Emointanger-deep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1= pd.read_csv(\"Desktop/emoint tweet/anger-ratings-0to1.train.txt\", delimiter='\\t', header=None)\n",
    "df1.columns = ['Id', 'tweet', 'emotion', 'score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Id                                              tweet emotion  score\n",
      "0    10000  How the fu*k! Who the heck! moved my fridge!.....   anger  0.938\n",
      "1    10001  So my Indian Uber driver just called someone t...   anger  0.896\n",
      "2    10002  @DPD_UK I asked for my parcel to be delivered ...   anger  0.896\n",
      "3    10003  so ef whichever butt wipe pulled the fire alar...   anger  0.896\n",
      "4    10004  Don't join @BTCare they put the phone down on ...   anger  0.896\n",
      "..     ...                                                ...     ...    ...\n",
      "852  10852   rose incense are the best thing I've ever bought   anger  0.125\n",
      "853  10853         @jaaames1993 Literally burst out laughing.   anger  0.067\n",
      "854  10854           Follow up. Follow through. Be . #success   anger  0.125\n",
      "855  10855  Wrinkles should merely hide where frown have b...   anger  0.125\n",
      "856  10856  Love the new song I can't stop thinking about ...   anger  0.083\n",
      "\n",
      "[857 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
    "import emoji\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_tweet(tweet):\n",
    "    tweet = tweet.lower()\n",
    "    tweet = re.sub(r\"http\\S+|www\\S+|https\\S+\", \"\", tweet)\n",
    "    \n",
    "    return tweet\n",
    "df1['tweet'] = df1['tweet'].apply(preprocess_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_mentions(tweet):\n",
    "    return re.sub(r'@\\w+', '', tweet)\n",
    "\n",
    "df1['tweet'] = df1['tweet'].apply(remove_mentions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    text = re.sub(r'[^\\w\\s#@]', '', text)  \n",
    "    text = re.sub(r'\\d+', '', text)  \n",
    "    text = re.sub(r'\\s+', ' ', text).strip()\n",
    "    return text\n",
    "\n",
    "df1['tweet'] = df1['tweet'].apply(clean_text)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>emotion</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>how the fuk who the heck moved my fridge shoul...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>so my indian uber driver just called someone t...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002</td>\n",
       "      <td>i asked for my parcel to be delivered to a pic...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>so ef whichever butt wipe pulled the fire alar...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>dont join they put the phone down on you talk ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</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>852</th>\n",
       "      <td>10852</td>\n",
       "      <td>rose incense are the best thing ive ever bought</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>10853</td>\n",
       "      <td>literally burst out laughing</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>10854</td>\n",
       "      <td>follow up follow through be #success</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>10855</td>\n",
       "      <td>wrinkles should merely hide where frown have b...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>10856</td>\n",
       "      <td>love the new song i cant stop thinking about y...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>857 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id                                              tweet emotion  score\n",
       "0    10000  how the fuk who the heck moved my fridge shoul...   anger  0.938\n",
       "1    10001  so my indian uber driver just called someone t...   anger  0.896\n",
       "2    10002  i asked for my parcel to be delivered to a pic...   anger  0.896\n",
       "3    10003  so ef whichever butt wipe pulled the fire alar...   anger  0.896\n",
       "4    10004  dont join they put the phone down on you talk ...   anger  0.896\n",
       "..     ...                                                ...     ...    ...\n",
       "852  10852    rose incense are the best thing ive ever bought   anger  0.125\n",
       "853  10853                       literally burst out laughing   anger  0.067\n",
       "854  10854               follow up follow through be #success   anger  0.125\n",
       "855  10855  wrinkles should merely hide where frown have b...   anger  0.125\n",
       "856  10856  love the new song i cant stop thinking about y...   anger  0.083\n",
       "\n",
       "[857 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_emoji(tweet):\n",
    "    text = emoji.demojize(tweet)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['tweet'] = df1['tweet'].apply(convert_emoji)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_tweets(tweet):\n",
    "    tokenizer = TweetTokenizer()\n",
    "    return tokenizer.tokenize(tweet)\n",
    "\n",
    "df1['tweet'] = df1['tweet'].apply(tokenize_tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    .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>Id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>emotion</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>[how, the, fuk, who, the, heck, moved, my, fri...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>[so, my, indian, uber, driver, just, called, s...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002</td>\n",
       "      <td>[i, asked, for, my, parcel, to, be, delivered,...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>[so, ef, whichever, butt, wipe, pulled, the, f...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>[dont, join, they, put, the, phone, down, on, ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</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>852</th>\n",
       "      <td>10852</td>\n",
       "      <td>[rose, incense, are, the, best, thing, ive, ev...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>10853</td>\n",
       "      <td>[literally, burst, out, laughing]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>10854</td>\n",
       "      <td>[follow, up, follow, through, be, #success]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>10855</td>\n",
       "      <td>[wrinkles, should, merely, hide, where, frown,...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>10856</td>\n",
       "      <td>[love, the, new, song, i, cant, stop, thinking...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>857 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id                                              tweet emotion  score\n",
       "0    10000  [how, the, fuk, who, the, heck, moved, my, fri...   anger  0.938\n",
       "1    10001  [so, my, indian, uber, driver, just, called, s...   anger  0.896\n",
       "2    10002  [i, asked, for, my, parcel, to, be, delivered,...   anger  0.896\n",
       "3    10003  [so, ef, whichever, butt, wipe, pulled, the, f...   anger  0.896\n",
       "4    10004  [dont, join, they, put, the, phone, down, on, ...   anger  0.896\n",
       "..     ...                                                ...     ...    ...\n",
       "852  10852  [rose, incense, are, the, best, thing, ive, ev...   anger  0.125\n",
       "853  10853                  [literally, burst, out, laughing]   anger  0.067\n",
       "854  10854        [follow, up, follow, through, be, #success]   anger  0.125\n",
       "855  10855  [wrinkles, should, merely, hide, where, frown,...   anger  0.125\n",
       "856  10856  [love, the, new, song, i, cant, stop, thinking...   anger  0.083\n",
       "\n",
       "[857 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = [\n",
    "    'a', 'an', 'and', 'are', 'as', 'at', 'be', 'by', 'for', 'from', 'has', 'he', 'in', 'is', 'it', 'its',\n",
    "    'of', 'on', 'that', 'the', 'to', 'was', 'were', 'will', 'with', 'i', 'you', 'your', 'so', 'this', 'all',\n",
    "    'am', 'or', 'but', 'if', 'my', 'me', 'we', 'us', 'our', 'we', 'up', 'down', 'out', 'just', 'how', 'why',\n",
    "    'when', 'where', 'here', 'there', 'about', 'more', 'most', 'some', 'any', 'few', 'many', 'much', 'not',\n",
