model_stop_words.ipynb

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    "colab": {
      "name": "model_stop_words.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNE+zZhbKFlTD68xbBLjKEz",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Gail529/AES/blob/main/model_stop_words.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54bvoPIZgTGY",
        "outputId": "a8af565b-9879-4af2-fafa-e846394dadba"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import gensim\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "from nltk import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65NL0CQGdfY3"
      },
      "source": [
        "data=pd.read_csv('/content/tweet_emotion_scores.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wW8CXhEhBaWl"
      },
      "source": [
        "data=pd.read_csv('/content/Mental_health.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "-SrqEWYCf2SE",
        "outputId": "d7b81928-49b6-4f42-9a1c-a1d89c976c66"
      },
      "source": [
        "data.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Unnamed: 0</th>\n",
              "      <th>tweets</th>\n",
              "      <th>Positive</th>\n",
              "      <th>Negative</th>\n",
              "      <th>Anger</th>\n",
              "      <th>Anticipation</th>\n",
              "      <th>Disgust</th>\n",
              "      <th>Fear</th>\n",
              "      <th>Joy</th>\n",
              "      <th>Sadness</th>\n",
              "      <th>Surprise</th>\n",
              "      <th>Trust</th>\n",
              "      <th>afinn_score</th>\n",
              "      <th>positive_vader</th>\n",
              "      <th>negative_vader</th>\n",
              "      <th>neutral_vader</th>\n",
              "      <th>emotion_score</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>0.101</td>\n",
              "      <td>0.809</td>\n",
              "      <td>0.283441</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>christmas is a good time to start practicing g...</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.388</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.612</td>\n",
              "      <td>0.110710</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>driving in the midst of depression   mindwand...</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.087</td>\n",
              "      <td>0.315</td>\n",
              "      <td>0.598</td>\n",
              "      <td>0.171658</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>may the prince of peace give u all peace of ou...</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>0.292</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.708</td>\n",
              "      <td>0.075926</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>anyone else just pretending they  re ok  sadtw...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>-3.0</td>\n",
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              "      <td>0.366</td>\n",
              "      <td>0.184060</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
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              "   Unnamed: 0  ... labels\n",
              "0           0  ...      2\n",
              "1           1  ...      1\n",
              "2           2  ...      1\n",
              "3           3  ...      1\n",
              "4           4  ...      1\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cN3w-L0vf_HO"
      },
      "source": [
        "data.loc[(data['Positive'] < data['Negative']) & (data['Sadness'] >= 1) , 'score']= '1'\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "-mGXZ60ngFSZ",
        "outputId": "3705c6c8-9921-4ab7-dee8-6a81f82eeaa4"
      },
      "source": [
        "del data['Unnamed: 0']\n",
        "del data['user']\n",
        "del data['location']\n",
