7/g/emhHF4bwU63f8XcYv2M+iuGzl+RH/dOYJKVQv+EEg7Zf2rJu2UG5Wq5ha0Ccoj7S/r/eZ8E2fSLrYRx9Sp+x+cP+vG5u8ujsmpWDmHtNPW1R1zvgtnMtypWNl2JVcuZcLOMTclFkn5U6Tk5s033yzSyUwmU6klNwDz589n5MiRJCQksGXLFvr27Uu7du3o0qULDz74IN7e3nzzzTcEBAQwZ84cOnXqxIEDB6hUqdIVzz19+nQOHDjAjTfeaBkIFRQUxJEjR4CC8UWvvfYadevWpWLFihw9epRu3boxZcoUPD09WbBgAT169GD//v3UrFmzSK8nOzub7OyL/zP/8/ke1zOTS8H/sSsWViX+fwUl8EN7fLi5XQaRD51i7lTbY3VEpHzocu8x1q0MJTdHA8Pl2ipScpOYmFjacRTJTTfdxPjx4wFo0KABb731FmvWrMHb25utW7eSkpJimVnxtddeY+nSpXz22WdFepR6QEAAHh4eVKhQodBZEidOnEiXLl0s65UqVbJUhQAmTZrEkiVL+Oqrr4iJiSnS64mLi+PFF18sUtvrTWqKBwBJv1lXaJJ+8yKoup6m7ShSUwp+xQQG5ZGacnHsTGBQHof2FIyfSj3lRmBl665eF1cDv0DrY8Sx3NA8lbDaZ3nlueZW20//5UFgResKjYurGT//3EvG68gVXONbwR2JQ91P+89xPdWqVSMlJYXdu3eTmZlJ5cqV8fX1tSyJiYkcOnSoRK799yeYAmRmZhIbG0uTJk0IDAzE19eXffv2WaahLopnn32W9PR0y3L06NESidUZnDzqwZ/J7tSom2W1vXrdbFKO6Rego0hO8uCvk27cfNvFsVIVfPNpfPM59u0oGGOzb7sPfoH51G96cXxN89syMbnArz9eOj2AOIa7eh7j4F5/Eg9aDzb/9eeK+PrnUb9xumVbs1Z/YXIx2P9L4DWO0sFd4xmKHYlDTSrg7m79V5zJZMJsNpOZmUm1atVYt27dJcdcmHvHxcUF4x/3EOfmFr0/38fHx2o9NjaW+Ph4XnvtNerXr4+3tzcPPPDAJQ8SvRxbj5V3Bl4V8gmtffGvs5CwbOqGn+NMmiunjnviG5BH1eo5VA4ueL9q1D0PwOlT7pw+5Q6Y+GxOCI+NOM7hfRU4tKcCXR74k7B655kyqF5ZvCSxwatCPqF1Lv7ch4TlUPeG8wWf9R8eLH0/iH8NS+GPRE+SkzyIHp3MXyfd2fz/c+Ec/c2LbWv9GP7aMWY+UwNXd4PBk4+x/stA3SlVDnl55xEadjERDQk9R92GGZxJd+fUyYJqnLdPLrd1Sub9aY0vOf7oEV+2b67CkOd/4e24G3B1M3hq1F42rKqmO6WkxDhUcmNLixYtSE5Oxs3Njdq1axfaJigoiF9+sZ5HYdeuXVYJk4eHB/n5+UW65qZNm+jbty/33XcfUFDJuTA+R6DhTWeZuvjiM0ieHFdQlYr/X2Vej61LRJc0/vP6xe7O594+DMBHb4by0bTqACz9bwgengZPjk3CLzCfw/u8eS6qESeS9AuwPGnY7Dyvfn6xQjroxeMArFpckddH1OTTt4PwqmBm2NRj+Prns2ebD89H1SX3bxMxvhJTk8FT/uDlTw/9/yR+AbzzQvVr/lrkyho0SeflOVst6wNG/grA6mXVefPFgup6+7tOgMlg/bfVCj3Hq2Ob8dSovUx5ZyuGYWLT2mDmvFb4vDlyGboV3CanSG46d+5MREQEvXr1YurUqTRs2JDjx4+zfPly7rvvPlq1akXHjh159dVXWbBgAREREXz00Uf88ssv3HzzzZbz1K5dm4SEBI4cOYKvr+9lByI3aNCAL774gh49emAymRg7dixms9lm++vNTz/407VWa5v74z+rQvxnVa54nk9nVbOa50bKn5+2+BIZ2uwyLUwseDWEBa/afuLvmTQ3Xh5c+ISPUr78vLMy3Vvffdk2K5fUZOUS2zdWZGZ48OrY5iUc2fWnLGYodhQONebGFpPJxIoVK7jjjjt44oknaNiwIX369OH3338nOLjgFsTIyEjGjh3L6NGjad26NWfOnOHxxx+3Ok9sbCyurq6Eh4cTFBR02fEzb7zxBhUrVqRt27b06NGDyMhIWrRoUaqvU0RERK7MZPxzIEoRfP/998yZM4dDhw7x2WefUb16dT788EPq1KnDbbfdVhpxXhcyMjIICAjgTvcHcTNprIGzM3J119f1xK16yT3xWMqvPHM2q4/PIT09HX9/WzN32+fCd0XtyVNw8br6bnpzVhZHXni+VGMtK8Wu3Hz++edERkbi7e3Njz/+aJmnJT09nZdeeqnEAxQREZFC6G4pm4qd3EyePJnZs2fz3nvvWQ3GbdeuHTt37izR4ERERESKq9gDivfv388dd9xxyfaAgADS0tJKIiYRERG5Ag0otq3YlZuQkBB+++23S7Zv3LiRunXrlkhQIiIicgUXZii2Z3FSxU5uBgwYwLBhw0hISMBkMnH8+HEWLlxIbGwsTz31VGnEKCIiIv+kMTc2FbtbasyYMZjNZjp16sS5c+e444478PT0JDY2liFDhpRGjCIiIiJFVuzkxmQy8fzzzzNq1Ch+++03MjMzCQ8Px9fXtzTiExERkUJozI1tVz1DsYeHB+Hhmi5bRESkTOjxCzYVO7m58847MZlsD0Jau3atXQGJiIiI2KPYyU3z5s2t1nNzc9m1axe//PIL0dHRJRWXiIiIXI6d3VKq3PzNm2++Wej2CRMmkJmZaXdAIiIiUgTqlrKpxB6c+eijj/Lf//63pE4nIiIiclWuekDxP23ZsgUvOx7gJSIiIsWgyo1NxU5u7r//fqt1wzA4ceIE27dvZ+zYsSUWmIiIiNimW8FtK3ZyExAQYLXu4uJCo0aNmDhxInfddVeJBSYiIiJyNYqV3OTn5/PEE0/QtGlTKlasWFoxiYiIiFy1Yg0odnV15a677tLTv0VERMqani1lU7Hvlrrxxhs5fPhwacQiIiIiRXRhzI09i7MqdnIzefJkYmNjWbZsGSdOnCAjI8NqERERESlLRR5zM3HiRP7zn//QrVs3AO69916rxzAYhoHJZCI/P7/koxQREZFLOXH1xR5FTm5efPFFBg0axHfffVea8YiIiEhRaJ4bm4qc3BhGwbvQvn37UgtGRERExF7FuhX8ck8DFxERkWtHk/jZVqzkpmHDhldMcFJTU+0KSERERIpA3VI2FSu5efHFFy+ZoVhERESkPClWctOnTx+qVq1aWrGIiIhIEalbyrYiJzcabyMiIlKOqFvKpiJP4nfhbikRERGR8qzIlRuz2VyacYiIiEhxqHJjU7HG3IiIiEj5oDE3tim5ERERcUSq3NhU7AdnioiIiJRnqtyIiIg4IlVubFJyIyIi4oA05sY2dUuJiIiIU1HlRkRExBGpW8omJTciIiIOSN1StqlbSkRERJyKKjciIiKOSN1SNim5ERERcURKbmxSt5SIiIg4FVVuREREHJDp/xd7jndWSm5EREQckbqlbFJyIyIi4oB0K7htGnMjIiIiTkWVGxEREUekbimblNyIiIg4KidOUOyhbikRERFxKqrciIiIOCANKLZNyY2IiIgj0pgbm9QtJSIiIk5FlRsREREHpG4p25TciIiIOCJ1S9mkbikRERFxKqrclENZd96Im7tXWYchpcxr1Y9lHYJcQ8u3rSjrEOQayDhjpmLDa3MtdUvZpuRGRETEEalbyiZ1S4mIiDgiowSWq/Tyyy9jMpkYPny4ZVtWVhaDBw+mcuXK+Pr60rt3b06ePGl1XFJSEt27d6dChQpUrVqVUaNGkZeXd/WB2KDkRkRERIps27ZtzJkzh5tuuslq+4gRI/j666/53//+x/r16zl+/Dj333+/ZX9+fj7du3cnJyeHzZs3M3/+fObNm8e4ceNKPEYlNyIiIg7owpgbe5biyszMJCoqivfee4+KFStatqenp/PBBx/wxhtv0LFjR1q2bMncuXPZvHkzP/zwAwCrVq1i7969fPTRRzRv3py7776bSZMm8fbbb5OTk1NSbwug5EZERMQxlVC3VEZGhtWSnZ1t85KDBw+me/fudO7c2Wr7jh07yM3NtdreuHFjatasyZYtWwDYsmULTZs2JTg42NImMjKSjIwM9uzZY8cbcSklNyIiItexsLAwAgICLEtcXFyh7T755BN27txZ6P7k5GQ8PDwIDAy02h4cHExycrKlzd8Tmwv7L+wrSbpbSkRExAGZDAOTcfWjgi8ce/ToUfz9/S3bPT09L2l79OhRhg0bRnx8PF5e5X+qElVuREREHFEJdUv5+/tbLYUlNzt27CAlJYUWLVrg5uaGm5sb69evZ8aMGbi5uREcHExOTg5paWlWx508eZKQkBAAQkJCLrl76sL6hTYlRcmNiIiIXFanTp34+eef2bVrl2Vp1aoVUVFRln+7u7uzZs0ayzH79+8nKSmJiIgIACIiIvj5559JSUmxtImPj8ff35/w8PASjVfdUiIiIg7oWs5Q7Ofnx4033mi1zcfHh8qVK1u29+vXj5EjR1KpUiX8/f0ZMmQIERER3HrrrQDcddddhIeH89hjjzF16lSSk5N54YUXGDx4cKHVInsouREREXFE5WyG4jfffBMXFxd69+5NdnY2kZGRvPPOO5b9rq6uLFu2jKeeeoqIiAh8fHyIjo5m4sSJJRsISm5ERETkKqxbt85q3cvLi7fffpu3337b5jG1atVixYrSf86akhsREREHpAdn2qbkRkRExBGVs26p8kTJjYiIiANS5cY23QouIiIiTkWVGxEREUekbimblNyIiIg4KGfuWrKHuqVERETEqahyIyIi4ogMo2Cx53gnpeRGRETEAeluKdvULSUiIiJORZUbERERR6S7pWxSciMiIuKATOaCxZ7jnZW6pURERMSpqHIjIiLiiNQtZZOSGxEREQeku6VsU3IjIiLiiDTPjU0acyMiIiJORZUbERERB6RuKduU3IiIiDgiDSi2Sd1SIiIi4lRUuREREXFA6payTcmNiIiII9LdUjapW0pERESciio3IiIiDkjdUrYpuREREXFEulvKJnVLiYiIiFNR5UZERMQBqVvKNiU3IiIijshsFCz2HO+klNyIiIg4Io25sUljbkRERMSpqHIjIiLigEzYOeamxCIpf5TciIiIOCLNUGyTuqVERETEqahyIyIi4oB0K7htSm5EREQcke6WskndUiIiIuJUVLkRERFxQCbDwGTHoGB7ji3vlNyIiIg4IvP/L/Yc76TULSUiIiJORZUbERERB6RuKduU3IiIiDgi3S1lk5IbERERR6QZim3SmBsRERFxKqrcSKm4t8NeenbYR0jlTACOHK/I/K9vZusvYQDcc8evdG7zGw1q/oWPdy73DHmMzPOeVuf45OVPCKmSabXt3c9bs+ibZtfmRUiR3HjLGR4YdJIGTc9ROTiXF/vXY8uqQMv+dl1P0+3RUzRoeg7/ivk83bUJh/dWsDrH0LjfaX5bBpWDczl/1pV9O3z4IK4Gxw55XeNXI3/38w8+/O+dqhz8uQKpJ90Z/0Eibe9Ot+w3DFjwaggrF1UmM8OV8FZnGfryUarXzbnkXDnZJoZ1b8jhvd68s2o/9W48X7A9y8SMMWEc/MmbpINetOmcwYS5idfsNToyzVBs23VXuVm3bh0mk4m0tLTLtqtduzbTpk27JjE5o1OnfXj381sYOKkXT07uxc5fqzElJp7aoacB8PLIY+svYSxc0fyy5/lgaUvuH/mIZfliTfg1iF6Kw6uCmcS93rz9QpjN/Xu2+fLfuBo2z3Hw5wq88Z/aDOx4Ay881gCTCV766AAuLk7829cBZJ1zoe4N54l56Vih+z99uypf/jeIIS8fZfqyA3hVMPPcI/XIybr0edMfTA6lckjuJdvNZhMeXmZ69jvFzbefKfHX4NQudEvZszip665y07ZtW06cOEFAQAAA8+bNY/jw4ZckO9u2bcPHx6cMInQOW3bXslr/YElrenb4lfC6KRw5XpHPVt8IQPNGxy97nvNZ7qRmVLhsGylb29cFsH1dgM39a76oDEBwjWybbb5ZFGT598ljMP/VUGat2kdwWA4nfve0eZyUrtYdz9C6Y+EJh2HA0veD+NewZNp2zQBg9IzfebjZjWxeGUCHXmmWttvW+rFjvR9j309k21p/q/N4VTAz9OWC5GnvNl8y011L58XIdeW6S248PDwICQm5YrugoKArtpGicTGZ6dAqES+PXPYcqlqsYx+5ezeP3fMjKam+rE6ox2fxN5Jvvu4KjtcVT+98ujz0FyeSPDh13L2swxEbkpM8SE1xp8XtF7uOffzNNL75HPt2+FiSm9On3Jg2Kozx/03E09t5KwVlwWQuWOw53lmVy2+JDh06EBMTQ0xMDAEBAVSpUoWxY8di/H8J7fTp0zz++ONUrFiRChUqcPfdd3Pw4EHL8b///js9evSgYsWK+Pj4cMMNN7BixQrAultq3bp1PPHEE6Snp2MymTCZTEyYMAGw7pZ65JFHePjhh61izM3NpUqVKixYsAAAs9lMXFwcderUwdvbm2bNmvHZZ5+V8jtVvtWpnso3b80jfvZcRj62ibHvdOH3ExWLfPzna25g4rt3MuK1bny9vjGPdtvFkw9sLcWIpSzd81gKS/b9yJf7d9G6QzrPRTUkL7dc/ooSIDWl4G/jwCDrrqbAoFzLPsOA14bXpPtjf9Gw2flrHqPTU7eUTeW2cjN//nz69evH1q1b2b59OwMHDqRmzZoMGDCAvn37cvDgQb766iv8/f155pln6NatG3v37sXd3Z3BgweTk5PDhg0b8PHxYe/evfj6+l5yjbZt2zJt2jTGjRvH/v37AQptFxUVxYMPPkhmZqZl/7fffsu5c+e47777AIiLi+Ojjz5i9uzZNGjQgA0bNvDoo48SFBRE+/btC32N2dnZZGdfLNVnZGTY/b6VJ0eTA+g/8T58vHNp3zKRZ/+9nmFTuxc5wflffFPLvw8fq0xungv/eWwj733Rmtw8la6dzdqlldn5vT+VqubywJMnee6dw4y8vxG52UpwHNWXH1ThfKYLDw85WdahyHWm3CY3YWFhvPnmm5hMJho1asTPP//Mm2++SYcOHfjqq6/YtGkTbdu2BWDhwoWEhYWxdOlSHnzwQZKSkujduzdNmxZ8OdatW7fQa3h4eBAQEIDJZLpsV1VkZCQ+Pj4sWbKExx57DIBFixZx77334ufnR3Z2Ni+99BKrV68mIiLCcs2NGzcyZ84cm8lNXFwcL7744lW/R+VdXr4rf6QUjMU48HsVGtc+Re/Oe3jjw9uu6nz7Eqvi5mYQUvkMR08GlmCkUh6cO+PKuTOuHD/ixa8/+vDZz7tpF5nGuq8qlXVoUohKVfMASDvlTuXgPMv2tFPu1LuhoEqza5Mf+3b4cE9t6zscY+5uSMf7TzNqetK1C9gZaRI/m8rtn0S33norJtPFEfcREREcPHiQvXv34ubmRps2bSz7KleuTKNGjdi3bx8AQ4cOZfLkybRr147x48fz008/2RWLm5sbDz30EAsXLgTg7NmzfPnll0RFRQHw22+/ce7cObp06YKvr69lWbBgAYcOHbJ53meffZb09HTLcvToUbviLO9MJgMPt/yrPr5+2F/km02cPuNdglFJeWQyASYDdw8nHhTg4EJq5lCpai4/brxY7T57xoVff6xAk5ZnAXh60jFmrd7PrPiCZfKHhwF4bvYR+j5zokzidiYXHr9gz+Ksym3lxh79+/cnMjKS5cuXs2rVKuLi4nj99dcZMmTIVZ8zKiqK9u3bk5KSQnx8PN7e3nTt2hWAzMyCAXXLly+nevXqVsd5etq+08PT0/Oy+x3ZgPu3kfBzDVJSffH2yqVzm0M0b3SCUdMK3rNK/ueoFHCe6lULuuLq1DjN+Sx3Tqb6cOasF+F1TxJe9xQ//lqNc1nu3FAvhcEP/0D8D/XJPOec75mj8qqQT2jti92rIWHZ1A0/x5k0N04d98A3II+q1XOoHFwwNqNGvSwATp9y5/Qpd0JqZtO+Ryo7NviT/pc7Varl8PDTyeRkubD1O9t3YUnpO3/WheOJF/9/Sz7qwaFfvPELzKNqjVx69T/Fx9ODqV4nm5CaOcyfWo3Kwbm07VowF07VGrnAxTE5Xj4FyWporRyCQi9u//2AJ3k5Lpw57cq5sy4c+qXgD5gLc+GIFFe5TW4SEhKs1n/44QcaNGhAeHg4eXl5JCQkWLql/vrrL/bv3094+MU5UMLCwhg0aBCDBg3i2Wef5b333is0ufHw8CA//8rVhLZt2xIWFsbixYv55ptvePDBB3F3L7iTIzw8HE9PT5KSkmx2QV1vAv3O81y/9VQKOMfZ8x4cPlaJUdO6smNvwVwn93bYR997f7S0n/nMMgBe/u8drNzckNw8Vzq2PkTfe3fi7pbPiT/9+F/8jVbjcKR8aHjTOaZ+esCy/uT4gtt64/9Xmdf/U5uILmn8543fLfufe7tggraP3qzGR2+GkpNt4obWmfT6dwq+Afmk/enGzwl+jLyvMel/6W6psnRgdwVGP1Dfsj5nQsEfb10eSiV2WhIPDU4h65wL00eHkZnhyg2tzzJl4WE8vIpXERj7aD1OHvOwrD99VyMAvj2+y/4X4cz0+AWbym1yk5SUxMiRI3nyySfZuXMnM2fO5PXXX6dBgwb07NmTAQMGMGfOHPz8/BgzZgzVq1enZ8+eAAwfPpy7776bhg0bcvr0ab777juaNGlS6HVq165NZmYma9asoVmzZlSoUIEKFQqfV+WRRx5h9uzZHDhwgO+++86y3c/Pj9jYWEaMGIHZbOa2224jPT2dTZs24e/vT3R0dMm/QeXcq/PvuOz+eV+1ZN5XLW3uP5hUhafjepZ0WFIKfvrBj641bX+W8Z9VIf6zKjb3p570YFzfBqURmtipWdvMyyYYJhNEj04menRykc4XEpZT6PkWbN17lRFe5wzAnp5b581tyu+Ym8cff5zz589zyy23MHjwYIYNG8bAgQMBmDt3Li1btuSee+4hIiICwzBYsWKFpZKSn5/P4MGDadKkCV27dqVhw4a88847hV6nbdu2DBo0iIcffpigoCCmTp1qM6aoqCj27t1L9erVadeundW+SZMmMXbsWOLi4izXXb58OXXq1Cmhd0REROQijbmxzWQY5e/VdejQgebNm193jz/IyMggICCAiLtexM1dz9Rxdl6rfrxyI3EaK5O2l3UIcg1knDFTseFh0tPT8ff3v/IBV3ON//+u6HjzGNxcr/67Ii8/i7U/vlyqsZaVctstJSIiIpdhYOeYmxKLpNxRciMiIuKINKDYpnKZ3Kxbt66sQxAREREHVS6TGxEREbkCM2C6YqvLH++klNyIiIg4IHvveHLmu6XK7a3gIiIiIldDlRsRERFHpAHFNim5ERERcURKbmxSt5SIiIg4FVVuREREHJEqNzYpuREREXFEuhXcJnVLiYiIOKBr/eDMuLg4WrdujZ+fH1WrVqVXr17s37/fqk1WVhaDBw+mcuXK+Pr60rt3b06ePGnVJikpie7du1OhQgWqVq3KqFGjyMvLs/v9+DslNyIiInJF69evZ/Dgwfzwww/Ex8eTm5vLXXfdxdmzZy1tRowYwddff83//vc/1q9fz/Hjx7n//vst+/Pz8+nevTs5OTls3ryZ+fPnM2/ePMaNG1eisapbSkRExBFd4zE3K1eutFqfN28eVatWZceOHdxxxx2kp6fzwQcfsGjRIjp27AjA3LlzadKkCT/88AO33norq1atYu/evaxevZrg4GCaN2/OpEmTeOaZZ5gwYQIeHh5X/3r+RpUbERERR2Q27F+AjIwMqyU7O7tIl09PTwegUqVKAOzYsYPc3Fw6d+5sadO4cWNq1qzJli1bANiyZQtNmzYlODjY0iYyMpKMjAz27NlTIm8LKLkRERG5roWFhREQEGBZ4uLirniM2Wxm+PDhtGvXjhtvvBGA5ORkPDw8CAwMtGobHBxMcnKypc3fE5sL+y/sKynqlhIREXFEJdQtdfToUfz9/S2bPT09r3jo4MGD+eWXX9i4cePVX78UKbkRERFxSHYmNxQc6+/vb5XcXElMTAzLli1jw4YN1KhRw7I9JCSEnJwc0tLSrKo3J0+eJCQkxNJm69atVue7cDfVhTYlQd1SIiIickWGYRATE8OSJUtYu3YtderUsdrfsmVL3N3dWbNmjWXb/v37SUpKIiIiAoCIiAh+/vlnUlJSLG3i4+Px9/cnPDy8xGJV5UZERMQRXeO7pQYPHsyiRYv48ssv8fPzs4yRCQgIwNvbm4CAAPr168fIkSOpVKkS/v7+DBkyhIiICG699VYA7rrrLsLDw3nssceYOnUqycnJvPDCCwwePLhI3WFFpeRGRETEEZkNLnQtXf3xRTdr1iwAOnToYLV97ty59O3bF4A333wTFxcXevfuTXZ2NpGRkbzzzjuWtq6urixbtoynnnqKiIgIfHx8iI6OZuLEiVf/Ogqh5EZERESuyChCpcfLy4u3336bt99+22abWrVqsWLFipIM7RJKbkRERByRYS5Y7DneSSm5ERERcUR6KrhNSm5EREQc0TUec+NIdCu4iIiIOBVVbkRERByRuqVsUnIjIiLiiAzsTG5KLJJyR91SIiIi4lRUuREREXFE6payScmNiIiIIzKbATvmqjE77zw36pYSERERp6LKjYiIiCNSt5RNSm5EREQckZIbm9QtJSIiIk5FlRsRERFHpMcv2KTkRkRExAEZhhnDjid723NseafkRkRExBEZhn3VF425EREREXEMqtyIiIg4IsPOMTdOXLlRciMiIuKIzGYw2TFuxonH3KhbSkRERJyKKjciIiKOSN1SNim5ERERcUCG2YxhR7eUM98Krm4pERERcSqq3IiIiDgidUvZpORGRETEEZkNMCm5KYy6pURERMSpqHIjIiLiiAwDsGeeG