    "    'only', 'other', 'same', 'such', 'no', 'nor', 'too', 'very', 'can', 'cannot', 'could', 'should', 'would',\n",
    "    'might', 'must', 'shall', 'will', 'isn', 'hasn', 'doesn', 'haven', 'didn', 'hadn', 'wasn', 'weren',\n",
    "    'wouldn', 'shouldn', 'ain', 'aren', 'ma'\n",
    "]\n",
    "\n",
    "def remove_stopwords(tokens):\n",
    "    filtered_tokens = [token for token in tokens if len(token) > 1 and token.lower() not in stop_words]\n",
    "    return filtered_tokens\n",
    "\n",
    "df1['tweet'] = df1['tweet'].apply(remove_stopwords)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>emotion</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>[fuk, who, heck, moved, fridge, knock, landlor...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>[indian, uber, driver, called, someone, word, ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002</td>\n",
       "      <td>[asked, parcel, delivered, pick, store, addres...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>[ef, whichever, butt, wipe, pulled, fire, alar...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>[dont, join, they, put, phone, talk, over, rud...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</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>852</th>\n",
       "      <td>10852</td>\n",
       "      <td>[rose, incense, best, thing, ive, ever, bought]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>10853</td>\n",
       "      <td>[literally, burst, laughing]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>10854</td>\n",
       "      <td>[follow, follow, through, #success]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>10855</td>\n",
       "      <td>[wrinkles, merely, hide, frown, have, been, ma...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>10856</td>\n",
       "      <td>[love, new, song, cant, stop, thinking]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>857 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id                                              tweet emotion  score\n",
       "0    10000  [fuk, who, heck, moved, fridge, knock, landlor...   anger  0.938\n",
       "1    10001  [indian, uber, driver, called, someone, word, ...   anger  0.896\n",
       "2    10002  [asked, parcel, delivered, pick, store, addres...   anger  0.896\n",
       "3    10003  [ef, whichever, butt, wipe, pulled, fire, alar...   anger  0.896\n",
       "4    10004  [dont, join, they, put, phone, talk, over, rud...   anger  0.896\n",
       "..     ...                                                ...     ...    ...\n",
       "852  10852    [rose, incense, best, thing, ive, ever, bought]   anger  0.125\n",
       "853  10853                       [literally, burst, laughing]   anger  0.067\n",
       "854  10854                [follow, follow, through, #success]   anger  0.125\n",
       "855  10855  [wrinkles, merely, hide, frown, have, been, ma...   anger  0.125\n",
       "856  10856            [love, new, song, cant, stop, thinking]   anger  0.083\n",
       "\n",
       "[857 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import BertModel, BertTokenizer\n",
    "\n",
    "model_name='bert-base-uncased'\n",
    "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
    "model = BertModel.from_pretrained(model_name)\n",
    "\n",
    "def extract_features(tweet):\n",
    "    tokenized_text = ' '.join(tweet)\n",
    "    input_ids = torch.tensor(tokenizer.encode(tokenized_text, add_special_tokens=True)).unsqueeze(0)\n",
    "    with torch.no_grad():\n",
    "        outputs = model(input_ids)\n",
    "    last_hidden_states = outputs.last_hidden_state\n",
    "    features = last_hidden_states.squeeze(0).numpy()\n",
    "    \n",
    "    return features\n",
    "\n",
    "df1['features'] = df1['tweet'].apply(extract_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>emotion</th>\n",
       "      <th>score</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>[fuk, who, heck, moved, fridge, knock, landlor...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.938</td>\n",
       "      <td>[[-0.048211165, 0.55411077, 0.17288177, -0.387...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>[indian, uber, driver, called, someone, word, ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "      <td>[[-0.114754006, 0.2242083, 0.09101931, -0.1088...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002</td>\n",
       "      <td>[asked, parcel, delivered, pick, store, addres...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "      <td>[[-0.3445066, -0.026175018, 0.0032749637, -0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>[ef, whichever, butt, wipe, pulled, fire, alar...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "      <td>[[-0.08527253, 0.29024652, 0.19312719, -0.0649...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>[dont, join, they, put, phone, talk, over, rud...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "      <td>[[-0.19458191, 0.13260978, 0.12249434, -0.2312...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>10852</td>\n",
       "      <td>[rose, incense, best, thing, ive, ever, bought]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "      <td>[[-0.5573598, 0.37391967, -0.46787184, 0.10334...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>10853</td>\n",
       "      <td>[literally, burst, laughing]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.067</td>\n",
       "      <td>[[-0.14927039, 0.18389827, 0.047589768, -0.044...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>10854</td>\n",
       "      <td>[follow, follow, through, #success]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "      <td>[[-0.21155725, 0.059810657, 0.18363638, 0.0126...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>10855</td>\n",
       "      <td>[wrinkles, merely, hide, frown, have, been, ma...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.125</td>\n",
       "      <td>[[0.040475816, 0.2076654, 0.0045883185, -0.075...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>10856</td>\n",
       "      <td>[love, new, song, cant, stop, thinking]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.083</td>\n",
       "      <td>[[-0.052609857, -0.124588385, -0.080250956, 0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>857 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id                                              tweet emotion  score  \\\n",
       "0    10000  [fuk, who, heck, moved, fridge, knock, landlor...   anger  0.938   \n",
       "1    10001  [indian, uber, driver, called, someone, word, ...   anger  0.896   \n",
       "2    10002  [asked, parcel, delivered, pick, store, addres...   anger  0.896   \n",
       "3    10003  [ef, whichever, butt, wipe, pulled, fire, alar...   anger  0.896   \n",
       "4    10004  [dont, join, they, put, phone, talk, over, rud...   anger  0.896   \n",
       "..     ...                                                ...     ...    ...   \n",
       "852  10852    [rose, incense, best, thing, ive, ever, bought]   anger  0.125   \n",
       "853  10853                       [literally, burst, laughing]   anger  0.067   \n",
       "854  10854                [follow, follow, through, #success]   anger  0.125   \n",
       "855  10855  [wrinkles, merely, hide, frown, have, been, ma...   anger  0.125   \n",
       "856  10856            [love, new, song, cant, stop, thinking]   anger  0.083   \n",
       "\n",
       "                                              features  \n",
       "0    [[-0.048211165, 0.55411077, 0.17288177, -0.387...  \n",
       "1    [[-0.114754006, 0.2242083, 0.09101931, -0.1088...  \n",
       "2    [[-0.3445066, -0.026175018, 0.0032749637, -0.1...  \n",
       "3    [[-0.08527253, 0.29024652, 0.19312719, -0.0649...  \n",
       "4    [[-0.19458191, 0.13260978, 0.12249434, -0.2312...  \n",
       "..                                                 ...  \n",
       "852  [[-0.5573598, 0.37391967, -0.46787184, 0.10334...  \n",
       "853  [[-0.14927039, 0.18389827, 0.047589768, -0.044...  \n",
       "854  [[-0.21155725, 0.059810657, 0.18363638, 0.0126...  \n",