        "del data['text']\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweets</th>\n",
              "      <th>Positive</th>\n",
              "      <th>Negative</th>\n",
              "      <th>Anger</th>\n",
              "      <th>Anticipation</th>\n",
              "      <th>Disgust</th>\n",
              "      <th>Fear</th>\n",
              "      <th>Joy</th>\n",
              "      <th>Sadness</th>\n",
              "      <th>Surprise</th>\n",
              "      <th>Trust</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "    <tr>\n",
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              "      <td>may the prince of peace give u all peace of ou...</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>anyone else just pretending they  re ok  sadtw...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "</table>\n",
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            ],
            "text/plain": [
              "                                              tweets  Positive  ...  Trust  score\n",
              "0  people can also take advantage of your phone a...         2  ...      1      0\n",
              "1  christmas is a good time to start practicing g...         3  ...      1      0\n",
              "2   driving in the midst of depression   mindwand...         1  ...      0      1\n",
              "3  may the prince of peace give u all peace of ou...         4  ...      2      0\n",
              "4  anyone else just pretending they  re ok  sadtw...         0  ...      0      1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DMP7clmMgm6t",
        "outputId": "ac0a8084-7fe8-4282-af3e-e7143c60e79d"
      },
      "source": [
        "tweets=data['tweets'].values.tolist()\n",
        "len(tweets)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1999"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pOc3IFfojdT8"
      },
      "source": [
        "tweet_list=[]\n",
        "for tweet in tweets:\n",
        "    tokens =  word_tokenize(tweet)\n",
        "    #stop_words=set(stopwords.words(\"english\")) \n",
        "    #word=[word for word in tokens if word not in stop_words]\n",
        "    tweet_list.append(tokens)\n",
        "\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQykIRJmLm5O"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1NxG_84kFh9"
      },
      "source": [
        "tweet_list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B24DKlqYgpp4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f45f5e20-e248-4d7d-b63d-3d00886e067e"
      },
      "source": [
        "EMBEDDING_DIM=100\n",
        "model = gensim.models.Word2Vec(sentences=tweet_list,size=EMBEDDING_DIM,window=5,workers=4,min_count=1)\n",
        "words = list(model.wv.vocab)\n",
        "print('Vocabulary size: %d',len(words))\n",
        "print(words)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Vocabulary size: %d 4908\n",
            "['people', 'can', 'also', 'take', 'advantage', 'of', 'your', 'phone', 'addiction', 'before', 'you', 'dont', 'treat', 'it', 'right', 'away', 'download', 'our', 'app', 'now', 'christmas', 'is', 'a', 'good', 'time', 'to', 'start', 'practicing', 'gratitude', 'check', 'out', 'how', 'will', 'change', 'life', 'driving', 'in', 'the', 'midst', 'depression', 'mindwandering', 'cause', 'higher', 'risk', 'be', 'careful', 'pl', 'may', 'prince', 'peace', 'give', 'u', 'all', 'mind', 'body', 'soul', 'from', 'today', 'going', 'forward', 'prayerforlove', 'anyone', 'else', 'just', 'pretending', 'they', 're', 'ok', 'sadtweets', 'sad', 'at', 'this', 'point', 'ain', 't', 'no', 'need', 'friend', 'for', 'help', 'trend', 'therefore', 'wisdom', 'gods', 'word', 'bring', 'christ', 'jesus', 'heart', 'without', 'delay', 'adult', 'there', 'still', 'struggling', 'with', 'abandonment', 'issue', 