+et3Ci5ERERcUCG2cCwo1vKUHIjIiIi5Yphxr7KjW4FFxEREXEIqtyIiIg4IHVL2abkRkRExBGpW8omJTflyIUsOi8vq4wjkWshz8gt6xDkGso447xfJHJRRmbB53wtqiJ55No1h18ezvs7yGQ4c13KwRw7doywsLCyDkNEROx09OhRatSoUSrnzsrKok6dOiQnJ9t9rpCQEBITE/Hy8iqByMoPJTfliNls5vjx4/j5+WEymco6nGsmIyODsLAwjh49ir+/f1mHI6VIn/X143r9rA3D4MyZM4SGhuLiUnr37GRlZZGTk2P3eTw8PJwusQF1S5UrLi4upZbpOwJ/f//r6pfg9Uyf9fXjevysAwICSv0aXl5eTpmUlBTdCi4iIiJORcmNiIiIOBUlN1LmPD09GT9+PJ6enmUdipQyfdbXD33WUpY0oFhERESciio3IiIi4lSU3IiIiIhTUXIjIiIiTkXJjTiUCRMm0Lx587IOQ8qZ2rVrM23atLIOQ4B169ZhMplIS0u7bDt9ZlKalNxIuWUymVi6dKnVttjYWNasWVM2AUmJ6dChA8OHDy/rMKQUtG3blhMnTlgmsps3bx6BgYGXtNu2bRsDBw68xtHJ9UIzFItD8fX1xdfXt6zDkGvAMAzy8/Nxc9OvKUfi4eFBSEjIFdsFBQVdg2jkeqXKjVyiQ4cODB06lNGjR1OpUiVCQkKYMGGCZX9aWhr9+/cnKCgIf39/OnbsyO7du63OMXnyZKpWrYqfnx/9+/dnzJgxVt1J27Zto0uXLlSpUoWAgADat2/Pzp07Lftr164NwH333YfJZLKs/71batWqVXh5eV1S/h42bBgdO3a0rG/cuJHbb78db29vwsLCGDp0KGfPnrX7fXJW9n7+ffv2pVevXlbnHD58OB06dLDsX79+PdOnT8dkMmEymThy5IilO+Obb76hZcuWeHp6snHjRg4dOkTPnj0JDg7G19eX1q1bs3r16mvwTjivDh06EBMTQ0xMDAEBAVSpUoWxY8danmR9+vRpHn/8cSpWrEiFChW4++67OXjwoOX433//nR49elCxYkV8fHy44YYbWLFiBWDdLbVu3TqeeOIJ0tPTLZ/1hZ+lv3dLPfLIIzz88MNWMebm5lKlShUWLFgAFDx7Ly4ujjp16uDt7U2zZs347LPPSvmdEkel5EYKNX/+fHx8fEhISGDq1KlMnDiR+Ph4AB588EFSUlL45ptv2LFjBy1atKBTp06kpqYCsHDhQqZMmcIrr7zCjh07qFmzJrNmzbI6/5kzZ4iOjmbjxo388MMPNGjQgG7dunHmzBmgIPkBmDt3LidOnLCs/12nTp0IDAzk888/t2zLz89n8eLFREVFAXDo0CG6du1K7969+emnn1i8eDEbN24kJiam5N80J2LP538l06dPJyIiggEDBnDixAlOnDhBWFiYZf+YMWN4+eWX2bdvHzfddBOZmZl069aNNWvW8OOPP9K1a1d69OhBUlJSqbz268X8+fNxc3Nj69atTJ8+nTfeeIP3338fKEhAt2/fzldffcWWLVswDINu3bqRm5sLwODBg8nOzmbDhg38/PPPvPLKK4VWVNu2bcu0adPw9/e3fNaxsbGXtIuKiuLrr78mMzPTsu3bb7/l3Llz3HfffQDExcWxYMECZs+ezZ49exgxYgSPPvoo69evL423RxydIfIP7du3N2677Tarba1btzaeeeYZ4/vvvzf8/f2NrKwsq/316tUz5syZYxiGYbRp08YYPHiw1f527doZzZo1s3nN/Px8w8/Pz/j6668t2wBjyZIlVu3Gjx9vdZ5hw4YZHTt2tKx/++23hqenp3H69GnDMAyjX79+xsCBA63O8f333xsuLi7G+fPnbcZzPbP384+OjjZ69uxptX/YsGFG+/btra4xbNgwqzbfffedARhLly69Yow33HCDMXPmTMt6rVq1jDfffPPKL04Mwyh4/5s0aWKYzWbLtmeeecZo0qSJceDAAQMwNm3aZNn3559/Gt7e3sann35qGIZhNG3a1JgwYUKh577wOV74f3Du3LlGQEDAJe3+/pnl5uYaVapUMRYsWGDZ/69//ct4+OGHDcMwjKysLKNChQrG5s2brc7Rr18/41//+lexX784P1VupFA33XST1Xq1atVISUlh9+7dZGZmUrlyZcv4F19fXxITEzl06BAA+/fv55ZbbrE6/p/rJ0+eZMCAATRo0ICAgAD8/f3JzMws9l/jUVFRrFu3juPHjwMFVaPu3btbBjDu3r2befPmWcUaGRmJ2WwmMTGxWNe6ntjz+durVatWVuuZmZnExsbSpEkTAgMD8fX1Zd++farc2OnWW2/FZDJZ1iMiIjh48CB79+7Fzc2NNm3aWPZVrlyZRo0asW/fPgCGDh3K5MmTadeuHePHj+enn36yKxY3NzceeughFi5cCMDZs2f58ssvLRXY3377jXPnztGlSxern7sFCxaU2M+dOBeN1JNCubu7W62bTCbMZjOZmZlUq1aNdevWXXJMYXdE2BIdHc1ff/3F9OnTqVWrFp6enkRERJCTk1OsOFu3bk29evX45JNPeOqpp1iyZAnz5s2z7M/MzOTJJ59k6NChlxxbs2bNYl3remLP5+/i4mIZu3HBhe6MovDx8bFaj42NJT4+ntdee4369evj7e3NAw88UOyfFSk5/fv3JzIykuXLl7Nq1Sri4uJ4/fXXGTJkyFWfMyoqivbt25OSkkJ8fDze3t507doVwNJdtXz5cqpXr251nJ5dJYVRciPF0qJFC5KTk3Fzc7MM8v2nRo0asW3bNh5//HHLtn+Omdm0aRPvvPMO3bp1A+Do0aP8+eefVm3c3d3Jz8+/YkxRUVEsXLiQGjVq4OLiQvfu3a3i3bt3L/Xr1y/qS5TLKMrnHxQUxC+//GK1bdeuXVYJk4eHR5E+Wyj4Wenbt69l7EVmZiZHjhy5qvjlooSEBKv1C2PfwsPDycvLIyEhgbZt2wLw119/sX//fsLDwy3tw8LCGDRoEIMGDeLZZ5/lvffeKzS5Kepn3bZtW8LCwli8eDHffPMNDz74oOVnJjw8HE9PT5KSkmjfvr09L1uuE+qWkmLp3LkzERER9OrVi1WrVnHkyBE2b97M888/z/bt2wEYMmQIH3zwAfPnz+fgwYNMnjyZn376yaoE3qBBAz788EP27dtHQkICUVFReHt7W12rdu3arFmzhuTkZE6fPm0zpqioKHbu3MmUKVN44IEHrP6Se+aZZ9i8eTMxMTHs2rWLgwcP8uWXX2pA8VUqyuffsWNHtm/fzoIFCzh48CDjx4+/JNmpXbs2CQkJHDlyhD///BOz2Wzzmg0aNOCLL75g165d7N69m0ceeeSy7aVokpKSGDlyJPv37+fjjz9m5syZDBs2jAYNGtCzZ08GDBjAxo0b2b17N48++ijVq1enZ8+eQMHdb99++y2JiYns3LmT7777jiZNmhR6ndq1a5OZmcmaNWv4888/OXfunM2YHnnkEWbPnk18fLylSwrAz8+P2NhYRowYwfz58zl06BA7d+5k5syZzJ8/v2TfGHEKSm6kWEwmEytWrOCOO+7giSeeoGHDhvTp04fff/+d4OBgoCDZePbZZ4mNjaVFixYkJibSt29fvLy8LOf54IMPOH36NC1atOCxxx5j6NChVK1a1epar7/+OvHx8YSFhXHzzTfbjKl+/frccsst/PTTT1a/EKFg7Mj69es5cOAAt99+OzfffDPjxo0jNDS0BN+V60dRPv/IyEjGjh3L6NGjad26NWfOnLGq4kFBV5Orqyvh4eEEBQVddvzMG2+8QcWKFWnbti09evQgMjKSFi1alOrrvB48/vjjnD9/nltuuYXBgwczbNgwy6R6c+fOpWXLltxzzz1ERERgGAYrVqywVFLy8/MZPHgwTZo0oWvXrjRs2JB33nmn0Ou0bduWQYMG8fDDDxMUFMTUqVNtxhQVFcXevXupXr067dq1s9o3adIkxo4dS1xcnOW6y5cvp06dOiX0jogzMRn/7BwXKQVdunQhJCSEDz/8sKxDEbnudejQgebNm+vxB+K0NOZGSty5c+eYPXs2kZGRuLq68vHHH7N69WrLPCkiIiKlScmNlLgLXRdTpkwhKyuLRo0a8fnnn9O5c+eyDk1ERK4D6pYSERERp6IBxSIiIuJUlNyIiIiIU1FyIyIiIk5FyY2IiIg4FSU3ImKlb9++9OrVy7LeoUMHhg8ffs3jWLduHSaTibS0NJttTCYTS5cuLfI5J0yYQPPmze2K68iRI5hMJnbt2mXXeUSk9Ci5EXEAffv2xWQyYTKZ8PDwoH79+kycOJG8vLxSv/YXX3zBpEmTitS2KAmJiEhp0zw3Ig6ia9euzJ07l+zsbFasWMHgwYNxd3fn2WefvaRtTk4OHh4eJXLdSpUqlch5RESuFVVuRByEp6cnISEh1KpVi6eeeorOnTvz1VdfARe7kqZMmUJoaCiNGjUCCp62/tBDDxEYGEilSpXo2bOn1RO18/PzGTlyJIGBgVSuXJnRo0fzz6mv/tktlZ2dzTPPPENYWBienp7Ur1+fDz74gCNHjnDnnXcCULFiRUwmE3379gXAbDYTFxdHnTp18Pb2plmzZnz22WdW11mxYgUNGzbE29ubO++886qe/P3MM8/QsGFDKlSoQN26dRk7diy5ubmXtJszZw5hYWFUqFCBhx56iPT0dKv977//Pk2aNMHLy4vGjRvbfG6SiJRPSm5EHJS3tzc5OTmW9TVr1rB//37i4+NZtmwZubm5REZG4ufnx/fff8+mTZvw9fWla9euluNef/115s2bx3//+182btxIamoqS5Ysuex1H3/8cT7++GNmzJjBvn37mDNnDr6+voSFhfH5558DsH//fk6cOMH06dMBiIuLY8GCBcyePZs9e/YwYsQIHn30UdavXw8UJGH3338/PXr0YNeuXfTv358xY8YU+z3x8/Nj3rx57N27l+nTp/Pee+/x5ptvWrX57bff+PTTT/n6669ZuXIlP/74I08//bRl/8KFCxk3bhxTpkxh3759vPTSS4wdO1ZPnxZxJIaIlHvR0dFGz549DcMwDLPZbMTHxxuenp5GbGysZX9wcLCRnZ1tOebDDz80GjVqZJjNZsu27Oxsw9vb2/j2228NwzCMatWqGVOnTrXsz83NNWrUqGG5lmEYRvv27Y1hw4YZhmEY+/fvNwAjPj6+0Di/++47AzBOnz5t2ZaVlWVUqFDB2Lx5s1Xbfv36Gf/6178MwzCMZ5991ggPD7fa/8wzz1xyrn8CjCVLltjc/+qrrxotW7a0rI8fP95wdXU1jh07Ztn2zTffGC4uLsaJEycMwzCMevXqGYsWLbI6z6RJk4yIiAjDMAwjMTHRAIwff/zR5nVFpGxpzI2Ig1i2bBm+vr7k5uZiNpt55JFHmDBhgmV/06ZNrcbZ7N69m99++w0/Pz+r82RlZXHo0CHS09M5ceIEbdq0sexzc3OjVatWl3RNXbBr1y5cXV1p3759keP+7bffOHfuHF26dLHanpOTw8033wzAvn37rOIAiIiIKPI1Lli8eDEzZszg0KFDZGZmkpeXh7+/v1WbmjVrUr16davrmM1m9u/fj5+fH4cOHaJfv34MGDDA0iYvL4+AgIBixyMiZUPJjYiDuPPOO5k1axYeHh6Ehobi5mb9v6+Pj4/VemZmJi1btmThwoWXnCsoKOiqYvD29i72MZmZmQAsX77cKqmAgnFEJWXLli1ERUXx4osvEhkZSUBAAJ988gmvv/56sWN97733Lkm2XF1dSyxWESldSm5EHISPjw/169cvcvsWLVqwePFiqlatekn14oJq1aqRkJDAHXfcARRUKHbs2EGLFi0Kbd+0aVPMZjPr168v9CnvFypH+fn5lm3h4eF4enqSlJRks+LTpEkTy+DoC3744Ycrv8i/2bx5M7Vq1eL555+3bPv9998vaZeUlMTx48cJDQ21XMfFxYVGjRoRHBxMaGgohw8fJioqqljXF5HyQwOKRZxUVFQUVapUoWfPnnz//fckJiaybt06hg4dyrFjxwAYNmwYL7/8MkuXLuXXX3/l6aefvuwcNbVr1yY6Opp///vfLF261HLOTz/9FIBatWphMplYtmwZp06dIjMzEz8/P2JjYxkxYgTz58/n0KFD7Ny5k5kzZ1oG6Q4aNIiDBw8yatQo9u/fz6JFi5g3b16xXm+DBg1ISkrik08+4dChQ8yYMaPQwdFeXl5ER0eze/duvv/+e4YOHcpDDz1ESEgIAC+++CJxcXHMmDGDAwcO8PPPPzN37lzeeOONYsUjImVHyY2Ik6pQoQIbNmygZs2a3H///TRp0oR+/fqRlZVlqeT85z//4bHHHiM6OpqIiAj8/Py47777LnveWbNm8cADD/D000/TuHFjBgwYwNmzZwGoXr06L774ImPGjCE4OJiYmBgAJk2axNixY4mLi6NJkyZ07dqV5cuXU6dOHaBgHMznn3/O0qVLadasGbNnz+all14q1uu99957GTFiBDExMTRv3pzNmzczduzYS9rVr1+f+++/n27dunHXXXdx0003Wd3q3b9/f95//33mzp1L06ZNad++PfPmzbPEKiLln8mwNXJQRERExAGpciMiIiJORcmNiIiIOBUlNyIiIuJUlNyIiIiIU1FyIyIiIk5FyY2IiIg4FSU3IiIi4lSU3IiIiIhTUXIjIiIiTkXJjYiIiDgVJTciIiLiVJTciIiIiFP5P6LnlqBk8s45AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.67      0.60      0.63      1465\n",
      "     neutral       0.28      0.26      0.27       386\n",
      "    positive       0.63      0.71      0.67      1469\n",
      "\n",
      "    accuracy                           0.61      3320\n",
      "   macro avg       0.53      0.52      0.52      3320\n",
      "weighted avg       0.61      0.61      0.61      3320\n",
      "\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "Model Building for MLP using Word2Vec feature extraction\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "[CV 1/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.01;, score=0.544 total time=  10.5s\n",
      "[CV 2/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.01;, score=0.536 total time=  12.5s\n",
      "[CV 3/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.01;, score=0.511 total time=  15.8s\n",
      "[CV 1/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.1;, score=0.441 total time=   2.5s\n",
      "[CV 2/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.1;, score=0.441 total time=   6.5s\n",
      "[CV 3/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.1;, score=0.441 total time=   6.1s\n",
      "[CV 1/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.01;, score=0.507 total time=   7.0s\n",
      "[CV 2/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.01;, score=0.541 total time=   6.9s\n",
      "[CV 3/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.01;, score=0.509 total time=  14.2s\n",
      "[CV 1/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.1;, score=0.441 total time=   3.8s\n",
      "[CV 2/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.1;, score=0.490 total time=   1.6s\n",
      "[CV 3/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.1;, score=0.441 total time=   2.9s\n",
      "[CV 1/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.01;, score=0.546 total time=   2.9s\n",
      "[CV 2/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.01;, score=0.535 total time=   2.7s\n",
      "[CV 3/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.01;, score=0.519 total time=   8.2s\n",
      "[CV 1/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.1;, score=0.441 total time=   2.3s\n",
      "[CV 2/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.1;, score=0.454 total time=   2.7s\n",
      "[CV 3/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.1;, score=0.441 total time=   2.8s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 450x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.62      0.24      0.35      1465\n",
      "     neutral       0.64      0.08      0.15       386\n",
      "    positive       0.49      0.91      0.64      1469\n",
      "\n",
      "    accuracy                           0.52      3320\n",
      "   macro avg       0.59      0.41      0.38      3320\n",
      "weighted avg       0.57      0.52      0.45      3320\n",
      "\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "Model Building for MLP using BERT feature extraction\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "[CV 1/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.01;, score=0.614 total time=  10.2s\n",
      "[CV 2/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.01;, score=0.629 total time=  13.1s\n",
      "[CV 3/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.01;, score=0.615 total time=  12.6s\n",
      "[CV 1/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.1;, score=0.657 total time=  15.6s\n",
      "[CV 2/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.1;, score=0.636 total time=  38.8s\n",
      "[CV 3/3] END hidden_layer_sizes=(64, 32), learning_rate_init=0.1;, score=0.587 total time=  45.8s\n",
      "[CV 1/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.01;, score=0.620 total time=  10.0s\n",
      "[CV 2/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.01;, score=0.618 total time=  10.5s\n",
      "[CV 3/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.01;, score=0.604 total time=   7.8s\n",
      "[CV 1/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.1;, score=0.441 total time=   1.9s\n",