       "855  [[0.040475816, 0.2076654, 0.0045883185, -0.075...  \n",
       "856  [[-0.052609857, -0.124588385, -0.080250956, 0....  \n",
       "\n",
       "[857 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 768)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.features[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>emotion</th>\n",
       "      <th>score</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>[fuk, who, heck, moved, fridge, knock, landlor...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.938</td>\n",
       "      <td>[[-0.048211165, 0.55411077, 0.17288177, -0.387...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>[indian, uber, driver, called, someone, word, ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "      <td>[[-0.114754006, 0.2242083, 0.09101931, -0.1088...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002</td>\n",
       "      <td>[asked, parcel, delivered, pick, store, addres...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "      <td>[[-0.3445066, -0.026175018, 0.0032749637, -0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>[ef, whichever, butt, wipe, pulled, fire, alar...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "      <td>[[-0.08527253, 0.29024652, 0.19312719, -0.0649...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>[dont, join, they, put, phone, talk, over, rud...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "      <td>[[-0.19458191, 0.13260978, 0.12249434, -0.2312...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10005</td>\n",
       "      <td>[blood, boiling]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.875</td>\n",
       "      <td>[[-0.33847663, 0.43582797, -0.3691528, -0.0296...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10006</td>\n",
       "      <td>[youve, still, got, whole, season, wentworth, ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.875</td>\n",
       "      <td>[[-0.2145357, 0.041926354, 0.31682923, -0.3384...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10007</td>\n",
       "      <td>[does, tracking, show, equipment, delivered, w...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.875</td>\n",
       "      <td>[[-0.17867346, 0.54314536, 0.12850264, -0.2737...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10008</td>\n",
       "      <td>[legit, furious, him, people, fucking, idiots]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.875</td>\n",
       "      <td>[[0.12987164, 0.45778227, 0.020973768, -0.0331...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10009</td>\n",
       "      <td>[suppose, work, do, wtf, dude, thanks, pissing...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.875</td>\n",
       "      <td>[[0.11742496, 0.36254063, 0.1227451, -0.129079...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id                                              tweet emotion  score  \\\n",
       "0  10000  [fuk, who, heck, moved, fridge, knock, landlor...   anger  0.938   \n",
       "1  10001  [indian, uber, driver, called, someone, word, ...   anger  0.896   \n",
       "2  10002  [asked, parcel, delivered, pick, store, addres...   anger  0.896   \n",
       "3  10003  [ef, whichever, butt, wipe, pulled, fire, alar...   anger  0.896   \n",
       "4  10004  [dont, join, they, put, phone, talk, over, rud...   anger  0.896   \n",
       "5  10005                                   [blood, boiling]   anger  0.875   \n",
       "6  10006  [youve, still, got, whole, season, wentworth, ...   anger  0.875   \n",
       "7  10007  [does, tracking, show, equipment, delivered, w...   anger  0.875   \n",
       "8  10008     [legit, furious, him, people, fucking, idiots]   anger  0.875   \n",
       "9  10009  [suppose, work, do, wtf, dude, thanks, pissing...   anger  0.875   \n",
       "\n",
       "                                            features  \n",
       "0  [[-0.048211165, 0.55411077, 0.17288177, -0.387...  \n",
       "1  [[-0.114754006, 0.2242083, 0.09101931, -0.1088...  \n",
       "2  [[-0.3445066, -0.026175018, 0.0032749637, -0.1...  \n",
       "3  [[-0.08527253, 0.29024652, 0.19312719, -0.0649...  \n",
       "4  [[-0.19458191, 0.13260978, 0.12249434, -0.2312...  \n",
       "5  [[-0.33847663, 0.43582797, -0.3691528, -0.0296...  \n",
       "6  [[-0.2145357, 0.041926354, 0.31682923, -0.3384...  \n",
       "7  [[-0.17867346, 0.54314536, 0.12850264, -0.2737...  \n",
       "8  [[0.12987164, 0.45778227, 0.020973768, -0.0331...  \n",
       "9  [[0.11742496, 0.36254063, 0.1227451, -0.129079...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "features = df1['features'].tolist()\n",
    "padded_features = pad_sequences(features, padding='post')\n",
    "padded_df = df1.copy()\n",
    "padded_df['features'] = padded_features.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input feature shape: (857, 40, 768)\n"
     ]
    }
   ],
   "source": [
    "X = np.stack(padded_df['features'])\n",
    "print('Input feature shape:', X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (857, 40, 768)\n",
      "Output shape: (857,)\n"
     ]
    }
   ],
   "source": [
    "y = np.array(padded_df['score']) \n",
    "print(\"Input shape:\", X.shape)\n",
    "print(\"Output shape:\", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.reshape(X, (857, 40 * 768)) \n",
    "y = np.reshape(y, (857,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(857, 30720)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df1['score'].copy()\n",
    "\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(857, 30720)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_sequence_length = len(max(X, key=len))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 8096)              248717216 \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 8096)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4048)              32776656  \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 4048)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 2024)              8195176   \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 2024)              0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1012)              2049300   \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 1012)              0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 506)               512578    \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 506)               0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 128)               64896     \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 32)                4128      \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 292,319,983\n",
      "Trainable params: 292,319,983\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras import regularizers\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(8096, input_shape=(30720,), activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(4048, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(2024, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(1012, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(506, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='mean_squared_error', patience=1, mode='min')\n",
    "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mean_squared_error'])\n",
    "\n",
    "model.summary()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(857, 30720)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['tweet'] = df1['tweet'].apply(lambda tokens: ' '.join(tokens))\n",
    "array = df1['tweet'].values\n",
    "tensor = tf.convert_to_tensor(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(857, 30720)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(857,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "32/32 [==============================] - 137s 4s/step - loss: 55.2204 - mean_squared_error: 1.2691\n",
      "Epoch 2/10\n",
      "32/32 [==============================] - 120s 4s/step - loss: 20.0903 - mean_squared_error: 0.0463\n",
      "Epoch 3/10\n",
      "32/32 [==============================] - 123s 4s/step - loss: 12.6189 - mean_squared_error: 0.0285\n",
      "Epoch 4/10\n",
      "32/32 [==============================] - 127s 4s/step - loss: 9.0873 - mean_squared_error: 0.0252\n",