'caused', 'by', 'parent', 'divorce', 'know', 'that', 'yourself', 'gift', 'self', 'love', 'holiday', 'season', 'more', 'than', 'ever', 'we', 'must', 'prioritize', 'wellbeing', 'w', 'see', 'their', 'free', 'healthy', 'minds', 'related', 'whats', 'up', 'new', '2019', 'conference', 'released', 'author', 'peterbreggin', 'psychiatric', 'drugs', 'are', 'neurotoxins', 'why', 'and', 'skype', 'therapy', 'overcoming', 'anxiety', 'effective', 'psychotherapy', 'home', 'email', 'me', 'schedule', 'sky', 'what', 'my', 'coffee', 'say', 'december', '23', 'drink', 'its', 'sparkle', 'shine', 'llamazing', 'one', 'coffeelovers', 'better', 'line', 'between', 'patient', 'loved', 'protect', 'poa', 'truly', 'adv', 'get', 'interactive', 'session', 'best', 'doctor', 'reach', 'mentalhealth', 'amazing', 'singleparents', 'widows', 'who', 'have', 'lost', '2020', 'into', '2021', 'i', 'hope', 'talked', 'about', 'feel', 'worth', 'reposting', 'rereading', 'an', 'endogenous', 'opioid', 'deficiency', 'expanded', 'suffocation', 'false', 'alarm', 'theory', 'model', 'employers', 'hr', 'department', 'should', 'consider', 'way', 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'versa', 'gianna', '1989', 'rhetorical', 'sarcastic', 'depersonnalisation', 'ahaha', 'sike', 'hearing', 'nerve', 'wrecking', 'thatll', 'stable', 'mr', 'topak', 'kasi', 'po', 'healed', 'beg', 'bro', 'ahhhh', 'imposter', 'syndrome', 'appt', 'ruthy', 'taylor', 'swift', 'thirteen', 'genuine', 'obsessing', 'bot', 'worrying', 'lifted', 'u0001f9b7', 'tooth', 'teeth', 'bisexy', 'addict', 'numerous', 'somatic', 'tmi', 'medicated', 'somehow', 'inspired', 'happ', 'moot', 'reece', 'str', 'users', 'labeled', 'anchor', 'satisfied', 'strict', 'appreciative', 'anxiet', 'inb', 'referred', 'waiting', 'list', 'bipolardepression', 'market', 'expected', 'cagr', '1669', '201730', 'managed', 'smoking', 'socialize', 'imprisoned', 'multiple', 'illnesss', 'invalidation', 'polit', 'liberal', 'following', 'rea', 'redoing', 'fyucki', 'fyu', 'pronoun', 'edtwt', 'sporadically', 'xem', 'idc', 'alw', 'hunny', 'regula', 'farahxleeuwenhof', 'deserves', 'pampering', 'rly', 'pissed', 'abt', 'ended', 'unfoll', 'stan', 'acc', 'commented', 'triggered', 'punched', 'hole', 'bedroom', 'nit', 'aggressive', 'order', 'sick', 'capacity', 'familys', 'ove', 'properly', 'seeker', 'doct', 'impossible', 'gm', 'susp', 'excuse', 'disclaimer', 'basically', 'traced', 'mdd', 'treated', 'differently', 'excluded', 'som', 'ooze', 'masc', 'deployment', 'strain', 'uhmh', 'suree', 'display', 'javent', 'sibling', 'u0001f971', 'fck', 'ailment', 'barney', 'february', 'grandfather', 'jul', 'eligible', 'pencil', 'understandable', 'professi', 'recorded', 'huh', 'except', 'su1c1dal', 'acti', 'specific', 'arachnoid', 'lobe', 'size', 'cleared', 'u1', 'eudicia', 'occasionally', '70000', 'ibd', 'tied', 'comorbid', 'awwww', 'ness', 'ik', 'und', 'hahaha', 'cue', 'frame', '28', 'granddaughter', 'woah', 'af', 'estimate', '570k', 'pushed', '3yrs', 'norm', 'allura', 'haru', 'surprise', 'span', 'xx', 'cybernova', 'fighti', 'sadly', 'selfharm', 'ineffective', 'gesture', 'wo', 'imo', 'omg', 'medicineeeeeeeeeeeee', 'inlaw', 'hill', 'sum', 'europea', 'absolute', '430', 'lifelong', 'annoying', 'internalize', 'ego', 'elite', 'bunch', '20092014', '70', 'male', 'si', 'confidence', 'lmaooooo', 'offends', 'hmu', 'sensory', 'allergy', 'blast', 'itching', 'niharika', 'pcod', 'healthier', 'sprea', 'difference', 'tellin', 'lexapro', 'weird', 'stupid', 'phrase', 'guilty', 'myselffeeling', 'concussion', 'neck', 'adrenal', 'btw', 'selft', 'agnosick', 'deseases', 'consistent', 'malar', 'rash', 'difficulty', 'tachycardia', 'indicative', 'mcas', 'lazy', 'proceed', 'sit', 'improv', 'kahit', 'binge', 'odaat', 'watched', 'penelop', 'emotionsafter', 'diabetes', 'blackout', 'attempted', 'lillyanas', 'mum', 'lee', 'witn', 'sen', 'heyo', 'names', 'emi', 