      "[CV 2/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.1;, score=0.441 total time=   2.4s\n",
      "[CV 3/3] END hidden_layer_sizes=(32, 16), learning_rate_init=0.1;, score=0.640 total time=  27.8s\n",
      "[CV 1/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.01;, score=0.621 total time=   8.2s\n",
      "[CV 2/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.01;, score=0.620 total time=  26.4s\n",
      "[CV 3/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.01;, score=0.593 total time=   5.8s\n",
      "[CV 1/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.1;, score=0.441 total time=   0.6s\n",
      "[CV 2/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.1;, score=0.441 total time=   0.4s\n",
      "[CV 3/3] END hidden_layer_sizes=(16, 8), learning_rate_init=0.1;, score=0.634 total time=   6.9s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 450x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.65      0.77      0.70      1465\n",
      "     neutral       0.00      0.00      0.00       386\n",
      "    positive       0.67      0.72      0.69      1469\n",
      "\n",
      "    accuracy                           0.66      3320\n",
      "   macro avg       0.44      0.50      0.46      3320\n",
      "weighted avg       0.58      0.66      0.62      3320\n",
      "\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "========================================================================================================================================================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = []\n",
    "for model_name in ['SVM', 'Naive-Bayes', 'MLP']:\n",
    "    for featuring_name, X_train_features, X_test_features in [\n",
    "        ('TF-IDF', X_train_tfidf, X_test_tfidf),\n",
    "        ('Word2Vec', X_train_word2vec, X_test_word2vec),\n",
    "        ('BERT', X_train_bert, X_test_bert)\n",
    "    ]:\n",
    "        print(f'Model Building for {model_name} using {featuring_name} feature extraction')\n",
    "        model_full_name = model_name + '_' + featuring_name\n",
    "        model_full_name = model_full_name.replace('-', '').lower()\n",
    "\n",
    "        try:\n",
    "            model = build_model(X_train_features, y_train, model_name)\n",
    "            \n",
    "            with open(os.path.join(pickle_folder, f'{model_full_name}.pickle'), 'wb') as fo:\n",
    "                pickle.dump(model, fo)\n",
    "            \n",
    "            y_pred = model.predict(X_test_features)\n",
    "            show_metrics(y_test, y_pred, model_full_name)\n",
    "            metrics.append({\n",
    "                'Model': model_name,\n",
    "                'Feature': featuring_name,\n",
    "                'Accuracy': accuracy_score(y_test, y_pred),\n",
    "                'F1_Score': f1_score(y_test, y_pred, average='weighted'),\n",
    "                'Precision': precision_score(y_test, y_pred, average='weighted'),\n",
    "                'Recall': recall_score(y_test, y_pred, average='weighted')\n",
    "            })\n",
    "        except Exception as e:\n",
    "            print(f'Exception occured while building {model_full_name}\\n{e}')\n",
    "        print('-' * 200)\n",
    "    print('=' * 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Model': 'SVM',\n",
       "  'Feature': 'TF-IDF',\n",
       "  'Accuracy': 0.6759036144578313,\n",
       "  'F1_Score': 0.6515372682154523,\n",
       "  'Precision': 0.6742817458280368,\n",
       "  'Recall': 0.6759036144578313},\n",
       " {'Model': 'SVM',\n",
       "  'Feature': 'Word2Vec',\n",
       "  'Accuracy': 0.5548192771084337,\n",
       "  'F1_Score': 0.5314704822871515,\n",
       "  'Precision': 0.5628532097096547,\n",
       "  'Recall': 0.5548192771084337},\n",
       " {'Model': 'SVM',\n",
       "  'Feature': 'BERT',\n",
       "  'Accuracy': 0.6725903614457831,\n",
       "  'F1_Score': 0.6600017429740271,\n",
       "  'Precision': 0.6608318463258314,\n",
       "  'Recall': 0.6725903614457831},\n",
       " {'Model': 'MLP',\n",
       "  'Feature': 'TF-IDF',\n",
       "  'Accuracy': 0.6078313253012049,\n",
       "  'F1_Score': 0.6055576179255558,\n",
       "  'Precision': 0.6071391892685131,\n",
       "  'Recall': 0.6078313253012049},\n",
       " {'Model': 'MLP',\n",
       "  'Feature': 'Word2Vec',\n",
       "  'Accuracy': 0.5171686746987951,\n",
       "  'F1_Score': 0.45333599161006777,\n",
       "  'Precision': 0.5671899997651424,\n",
       "  'Recall': 0.5171686746987951},\n",
       " {'Model': 'MLP',\n",
       "  'Feature': 'BERT',\n",
       "  'Accuracy': 0.6566265060240963,\n",
       "  'F1_Score': 0.615960284849616,\n",
       "  'Precision': 0.5807400679185817,\n",
       "  'Recall': 0.6566265060240963}]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Building for Naive-Bayes using TF-IDF feature extraction\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "[CV 1/3] END .........alpha=0.1, fit_prior=True;, score=0.612 total time=   0.0s\n",
      "[CV 2/3] END .........alpha=0.1, fit_prior=True;, score=0.599 total time=   0.0s\n",
      "[CV 3/3] END .........alpha=0.1, fit_prior=True;, score=0.591 total time=   0.0s\n",
      "[CV 1/3] END ........alpha=0.1, fit_prior=False;, score=0.543 total time=   0.0s\n",
      "[CV 2/3] END ........alpha=0.1, fit_prior=False;, score=0.540 total time=   0.0s\n",