      "Epoch 5/10\n",
      "32/32 [==============================] - 128s 4s/step - loss: 6.9675 - mean_squared_error: 0.0252\n",
      "Epoch 6/10\n",
      "32/32 [==============================] - 125s 4s/step - loss: 5.5417 - mean_squared_error: 0.0214\n",
      "Epoch 7/10\n",
      "32/32 [==============================] - 129s 4s/step - loss: 4.5301 - mean_squared_error: 0.0223\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X, Y, batch_size=27, epochs=10, shuffle=True, verbose=1, callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('emointjoy.h5','/Home')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "loaded_model = load_model('emointjoy.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold= pd.read_csv(\"Desktop/emoint tweet/anger-ratings-0to1.dev.gold.txt\", delimiter='\\t', header=None)\n",
    "gold.columns = ['Id', 'tweet', 'emotion', 'score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>emotion</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10857</td>\n",
       "      <td>@ZubairSabirPTI  pls dont insult the word 'Molna'</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10858</td>\n",
       "      <td>@ArcticFantasy I would have almost took offens...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10859</td>\n",
       "      <td>@IllinoisLoyalty that Rutgers game was an abom...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10860</td>\n",
       "      <td>@CozanGaming that's what lisa asked before she...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10861</td>\n",
       "      <td>Sometimes I get mad over something so minuscul...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.708</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>79</th>\n",
       "      <td>10936</td>\n",
       "      <td>@Jen_ny69 People will always get offended ever...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>10937</td>\n",
       "      <td>@gayla_weeks1 I try not to let my anger seep i...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>10938</td>\n",
       "      <td>I hope my hustle don't offend nobody</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>10939</td>\n",
       "      <td>Just watched Django Unchained, Other people ma...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>10940</td>\n",
       "      <td>Lol little things like that make me so angry x</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id                                              tweet emotion  score\n",
       "0   10857  @ZubairSabirPTI  pls dont insult the word 'Molna'   anger  0.479\n",
       "1   10858  @ArcticFantasy I would have almost took offens...   anger  0.458\n",
       "2   10859  @IllinoisLoyalty that Rutgers game was an abom...   anger  0.562\n",
       "3   10860  @CozanGaming that's what lisa asked before she...   anger  0.500\n",
       "4   10861  Sometimes I get mad over something so minuscul...   anger  0.708\n",
       "..    ...                                                ...     ...    ...\n",
       "79  10936  @Jen_ny69 People will always get offended ever...   anger  0.562\n",
       "80  10937  @gayla_weeks1 I try not to let my anger seep i...   anger  0.625\n",
       "81  10938               I hope my hustle don't offend nobody   anger  0.292\n",
       "82  10939  Just watched Django Unchained, Other people ma...   anger  0.229\n",
       "83  10940     Lol little things like that make me so angry x   anger  0.604\n",
       "\n",
       "[84 rows x 4 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def preprocess_tweet(tweet):\n",
    "    tweet = tweet.lower()\n",
    "    tweet = re.sub(r\"http\\S+|www\\S+|https\\S+\", \"\", tweet)\n",
    "    \n",
    "    return tweet\n",
    "gold['tweet'] = gold['tweet'].apply(preprocess_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>anger</td>\n",
       "      <td>0.458</td>\n",
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       "      <th>2</th>\n",
       "      <td>10859</td>\n",
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       "      <td>0.562</td>\n",
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       "      <th>79</th>\n",
       "      <td>10936</td>\n",
       "      <td>@jen_ny69 people will always get offended ever...</td>\n",
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       "      <td>0.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>10937</td>\n",
       "      <td>@gayla_weeks1 i try not to let my anger seep i...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>10938</td>\n",
       "      <td>i hope my hustle don't offend nobody</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>10939</td>\n",
       "      <td>just watched django unchained, other people ma...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>10940</td>\n",
       "      <td>lol little things like that make me so angry x</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id                                              tweet emotion  score\n",
       "0   10857  @zubairsabirpti  pls dont insult the word 'molna'   anger  0.479\n",
       "1   10858  @arcticfantasy i would have almost took offens...   anger  0.458\n",
       "2   10859  @illinoisloyalty that rutgers game was an abom...   anger  0.562\n",
       "3   10860  @cozangaming that's what lisa asked before she...   anger  0.500\n",
       "4   10861  sometimes i get mad over something so minuscul...   anger  0.708\n",
       "..    ...                                                ...     ...    ...\n",
       "79  10936  @jen_ny69 people will always get offended ever...   anger  0.562\n",
       "80  10937  @gayla_weeks1 i try not to let my anger seep i...   anger  0.625\n",
       "81  10938               i hope my hustle don't offend nobody   anger  0.292\n",
       "82  10939  just watched django unchained, other people ma...   anger  0.229\n",
       "83  10940     lol little things like that make me so angry x   anger  0.604\n",
       "\n",
       "[84 rows x 4 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.479</td>\n",
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       "      <th>1</th>\n",
       "      <td>10858</td>\n",
       "      <td>i would have almost took offense to this if i...</td>\n",
       "      <td>anger</td>\n",
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       "      <th>2</th>\n",
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       "      <td>that rutgers game was an abomination. an affr...</td>\n",
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       "      <th>4</th>\n",
       "      <td>10861</td>\n",
       "      <td>sometimes i get mad over something so minuscul...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.708</td>\n",
       "    </tr>\n",
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       "      <th>79</th>\n",
       "      <td>10936</td>\n",
       "      <td>people will always get offended everyone's si...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>10937</td>\n",
       "      <td>i try not to let my anger seep into reviews, ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>10938</td>\n",
       "      <td>i hope my hustle don't offend nobody</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>10939</td>\n",
       "      <td>just watched django unchained, other people ma...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>10940</td>\n",
       "      <td>lol little things like that make me so angry x</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id                                              tweet emotion  score\n",
       "0   10857                   pls dont insult the word 'molna'   anger  0.479\n",
       "1   10858   i would have almost took offense to this if i...   anger  0.458\n",
       "2   10859   that rutgers game was an abomination. an affr...   anger  0.562\n",
       "3   10860   that's what lisa asked before she started rag...   anger  0.500\n",
       "4   10861  sometimes i get mad over something so minuscul...   anger  0.708\n",
       "..    ...                                                ...     ...    ...\n",
       "79  10936   people will always get offended everyone's si...   anger  0.562\n",