'sitting', 'effort', 'politician', 'noone', 'sectioned', 'schizophrenic', 'doc', 'trash', 'medscape', 'conditio', 'substantial', 'sometim', 'replying', 'dms', 'humbly', 'pp', 'refused', 'toward', 'personally', 'gotten', 'smoker', 'various', 'uncured', 'tashigi', 'tfw', 'comorbidities', 'alongside', 'yeaaaa', 'gue', 'wellbutrin', 'pure', 'babeee', 'penis', 'heroine', 'addicted', 'cli', 'sons', 'combination', 'omegle', 'tysm', 'pr', 'badly', 'portray', 'zoey', 'traumatic', 'bcz', 'mome', 'survived2020', 'herself', 'frustrated', 'angry', 'catfishing', 'gaslig', 'antidepressants', 'shrink', 'craig', 'deen', 'plenty', 'hahahahhaha', 'therapi', 'controlling', '2014', 'fanfic', 'drained', 'atlas', 'evanna', 'lupin', 'attacked', 'werewolf', 'hooooly', 'anyway', '34', 'suicides', 'incorrectly', 'manic', 'hot', 'explanation', 'joking', 'depers', 'presentation', 'eupd', 'unemployed', 'copay', 'stare', 'nhs', 'overwhelmed', 'restriction', 'financ', 'ii', 'narcissism', 'scored', 'curious', 'sane', 'boy', 'amongst', 'outmuscled', 'freekick', 'lewa', 'invalidating', 'se', 'nyc', 'selfdiagnosis', 'dangerous', 'medicatio', 'suici', 'lock', 'normalized', 'texting', 'resistant', 'const', 'paranoid', 'rece', 'yikes', 'concerned', 'unde', 'grandas', 'frie', 'tak', 'akan', 'tahu', 'hadap', 'llllloooollllll', 'nur', 'middl', 'identified', 'oxygen', 'organ', 'surgery', 'worke', 'comparing', 'boyfriend', 'wreck', 'idrk', 'swag', 'prophet', 'yaqub', 'alaihisala', 'assoc', 'werent', 'rejected', 'questionnaire', 'committed', 'complicated', 'pfft', 'questioning', 'assume', 'freshman', 'prob', 'reaction', 'hysteria', 'teens', 'educational', 'achievement', 'terminal', 'fair', 'nursing', 'richard', 'moron', 'conscious', 'iceberg', 'goin', 'masculine', '3am', 'dissociation', 'oomfs', 'recommended', 'sincerely', 'stigm', 'drawingwithanxiety', 'drawingwithadhd', 'max', 'undi', 'bp2', 'immediately', 'besid', 'knowing', 'frightening', 'waz', 'bcos', 'epic', 'pogchamp', 'rejection', 'sensitivity', 'obvious', '52560', 'sc', 'chest', 'glass', 'lmaooooooooo', 'fee', 'poggers', 'decades', 'poison', 'progressively', 'dysthymia', 'disentangle', 'range', 'kalon', 'delayed', 'operation', 'cap', 'aka', 'livingalegacy', 'entertainment', 'hypomanic', 'saddest', 'hysterical', 'gir', 'fridaythoughts', 'fridaymood', 'snuck', 'received', 'bulimia', 'aga', 'generaliz', 'outburst', 'easily', 'mancave', 'cont', 'brushed', 'mar', 'square', 'saturn', 'pluto', 'orange', 'aunt', 'blaimed', 'commonly', 'generalize', 'deppression', 'doting', 'inc', 'anfield', 'redecorated', 'interior', 'psycologist', 'loosing', 'yup', 'traum', 'record', 'dollar', 'dropped', 'publicly', 'baseline', 'selfdx', 'evil', 'spouse', 'pointed', 'fandom', 'happend', 'foreve', 'frankincense', 'copaiba', 'bra', 'struggled', 'aspect', 'trashcan', 'shou', 'project', 'skill', 'covidrelated', 'textshe', 'dysfunction', 'rsd', 'regulation', 'impulsive', 'participate', 'hashtag', 'hypersensitive', 'hearin', 'western', 'apa', 'nyumbani', 'looooong', 'roman', 'tumblr', 'auntie', 'tumigil', 'ang', 'trabaho', 'depends', 'shanti', 'das', 'announce', 'fifa', 'ongoing', 'autis', 'untold', 'misery', 'economic', 'disproportionate', 'glorifie', 'welp', 'dunno', 'mark', 'turned', 'zoloft', 'dating', 'diagnostic', 'ev', 'equate', 'epilepsy', '2001', 'undercurrent', 'achieved', 'translating', 'publish', 'midget', 'returning', 'rewind', 'cu', 'todo', 'mal', 'fighting', 'neva', 'fourteen', 'slee', 'punch', 'sth', 'wiping', 'kaia', 'pat', 'siri', 'hispanic', 'turns', 'tying', 'hair', 'visiting', 'stomach', 'ache', 'admitting', 'overweight', 'inherent', 'bias', 'gro', 'mainly', 'discredit', 'dxers', 'anecdote', 'spam', 'reply', 'bump', 'vibe', 'beca', 'ima', 'mmmmm', 'dyspraxia', 'crohn', 'latin', 'wtf', 'encima', 'dice', 'queee', 'initially', 'disastrous', '65', 'percent', 'poverty', 'religious', 