      "[CV 3/3] END ........alpha=0.1, fit_prior=False;, score=0.530 total time=   0.0s\n",
      "[CV 1/3] END .........alpha=0.5, fit_prior=True;, score=0.603 total time=   0.0s\n",
      "[CV 2/3] END .........alpha=0.5, fit_prior=True;, score=0.600 total time=   0.0s\n",
      "[CV 3/3] END .........alpha=0.5, fit_prior=True;, score=0.588 total time=   0.0s\n",
      "[CV 1/3] END ........alpha=0.5, fit_prior=False;, score=0.535 total time=   0.0s\n",
      "[CV 2/3] END ........alpha=0.5, fit_prior=False;, score=0.529 total time=   0.0s\n",
      "[CV 3/3] END ........alpha=0.5, fit_prior=False;, score=0.523 total time=   0.0s\n",
      "[CV 1/3] END .........alpha=1.0, fit_prior=True;, score=0.599 total time=   0.0s\n",
      "[CV 2/3] END .........alpha=1.0, fit_prior=True;, score=0.594 total time=   0.0s\n",
      "[CV 3/3] END .........alpha=1.0, fit_prior=True;, score=0.583 total time=   0.0s\n",
      "[CV 1/3] END ........alpha=1.0, fit_prior=False;, score=0.533 total time=   0.0s\n",
      "[CV 2/3] END ........alpha=1.0, fit_prior=False;, score=0.529 total time=   0.0s\n",
      "[CV 3/3] END ........alpha=1.0, fit_prior=False;, score=0.520 total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 450x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.62      0.58      0.60      1465\n",
      "     neutral       0.57      0.06      0.11       386\n",
      "    positive       0.59      0.76      0.67      1469\n",
      "\n",
      "    accuracy                           0.60      3320\n",
      "   macro avg       0.59      0.47      0.46      3320\n",
      "weighted avg       0.60      0.60      0.57      3320\n",
      "\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "Model Building for Naive-Bayes using Word2Vec feature extraction\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "[CV 1/3] END ...........alpha=0.1, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ...........alpha=0.1, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ...........alpha=0.1, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 1/3] END ..........alpha=0.1, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ..........alpha=0.1, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ..........alpha=0.1, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 1/3] END ...........alpha=0.5, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ...........alpha=0.5, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ...........alpha=0.5, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 1/3] END ..........alpha=0.5, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ..........alpha=0.5, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ..........alpha=0.5, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 1/3] END ...........alpha=1.0, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ...........alpha=1.0, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ...........alpha=1.0, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 1/3] END ..........alpha=1.0, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ..........alpha=1.0, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ..........alpha=1.0, fit_prior=False;, score=nan total time=   0.0s\n",
      "Exception occured while building naivebayes_word2vec\n",
      "\n",
      "All the 18 fits failed.\n",
      "It is very likely that your model is misconfigured.\n",
      "You can try to debug the error by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "18 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\naive_bayes.py\", line 772, in fit\n",
      "    self._count(X, Y)\n",
      "  File \"c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\naive_bayes.py\", line 894, in _count\n",
      "    check_non_negative(X, \"MultinomialNB (input X)\")\n",
      "  File \"c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1489, in check_non_negative\n",
      "    raise ValueError(\"Negative values in data passed to %s\" % whom)\n",
      "ValueError: Negative values in data passed to MultinomialNB (input X)\n",
      "\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "Model Building for Naive-Bayes using BERT feature extraction\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "[CV 1/3] END ...........alpha=0.1, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ...........alpha=0.1, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ...........alpha=0.1, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 1/3] END ..........alpha=0.1, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ..........alpha=0.1, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ..........alpha=0.1, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 1/3] END ...........alpha=0.5, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ...........alpha=0.5, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ...........alpha=0.5, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 1/3] END ..........alpha=0.5, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ..........alpha=0.5, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ..........alpha=0.5, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 1/3] END ...........alpha=1.0, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ...........alpha=1.0, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ...........alpha=1.0, fit_prior=True;, score=nan total time=   0.0s\n",
      "[CV 1/3] END ..........alpha=1.0, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 2/3] END ..........alpha=1.0, fit_prior=False;, score=nan total time=   0.0s\n",