       "80  10937   i try not to let my anger seep into reviews, ...   anger  0.625\n",
       "81  10938               i hope my hustle don't offend nobody   anger  0.292\n",
       "82  10939  just watched django unchained, other people ma...   anger  0.229\n",
       "83  10940     lol little things like that make me so angry x   anger  0.604\n",
       "\n",
       "[84 rows x 4 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_mentions(tweet):\n",
    "    return re.sub(r'@\\w+', '', tweet)\n",
    "\n",
    "gold['tweet'] = gold['tweet'].apply(remove_mentions)\n",
    "gold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_gold(gold):\n",
    "    gold  = re.sub(r'[^\\w\\s#@]', '', gold ) \n",
    "    gold  = re.sub(r'\\d+', '', gold)  \n",
    "    gold  = re.sub(r'\\s+', ' ', gold).strip()\n",
    "    return gold\n",
    "\n",
    "gold['tweet'] = gold['tweet'].apply(clean_gold)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>emotion</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10857</td>\n",
       "      <td>pls dont insult the word molna</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10858</td>\n",
       "      <td>i would have almost took offense to this if i ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10859</td>\n",
       "      <td>that rutgers game was an abomination an affron...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10860</td>\n",
       "      <td>thats what lisa asked before she started ragin...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10861</td>\n",
       "      <td>sometimes i get mad over something so minuscul...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.708</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>79</th>\n",
       "      <td>10936</td>\n",
       "      <td>people will always get offended everyones situ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>10937</td>\n",
       "      <td>i try not to let my anger seep into reviews bu...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>10938</td>\n",
       "      <td>i hope my hustle dont offend nobody</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>10939</td>\n",
       "      <td>just watched django unchained other people may...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>10940</td>\n",
       "      <td>lol little things like that make me so angry x</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id                                              tweet emotion  score\n",
       "0   10857                     pls dont insult the word molna   anger  0.479\n",
       "1   10858  i would have almost took offense to this if i ...   anger  0.458\n",
       "2   10859  that rutgers game was an abomination an affron...   anger  0.562\n",
       "3   10860  thats what lisa asked before she started ragin...   anger  0.500\n",
       "4   10861  sometimes i get mad over something so minuscul...   anger  0.708\n",
       "..    ...                                                ...     ...    ...\n",
       "79  10936  people will always get offended everyones situ...   anger  0.562\n",
       "80  10937  i try not to let my anger seep into reviews bu...   anger  0.625\n",
       "81  10938                i hope my hustle dont offend nobody   anger  0.292\n",
       "82  10939  just watched django unchained other people may...   anger  0.229\n",
       "83  10940     lol little things like that make me so angry x   anger  0.604\n",
       "\n",
       "[84 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_emoji(tweet):\n",
    "    gold = emoji.demojize(tweet)\n",
    "    return gold\n",
    "\n",
    "gold['tweet'] = gold['tweet'].apply(convert_emoji)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_tweets(tweet):\n",
    "    tokenizer = TweetTokenizer()\n",
    "    return tokenizer.tokenize(tweet)\n",
    "\n",
    "gold['tweet'] = gold['tweet'].apply(tokenize_tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>emotion</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10857</td>\n",
       "      <td>[pls, dont, insult, the, word, molna]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10858</td>\n",
       "      <td>[i, would, have, almost, took, offense, to, th...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10859</td>\n",
       "      <td>[that, rutgers, game, was, an, abomination, an...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10860</td>\n",
       "      <td>[thats, what, lisa, asked, before, she, starte...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10861</td>\n",
       "      <td>[sometimes, i, get, mad, over, something, so, ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.708</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>79</th>\n",
       "      <td>10936</td>\n",
       "      <td>[people, will, always, get, offended, everyone...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>10937</td>\n",
       "      <td>[i, try, not, to, let, my, anger, seep, into, ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>10938</td>\n",
       "      <td>[i, hope, my, hustle, dont, offend, nobody]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>10939</td>\n",
       "      <td>[just, watched, django, unchained, other, peop...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>10940</td>\n",
       "      <td>[lol, little, things, like, that, make, me, so...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id                                              tweet emotion  score\n",
       "0   10857              [pls, dont, insult, the, word, molna]   anger  0.479\n",
       "1   10858  [i, would, have, almost, took, offense, to, th...   anger  0.458\n",
       "2   10859  [that, rutgers, game, was, an, abomination, an...   anger  0.562\n",
       "3   10860  [thats, what, lisa, asked, before, she, starte...   anger  0.500\n",
       "4   10861  [sometimes, i, get, mad, over, something, so, ...   anger  0.708\n",
       "..    ...                                                ...     ...    ...\n",
       "79  10936  [people, will, always, get, offended, everyone...   anger  0.562\n",
       "80  10937  [i, try, not, to, let, my, anger, seep, into, ...   anger  0.625\n",
       "81  10938        [i, hope, my, hustle, dont, offend, nobody]   anger  0.292\n",
       "82  10939  [just, watched, django, unchained, other, peop...   anger  0.229\n",
       "83  10940  [lol, little, things, like, that, make, me, so...   anger  0.604\n",
       "\n",
       "[84 rows x 4 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = [\n",
    "    'a', 'an', 'and', 'are', 'as', 'at', 'be', 'by', 'for', 'from', 'has', 'he', 'in', 'is', 'it', 'its',\n",
    "    'of', 'on', 'that', 'the', 'to', 'was', 'were', 'will', 'with', 'i', 'you', 'your', 'so', 'this', 'all',\n",
    "    'am', 'or', 'but', 'if', 'my', 'me', 'we', 'us', 'our', 'we', 'up', 'down', 'out', 'just', 'how', 'why',\n",
    "    'when', 'where', 'here', 'there', 'about', 'more', 'most', 'some', 'any', 'few', 'many', 'much', 'not',\n",
    "    'only', 'other', 'same', 'such', 'no', 'nor', 'too', 'very', 'can', 'cannot', 'could', 'should', 'would',\n",
    "    'might', 'must', 'shall', 'will', 'isn', 'hasn', 'doesn', 'haven', 'didn', 'hadn', 'wasn', 'weren',\n",
    "    'wouldn', 'shouldn', 'ain', 'aren', 'ma'\n",
    "]\n",
    "\n",
    "\n",
    "def remove_stopwords(tokens):\n",
    "    filtered_tokens = [token for token in tokens if len(token) > 1 and token.lower() not in stop_words]\n",
    "    return filtered_tokens\n",
    "\n",
    "gold['tweet'] = gold['tweet'].apply(remove_stopwords)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>emotion</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10857</td>\n",
       "      <td>[pls, dont, insult, word, molna]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10858</td>\n",
       "      <td>[have, almost, took, offense, actually, snapped]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10859</td>\n",
       "      <td>[rutgers, game, abomination, affront, god, man...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10860</td>\n",
       "      <td>[thats, what, lisa, asked, before, she, starte...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10861</td>\n",
       "      <td>[sometimes, get, mad, over, something, minuscu...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.708</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>79</th>\n",
       "      <td>10936</td>\n",
       "      <td>[people, always, get, offended, everyones, sit...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>10937</td>\n",
       "      <td>[try, let, anger, seep, into, reviews, resent,...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>10938</td>\n",
       "      <td>[hope, hustle, dont, offend, nobody]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>10939</td>\n",
       "      <td>[watched, django, unchained, people, may, frow...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>10940</td>\n",
       "      <td>[lol, little, things, like, make, angry]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id                                              tweet emotion  score\n",
       "0   10857                   [pls, dont, insult, word, molna]   anger  0.479\n",
       "1   10858   [have, almost, took, offense, actually, snapped]   anger  0.458\n",
       "2   10859  [rutgers, game, abomination, affront, god, man...   anger  0.562\n",
       "3   10860  [thats, what, lisa, asked, before, she, starte...   anger  0.500\n",
       "4   10861  [sometimes, get, mad, over, something, minuscu...   anger  0.708\n",
       "..    ...                                                ...     ...    ...\n",
       "79  10936  [people, always, get, offended, everyones, sit...   anger  0.562\n",
       "80  10937  [try, let, anger, seep, into, reviews, resent,...   anger  0.625\n",
       "81  10938               [hope, hustle, dont, offend, nobody]   anger  0.292\n",
       "82  10939  [watched, django, unchained, people, may, frow...   anger  0.229\n",
       "83  10940           [lol, little, things, like, make, angry]   anger  0.604\n",
       "\n",
       "[84 rows x 4 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import BertModel, BertTokenizer\n",
    "\n",
    "\n",
    "model_name = 'bert-base-uncased'\n",
    "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
    "model = BertModel.from_pretrained(model_name)\n",
    "\n",
    "def extract_features(tweet):\n",
    "    tokenized_text = ' '.join(tweet)\n",
    "    input_ids = torch.tensor(tokenizer.encode(tokenized_text, add_special_tokens=True)).unsqueeze(0)\n",
    "    with torch.no_grad():\n",
    "        outputs = model(input_ids)\n",
    "    last_hidden_states = outputs.last_hidden_state\n",
    "    features = last_hidden_states.squeeze(0).numpy()\n",
    "    return features\n",
    "\n",
    "\n",
    "gold['features'] = gold['tweet'].apply(extract_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 768)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold[\"features\"][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "features =gold['features'].tolist()\n",
    "\n",
    "padded_features = pad_sequences(features, padding='post')\n",
    "\n",
    "padded_df = gold.copy()\n",
    "padded_df['features'] = padded_features.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input feature shape: (84, 43, 768)\n"
     ]
    }
   ],
   "source": [
    "X_gold= np.stack(padded_df['features'])\n",
    "print('Input feature shape:', X_gold.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "max_sequence_length = 40\n",
    "\n",
    "padded_gold_data = pad_sequences(X_gold, maxlen=max_sequence_length, padding='post', truncating='post')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.reshape(padded_gold_data, (84, 40 * 768))  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_features = np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84, 30720)\n"
     ]
    }
   ],
   "source": [
    "print(gold_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 121ms/step\n"
     ]
    }
   ],
   "source": [
    "\n",
    "predictions = loaded_model.predict(gold_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.42983025]\n",
      " [0.39067608]\n",
      " [0.4761458 ]\n",
      " [0.41589832]\n",
      " [0.45870632]\n",
      " [0.4723348 ]\n",
      " [0.47835708]\n",
      " [0.5010797 ]\n",
      " [0.49646306]\n",
      " [0.45078397]\n",
      " [0.47035313]\n",
      " [0.4992628 ]\n",
      " [0.48040706]\n",
      " [0.5068386 ]\n",
      " [0.4588393 ]\n",
      " [0.44625086]\n",
      " [0.47188455]\n",
      " [0.4091395 ]\n",
      " [0.37288994]\n",
      " [0.41724902]\n",
      " [0.42723107]\n",
      " [0.4294554 ]\n",
      " [0.3768196 ]\n",
      " [0.44328767]\n",
      " [0.42854536]\n",
      " [0.48328167]\n",
      " [0.43076688]\n",
      " [0.40173358]\n",
      " [0.45567417]\n",
      " [0.4664476 ]\n",
      " [0.3817675 ]\n",
      " [0.39712238]\n",
      " [0.41243452]\n",
      " [0.3542381 ]\n",
      " [0.43463552]\n",
      " [0.4955246 ]\n",
      " [0.45220208]\n",
      " [0.42163926]\n",
      " [0.40661412]\n",
      " [0.39225864]\n",
      " [0.39914083]\n",
      " [0.4055798 ]\n",
      " [0.4092601 ]\n",
      " [0.4164049 ]\n",
      " [0.457775  ]\n",
      " [0.45655042]\n",
      " [0.40969992]\n",
      " [0.40172863]\n",
      " [0.47126442]\n",
      " [0.42962474]\n",
      " [0.42130685]\n",
      " [0.42085493]\n",
      " [0.38543588]\n",
      " [0.48216414]\n",
      " [0.44782507]\n",
      " [0.46181726]\n",
      " [0.37941873]\n",
      " [0.42436415]\n",
      " [0.41671836]\n",
      " [0.48221815]\n",
      " [0.41491187]\n",
      " [0.4152171 ]\n",
      " [0.40396369]\n",
      " [0.40331036]\n",
      " [0.3783309 ]\n",
      " [0.43287504]\n",
      " [0.43131328]\n",
      " [0.38366055]\n",
      " [0.38366055]\n",
      " [0.43132257]\n",
      " [0.3792795 ]\n",
      " [0.3913362 ]\n",
      " [0.41876227]\n",
      " [0.4180056 ]\n",
      " [0.37319607]\n",
      " [0.30444986]\n",
      " [0.44442767]\n",
      " [0.41896302]\n",
      " [0.4435563 ]\n",
      " [0.45634675]\n",
      " [0.4392377 ]\n",
      " [0.39077467]\n",
      " [0.42418593]\n",
      " [0.40567714]]\n"
     ]
    }
   ],
   "source": [
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold['prediction']=pd.DataFrame(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>emotion</th>\n",
       "      <th>score</th>\n",
       "      <th>features</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10857</td>\n",
       "      <td>[pls, dont, insult, word, molna]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.479</td>\n",
       "      <td>[[-0.16410866, 0.59082836, -0.29882964, -0.219...</td>\n",
       "      <td>0.429830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10858</td>\n",
       "      <td>[have, almost, took, offense, actually, snapped]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.458</td>\n",
       "      <td>[[0.0656415, 0.116881676, 0.07354636, 0.036941...</td>\n",
       "      <td>0.390676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10859</td>\n",
       "      <td>[rutgers, game, abomination, affront, god, man...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.562</td>\n",
       "      <td>[[-0.053360425, 0.269789, 0.00304747, -0.07536...</td>\n",
       "      <td>0.476146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10860</td>\n",
       "      <td>[thats, what, lisa, asked, before, she, starte...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.500</td>\n",
       "      <td>[[-0.1695536, 0.36146763, 0.1704143, -0.027060...</td>\n",
       "      <td>0.415898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10861</td>\n",
       "      <td>[sometimes, get, mad, over, something, minuscu...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.708</td>\n",
       "      <td>[[0.32172096, -0.21224442, -0.00801837, -0.002...</td>\n",
       "      <td>0.458706</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>10936</td>\n",
       "      <td>[people, always, get, offended, everyones, sit...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.562</td>\n",
       "      <td>[[0.20103475, 0.36392444, -0.038629428, 0.0799...</td>\n",
       "      <td>0.456347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>10937</td>\n",