      "[CV 3/3] END ..........alpha=1.0, fit_prior=False;, score=nan total time=   0.0s\n",
      "Exception occured while building naivebayes_bert\n",
      "\n",
      "All the 18 fits failed.\n",
      "It is very likely that your model is misconfigured.\n",
      "You can try to debug the error by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "18 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\naive_bayes.py\", line 772, in fit\n",
      "    self._count(X, Y)\n",
      "  File \"c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\naive_bayes.py\", line 894, in _count\n",
      "    check_non_negative(X, \"MultinomialNB (input X)\")\n",
      "  File \"c:\\Users\\infor\\Documents\\AAIML\\local_repo\\venv\\pytorch_gpu\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1489, in check_non_negative\n",
      "    raise ValueError(\"Negative values in data passed to %s\" % whom)\n",
      "ValueError: Negative values in data passed to MultinomialNB (input X)\n",
      "\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "========================================================================================================================================================================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model_name in ['Naive-Bayes']:\n",
    "    for featuring_name, X_train_features, X_test_features in [\n",
    "        ('TF-IDF', X_train_tfidf, X_test_tfidf),\n",
    "        ('Word2Vec', X_train_word2vec, X_test_word2vec),\n",
    "        ('BERT', X_train_bert, X_test_bert)\n",
    "    ]:\n",
    "        print(f'Model Building for {model_name} using {featuring_name} feature extraction')\n",
    "        model_full_name = model_name + '_' + featuring_name\n",
    "        model_full_name = model_full_name.replace('-', '').lower()\n",
    "\n",
    "        try:\n",
    "            model = build_model(X_train_features, y_train, model_name)\n",
    "            \n",
    "            with open(os.path.join(pickle_folder, f'{model_full_name}.pickle'), 'wb') as fo:\n",
    "                pickle.dump(model, fo)\n",
    "            \n",
    "            y_pred = model.predict(X_test_features)\n",
    "            show_metrics(y_test, y_pred, model_full_name)\n",
    "            metrics.append({\n",
    "                'Model': model_name,\n",
    "                'Feature': featuring_name,\n",
    "                'Accuracy': accuracy_score(y_test, y_pred),\n",
    "                'F1_Score': f1_score(y_test, y_pred, average='weighted'),\n",
    "                'Precision': precision_score(y_test, y_pred, average='weighted'),\n",
    "                'Recall': recall_score(y_test, y_pred, average='weighted')\n",
    "            })\n",
    "        except Exception as e:\n",
    "            print(f'Exception occured while building {model_full_name}\\n{e}')\n",
    "        print('-' * 200)\n",
    "    print('=' * 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Model': 'SVM',\n",
       "  'Feature': 'TF-IDF',\n",
       "  'Accuracy': 0.6759036144578313,\n",
       "  'F1_Score': 0.6515372682154523,\n",
       "  'Precision': 0.6742817458280368,\n",
       "  'Recall': 0.6759036144578313},\n",
       " {'Model': 'SVM',\n",
       "  'Feature': 'Word2Vec',\n",
       "  'Accuracy': 0.5548192771084337,\n",
       "  'F1_Score': 0.5314704822871515,\n",
       "  'Precision': 0.5628532097096547,\n",
       "  'Recall': 0.5548192771084337},\n",
       " {'Model': 'SVM',\n",
       "  'Feature': 'BERT',\n",
       "  'Accuracy': 0.6725903614457831,\n",
       "  'F1_Score': 0.6600017429740271,\n",
       "  'Precision': 0.6608318463258314,\n",
       "  'Recall': 0.6725903614457831},\n",
       " {'Model': 'MLP',\n",
       "  'Feature': 'TF-IDF',\n",
       "  'Accuracy': 0.6078313253012049,\n",
       "  'F1_Score': 0.6055576179255558,\n",
       "  'Precision': 0.6071391892685131,\n",
       "  'Recall': 0.6078313253012049},\n",
       " {'Model': 'MLP',\n",
       "  'Feature': 'Word2Vec',\n",
       "  'Accuracy': 0.5171686746987951,\n",
       "  'F1_Score': 0.45333599161006777,\n",
       "  'Precision': 0.5671899997651424,\n",
       "  'Recall': 0.5171686746987951},\n",
       " {'Model': 'MLP',\n",
       "  'Feature': 'BERT',\n",
       "  'Accuracy': 0.6566265060240963,\n",
       "  'F1_Score': 0.615960284849616,\n",
       "  'Precision': 0.5807400679185817,\n",
       "  'Recall': 0.6566265060240963},\n",
       " {'Model': 'Naive-Bayes',\n",
       "  'Feature': 'TF-IDF',\n",
       "  'Accuracy': 0.6012048192771084,\n",
       "  'F1_Score': 0.5713139776892697,\n",
       "  'Precision': 0.6004637453220646,\n",
       "  'Recall': 0.6012048192771084}]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_metrics = pd.DataFrame(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Model</th>\n",
       "      <th>Feature</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1_Score</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVM</td>\n",
       "      <td>TF-IDF</td>\n",
       "      <td>0.675904</td>\n",
       "      <td>0.651537</td>\n",
       "      <td>0.674282</td>\n",
       "      <td>0.675904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM</td>\n",
       "      <td>Word2Vec</td>\n",
       "      <td>0.554819</td>\n",
       "      <td>0.531470</td>\n",
       "      <td>0.562853</td>\n",