       "      <td>[try, let, anger, seep, into, reviews, resent,...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.625</td>\n",
       "      <td>[[0.15884677, -0.2910138, -0.052284643, 0.0484...</td>\n",
       "      <td>0.439238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>10938</td>\n",
       "      <td>[hope, hustle, dont, offend, nobody]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.292</td>\n",
       "      <td>[[-0.023534462, 0.50190735, -0.4802528, -0.017...</td>\n",
       "      <td>0.390775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>10939</td>\n",
       "      <td>[watched, django, unchained, people, may, frow...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.229</td>\n",
       "      <td>[[0.15952887, 0.13285294, 0.20451696, 0.080129...</td>\n",
       "      <td>0.424186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>10940</td>\n",
       "      <td>[lol, little, things, like, make, angry]</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.604</td>\n",
       "      <td>[[-0.13832283, 0.4406555, -0.28989294, 0.06961...</td>\n",
       "      <td>0.405677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id                                              tweet emotion  score  \\\n",
       "0   10857                   [pls, dont, insult, word, molna]   anger  0.479   \n",
       "1   10858   [have, almost, took, offense, actually, snapped]   anger  0.458   \n",
       "2   10859  [rutgers, game, abomination, affront, god, man...   anger  0.562   \n",
       "3   10860  [thats, what, lisa, asked, before, she, starte...   anger  0.500   \n",
       "4   10861  [sometimes, get, mad, over, something, minuscu...   anger  0.708   \n",
       "..    ...                                                ...     ...    ...   \n",
       "79  10936  [people, always, get, offended, everyones, sit...   anger  0.562   \n",
       "80  10937  [try, let, anger, seep, into, reviews, resent,...   anger  0.625   \n",
       "81  10938               [hope, hustle, dont, offend, nobody]   anger  0.292   \n",
       "82  10939  [watched, django, unchained, people, may, frow...   anger  0.229   \n",
       "83  10940           [lol, little, things, like, make, angry]   anger  0.604   \n",
       "\n",
       "                                             features  prediction  \n",
       "0   [[-0.16410866, 0.59082836, -0.29882964, -0.219...    0.429830  \n",
       "1   [[0.0656415, 0.116881676, 0.07354636, 0.036941...    0.390676  \n",
       "2   [[-0.053360425, 0.269789, 0.00304747, -0.07536...    0.476146  \n",
       "3   [[-0.1695536, 0.36146763, 0.1704143, -0.027060...    0.415898  \n",
       "4   [[0.32172096, -0.21224442, -0.00801837, -0.002...    0.458706  \n",
       "..                                                ...         ...  \n",
       "79  [[0.20103475, 0.36392444, -0.038629428, 0.0799...    0.456347  \n",
       "80  [[0.15884677, -0.2910138, -0.052284643, 0.0484...    0.439238  \n",
       "81  [[-0.023534462, 0.50190735, -0.4802528, -0.017...    0.390775  \n",
       "82  [[0.15952887, 0.13285294, 0.20451696, 0.080129...    0.424186  \n",
       "83  [[-0.13832283, 0.4406555, -0.28989294, 0.06961...    0.405677  \n",
       "\n",
       "[84 rows x 6 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean square Error: 0.027248999115929234\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(gold['score'], gold['prediction'])\n",
    "print(\"Mean square Error:\", mse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "test= pd.read_csv(\"Desktop/emoint tweet/anger-ratings-0to1.test.target.txt\", delimiter='\\t', header=None)\n",
    "test.columns = ['Id', 'tweet', 'emotion', 'score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_tweet(tweet):\n",
    "    tweet = tweet.lower()\n",
    "    tweet = re.sub(r\"http\\S+|www\\S+|https\\S+\", \"\", tweet)\n",
    "    \n",
    "    return tweet\n",
    "test['tweet'] = test['tweet'].apply(preprocess_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>emotion</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10941</td>\n",
       "      <td>at the point today where if someone says somet...</td>\n",
       "      <td>anger</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10942</td>\n",
       "      <td>it's game day!!!!      t minus 14:30  #relen...</td>\n",
       "      <td>anger</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10943</td>\n",
       "      <td>this game has pissed me off more than any othe...</td>\n",
       "      <td>anger</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10944</td>\n",
       "      <td>i've just found out it's candice and not cand...</td>\n",
       "      <td>anger</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10945</td>\n",
       "      <td>if he can't come to my mum'a 60th after 25...</td>\n",
       "      <td>anger</td>\n",
       "      <td>NONE</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>755</th>\n",
       "      <td>11696</td>\n",
       "      <td>what if the supposed animosity is all bullshi...</td>\n",
       "      <td>anger</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>11697</td>\n",
       "      <td>will byu's offense score 24+ vs wvu?</td>\n",
       "      <td>anger</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>11698</td>\n",
       "      <td>id love 2 c gyimah in action but his coach is ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>11699</td>\n",
       "      <td>forgiving means operating with god's spirit &amp;a...</td>\n",
       "      <td>anger</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>11700</td>\n",
       "      <td>i've got a lot of tokens saved up and i wanna ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>760 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id                                              tweet emotion score\n",
       "0    10941  at the point today where if someone says somet...   anger  NONE\n",
       "1    10942    it's game day!!!!      t minus 14:30  #relen...   anger  NONE\n",
       "2    10943  this game has pissed me off more than any othe...   anger  NONE\n",
       "3    10944   i've just found out it's candice and not cand...   anger  NONE\n",
       "4    10945      if he can't come to my mum'a 60th after 25...   anger  NONE\n",
       "..     ...                                                ...     ...   ...\n",
       "755  11696   what if the supposed animosity is all bullshi...   anger  NONE\n",
       "756  11697               will byu's offense score 24+ vs wvu?   anger  NONE\n",
       "757  11698  id love 2 c gyimah in action but his coach is ...   anger  NONE\n",
       "758  11699  forgiving means operating with god's spirit &a...   anger  NONE\n",
       "759  11700  i've got a lot of tokens saved up and i wanna ...   anger  NONE\n",
       "\n",
       "[760 rows x 4 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_mentions(tweet):\n",
    "    return re.sub(r'@\\w+', '', tweet)\n",
    "\n",
    "test['tweet'] = test['tweet'].apply(remove_mentions)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_test(test):\n",
    "    test  = re.sub(r'[^\\w\\s#@]', '', test ) \n",
    "    test  = re.sub(r'\\d+', '', test)  \n",
    "    test  = re.sub(r'\\s+', ' ', test).strip()\n",
    "    return test\n",
    "\n",
    "test['tweet'] = test['tweet'].apply(clean_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_tweets(tweet):\n",
    "    tokenizer = TweetTokenizer()\n",
    "    return tokenizer.tokenize(tweet)\n",
    "\n",
    "test['tweet'] = test['tweet'].apply(tokenize_tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = [\n",
    "    'a', 'an', 'and', 'are', 'as', 'at', 'be', 'by', 'for', 'from', 'has', 'he', 'in', 'is', 'it', 'its',\n",
    "    'of', 'on', 'that', 'the', 'to', 'was', 'were', 'will', 'with', 'i', 'you', 'your', 'so', 'this', 'all',\n",
    "    'am', 'or', 'but', 'if', 'my', 'me', 'we', 'us', 'our', 'we', 'up', 'down', 'out', 'just', 'how', 'why',\n",
    "    'when', 'where', 'here', 'there', 'about', 'more', 'most', 'some', 'any', 'few', 'many', 'much', 'not',\n",
    "    'only', 'other', 'same', 'such', 'no', 'nor', 'too', 'very', 'can', 'cannot', 'could', 'should', 'would',\n",
    "    'might', 'must', 'shall', 'will', 'isn', 'hasn', 'doesn', 'haven', 'didn', 'hadn', 'wasn', 'weren',\n",
    "    'wouldn', 'shouldn', 'ain', 'aren', 'ma'\n",
    "]\n",
    "\n",
    "def remove_stopwords(tokens):\n",
    "    filtered_tokens = [token for token in tokens if len(token) > 1 and token.lower() not in stop_words]\n",
    "    return filtered_tokens\n",
    "\n",
    "\n",
    "test['tweet'] = test['tweet'].apply(remove_stopwords)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import BertModel, BertTokenizer\n",
    "\n",
    "\n",
    "model_name = 'bert-base-uncased'\n",
    "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
    "model = BertModel.from_pretrained(model_name)\n",
    "\n",
    "def extract_features(tweet):\n",
    "    tokenized_text = ' '.join(tweet)\n",
    "    input_ids = torch.tensor(tokenizer.encode(tokenized_text, add_special_tokens=True)).unsqueeze(0)\n",
    "    with torch.no_grad():\n",
    "        outputs = model(input_ids)\n",
    "    last_hidden_states = outputs.last_hidden_state\n",
    "    features = last_hidden_states.squeeze(0).numpy()\n",
    "    return features\n",
    "\n",
    "\n",
    "test['features'] = test['tweet'].apply(extract_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 768)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"features\"][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "features =test['features'].tolist()\n",
    "\n",
    "padded_features = pad_sequences(features, padding='post')\n",
    "\n",
    "padded_df = test.copy()\n",
    "padded_df['features'] = padded_features.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input feature shape: (760, 43, 768)\n"
     ]
    }
   ],
   "source": [
    "X_test= np.stack(padded_df['features'])\n",
    "print('Input feature shape:', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(760, 40, 768)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "max_sequence_length = 40\n",
    "truncated_test_data = pad_sequences(X_test, maxlen=max_sequence_length, padding='post', truncating='post')\n",
    "\n",
    "print(truncated_test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.reshape(truncated_test_data, (760, 40 * 768)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_features = np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(760, 30720)\n"
     ]
    }
   ],
   "source": [
    "print(test_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 4s 145ms/step\n"
     ]
    }
   ],
   "source": [
    "\n",
    "predictions = loaded_model.predict(test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [0.44965023]\n",
      " [0.55322933]\n",
      " [0.4946732 ]\n",
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      " [0.43886065]\n",
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      " [0.47329324]\n",
      " [0.3406675 ]\n",
      " [0.39272594]\n",
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      " [0.47087556]\n",
      " [0.45046932]\n",
      " [0.46283352]\n",
      " [0.4703461 ]\n",
      " [0.48368895]\n",
      " [0.36607438]\n",
      " [0.38598782]\n",
      " [0.3625208 ]\n",
      " [0.38516235]\n",
      " [0.5023075 ]\n",
      " [0.48590904]\n",
      " [0.42889124]\n",
      " [0.41300106]\n",
      " [0.39553988]\n",
      " [0.39968783]\n",
      " [0.47216278]\n",
      " [0.4198298 ]\n",
      " [0.42300594]\n",
      " [0.3711148 ]\n",
      " [0.42589307]\n",
      " [0.38915366]\n",
      " [0.42824107]\n",
      " [0.38764006]\n",
      " [0.4838857 ]\n",
      " [0.4893371 ]\n",
      " [0.4734158 ]\n",
      " [0.4443286 ]\n",
      " [0.37540495]\n",
      " [0.39036924]\n",
      " [0.44267237]\n",
      " [0.46056813]]\n"
     ]
    }
   ],
   "source": [
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['score']=pd.DataFrame(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
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       "      <th>tweet</th>\n",
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       "      <td>10941</td>\n",
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       "      <th>757</th>\n",
       "      <td>11698</td>\n",
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       "        Id                                              tweet emotion  \\\n",
       "0    10941  [point, today, someone, says, something, remot...   anger   \n",
       "1    10942                    [game, day, minus, #relentless]   anger   \n",
       "2    10943  [game, pissed, off, than, game, year, blood, b...   anger   \n",
       "3    10944  [ive, found, candice, candace, she, pout, she,...   anger   \n",
       "4    10945  [cant, come, muma, th, after, tweets, then, #s...   anger   \n",
       "..     ...                                                ...     ...   \n",
       "755  11696  [what, supposed, animosity, bullshit, con, ira...   anger   \n",
       "756  11697                    [byus, offense, score, vs, wvu]   anger   \n",
       "757  11698  [id, love, gyimah, action, his, coach, holding...   anger   \n",
       "758  11699  [forgiving, means, operating, gods, spirit, am...   anger   \n",
       "759  11700  [ive, got, lot, tokens, saved, wanna, spam, ev...   anger   \n",
       "\n",
       "        score                                           features  \n",
       "0    0.451130  [[0.27355012, 0.030854275, 0.0725501, 0.058589...  \n",
       "1    0.378544  [[-0.062490027, -0.046009917, 0.018530663, 0.1...  \n",
       "2    0.510505  [[-0.21455964, 0.009541917, 0.15138617, -0.157...  \n",
       "3    0.373545  [[-0.18939371, 0.05794645, -0.04610769, 0.0864...  \n",
       "4    0.474511  [[0.055749744, 0.199722, 0.5565932, -0.1949415...  \n",
       "..        ...                                                ...  \n",
       "755  0.444329  [[-0.073396154, 0.24903403, -0.1305108, 0.0700...  \n",
       "756  0.375405  [[-0.72507215, -0.6317022, -0.17955363, 0.0976...  \n",
       "757  0.390369  [[-0.917267, 0.029235095, -0.12552397, -0.4948...  \n",
       "758  0.442672  [[0.026432868, 0.3324353, -0.22418864, -0.1191...  \n",
       "759  0.460568  [[-0.0750121, 0.17871594, -0.030560482, -0.190...  \n",
       "\n",
       "[760 rows x 5 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=test.drop('features',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
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       "      <td>[ive, got, lot, tokens, saved, wanna, spam, ev...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.460568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>760 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id                                              tweet emotion  \\\n",
       "0    10941  [point, today, someone, says, something, remot...   anger   \n",
       "1    10942                    [game, day, minus, #relentless]   anger   \n",
       "2    10943  [game, pissed, off, than, game, year, blood, b...   anger   \n",
       "3    10944  [ive, found, candice, candace, she, pout, she,...   anger   \n",
       "4    10945  [cant, come, muma, th, after, tweets, then, #s...   anger   \n",
       "..     ...                                                ...     ...   \n",
       "755  11696  [what, supposed, animosity, bullshit, con, ira...   anger   \n",
       "756  11697                    [byus, offense, score, vs, wvu]   anger   \n",
       "757  11698  [id, love, gyimah, action, his, coach, holding...   anger   \n",
       "758  11699  [forgiving, means, operating, gods, spirit, am...   anger   \n",
       "759  11700  [ive, got, lot, tokens, saved, wanna, spam, ev...   anger   \n",
       "\n",
       "        score  \n",
       "0    0.451130  \n",
       "1    0.378544  \n",
       "2    0.510505  \n",
       "3    0.373545  \n",
       "4    0.474511  \n",
       "..        ...  \n",
       "755  0.444329  \n",
       "756  0.375405  \n",
       "757  0.390369  \n",
       "758  0.442672  \n",
       "759  0.460568  \n",
       "\n",
       "[760 rows x 4 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test\n",
    "\n",
    "\n"
   ]
  }
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