       "      <td>0.554819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVM</td>\n",
       "      <td>BERT</td>\n",
       "      <td>0.672590</td>\n",
       "      <td>0.660002</td>\n",
       "      <td>0.660832</td>\n",
       "      <td>0.672590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MLP</td>\n",
       "      <td>TF-IDF</td>\n",
       "      <td>0.607831</td>\n",
       "      <td>0.605558</td>\n",
       "      <td>0.607139</td>\n",
       "      <td>0.607831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MLP</td>\n",
       "      <td>Word2Vec</td>\n",
       "      <td>0.517169</td>\n",
       "      <td>0.453336</td>\n",
       "      <td>0.567190</td>\n",
       "      <td>0.517169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MLP</td>\n",
       "      <td>BERT</td>\n",
       "      <td>0.656627</td>\n",
       "      <td>0.615960</td>\n",
       "      <td>0.580740</td>\n",
       "      <td>0.656627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Naive-Bayes</td>\n",
       "      <td>TF-IDF</td>\n",
       "      <td>0.601205</td>\n",
       "      <td>0.571314</td>\n",
       "      <td>0.600464</td>\n",
       "      <td>0.601205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Model   Feature  Accuracy  F1_Score  Precision    Recall\n",
       "0          SVM    TF-IDF  0.675904  0.651537   0.674282  0.675904\n",
       "1          SVM  Word2Vec  0.554819  0.531470   0.562853  0.554819\n",
       "2          SVM      BERT  0.672590  0.660002   0.660832  0.672590\n",
       "3          MLP    TF-IDF  0.607831  0.605558   0.607139  0.607831\n",
       "4          MLP  Word2Vec  0.517169  0.453336   0.567190  0.517169\n",
       "5          MLP      BERT  0.656627  0.615960   0.580740  0.656627\n",
       "6  Naive-Bayes    TF-IDF  0.601205  0.571314   0.600464  0.601205"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model_name = 'svm_bert'\n",
    "with open(os.path.join(pickle_folder, f'{final_model_name}.pickle'), 'rb') as fo:\n",
    "    final_model = pickle.load(fo)\n",
    "\n",
    "with open(os.path.join(pickle_folder, f'final_model.pickle'), 'wb') as fo:\n",
    "    pickle.dump(final_model, fo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_bert = get_bert_embeddings(X_test.to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tkinter as tk\n",
    "from tkinter import simpledialog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(pickle_folder, 'bert_tokenizer.pickle'), 'rb') as fo:\n",
    "    tokenizer = pickle.load( fo)\n",
    "\n",
    "with open(os.path.join(pickle_folder, 'bert_model.pickle'), 'rb') as fo:\n",
    "    model = pickle.load(fo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['neutral']\n"
     ]
    }
   ],
   "source": [
    "user_input = \"The gov funds billions in medicine research. How the gov don't have partial ownership on those patents blows my mind.\"\n",
    "processed_input = preprocess_text(user_input)\n",
    "X_test = get_bert_embeddings([processed_input])\n",
    "sentiment = final_model.predict(X_test)\n",
    "print(sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def submit_function():\n",
    "\n",
    "    # Get user input from the Text widget\n",
    "    # user_input = entry.get(\"1.0\", tk.END).strip()\n",
    "    #display_label.config('processing')\n",
    "    # processed_input = preprocess_text(user_input)\n",
    "    # display_label.config('vectorizing')\n",
    "    # X_test = tfidf_vectorizer.transform(processed_input)\n",
    "    # #X_test = get_word2vec_embeddings(processed_input)\n",
    "    # display_label.config('predicting')\n",
    "    # sentiment = final_model.predict(X_test)\n",
    "\n",
    "    # # Display the prediction result in the label\n",
    "    # result = f\"Sentiment: {sentiment}\"\n",
    "    # display_label.config(text=result)\n",
    "\n",
    "    user_input = entry.get(\"1.0\", tk.END).strip()\n",
    "\n",
    "    #prediction = 'Test'\n",
    "    \n",
    "    processed_input = preprocess_text(user_input)\n",
    "    X_test = get_bert_embeddings([processed_input])\n",
    "    sentiment = final_model.predict(X_test)\n",
    "    \n",
    "    result = f\"Sentiment: {sentiment[0]}\"\n",
    "    display_label.config(text=result)\n",
    "\n",
    "def clear_input():\n",
    "    entry.delete(\"1.0\", tk.END)\n",
    "    display_label.config(text=\"\")\n",
    "\n",
    "\n",
    "# Creating the main window\n",
    "root = tk.Tk()\n",
    "root.title(\"Sentiment Analysis\")\n",
    "\n",
    "# Setting the size of the window (width x height)\n",
    "root.geometry(\"500x300\")\n",
    "\n",
    "# Creating  multi-line user input box\n",
    "entry = tk.Text(root, width=60, height=5)\n",
    "entry.pack(pady=10)\n",
    "\n",
    "# Creating the clear and submit buttons\n",
    "submit_button = tk.Button(root, text=\"Submit\", command=submit_function)\n",
    "submit_button.pack(side=tk.RIGHT, padx=6, anchor=tk.N, pady=(5, 0))  # Adjusted padding\n",
    "\n",
    "clear_button = tk.Button(root, text=\"Clear\", command=clear_input)\n",
    "clear_button.pack(side=tk.RIGHT, padx=6, anchor=tk.N, pady=(5, 0))  # Adjusted padding\n",
    "\n",
    "display_label = tk.Label(root, text=\"\")\n",
    "display_label.pack(pady=70)\n",
    "\n",
    "display_label.place(relx=1.0, x=-175, rely=0.5, anchor=tk.E)\n",
    "\n",
    "# Running the Tkinter event loop\n",
    "root.mainloop()"
   ]
  }
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