'hatred', 'bullimia', 'nervosa', 'closely', 'stated', 'initial', 'generally', 'score', 'neuroticism', 'amount', 'ham', 'everytime', 'sdxs', 'causing', 'develop', 'pist', 'october', 'internalised', 'apar', 'jeff', 'killer', 'randomly', 'asperges', 'izukus', 'tic', 'repressed', 'w33d', 'believed', 'newer', 'follower', 'owllie', 'oc', 'fatality', 'statistic', 'subtract', '27', 'fell', 'har', 'dose', 'bette', 'vertigo', 'fashionable', 'nigga', 'realise', 'minor', 'unt', 'woo', 'boi', 'soup', 'boiling', 'completing', 'somethin', 'mistook', 'newly', 'accurate', 'wanti', 'ext', 'australia', 'nsw', 'gambling', '201920', 'principal', 'disfunction', 'usi', 'has', 'uphill', 'lewy', 'dementia', 'prayed', 'pray', 'deel', 'asks', 'respect', 'selfharmed', 'dyslexia', 'smth', 'owns', 'raging', 'inferiority', 'compl', 'varies', 'chanel', 'factz', 'ab', 'bcs', 'sebastian', 'misunderstand', '2010', 'placed', 'cute', 'someones', 'heartwarming', 'rant', '2yrs', 'declined', 'bulb', 'anx', 'upset', 'blocked', 'dykes', 'lashed', 'neil', 'lennon', 'nida', 'rehman', 'kashmir', 'destigmatized', 'updated', 'addition', 'alreadydiagnosed', 'disea', 'interview', 'killing', 'trust', 'acute', 'paper', 'fucked', 'picky', 'iokparadiseinpain', '42', 'visited', 'reasons', 'teensplain', 'sweetie']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmNXuyVliCI1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6212945d-e381-4224-98a0-4484f9755e64"
      },
      "source": [
        "model.wv.most_similar('depression')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('diagnosed', 0.9967317581176758),\n",
              " ('wa', 0.9963631629943848),\n",
              " ('anxiety', 0.9959952235221863),\n",
              " ('mentalhealth', 0.9959667921066284),\n",
              " ('year', 0.9958199262619019),\n",
              " ('get', 0.9953647255897522),\n",
              " ('people', 0.9949799180030823),\n",
              " ('health', 0.994754433631897),\n",
              " ('like', 0.994587779045105),\n",
              " ('time', 0.9944572448730469)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZnauC_OmInN"
      },
      "source": [
        "filename = 'tweets_embedding.txt'\n",
        "model.wv.save_word2vec_format(filename,binary=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOQDMEcHm49c"
      },
      "source": [
        "import os\n",
        "embeddings_index = {}\n",
        "f = open(os.path.join('','tweets_embedding.txt'),encoding=\"utf-8\")\n",
        "for line in f:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    coefs = np.asarray(values[1:])\n",
        "    embeddings_index[word] = coefs\n",
        "f.close()    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lGmIhfvMr1bV"
      },
      "source": [
        "from tensorflow.python.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.python.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ywLWDRgos4-",
        "outputId": "8f62d7c3-913c-45ee-b6a6-01c7d20a99c3"
      },
      "source": [
        "tok_obj = Tokenizer()\n",
        "tok_obj.fit_on_texts(tweet_list)\n",
        "sequences = tok_obj.texts_to_sequences(tweet_list)\n",
        "\n",
        "word_index=tok_obj.word_index\n",
        "print(len(words))\n",
        "\n",
        "max_length = max([len(s.split()) for s in tweets])\n",
        "\n",
        "tweet_pad =pad_sequences(sequences,maxlen=max_length)\n",
        "emotion_score=data['labels'].values\n",
        "\n",
        "print('Shape of tweet Tensor:',tweet_pad.shape)\n",
        "print('Shape of emotion_score Tensor:',emotion_score.shape)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4908\n",
            "Shape of tweet Tensor: (1999, 29)\n",
            "Shape of emotion_score Tensor: (1999,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxvwpIMPtGqi"
      },
      "source": [
        "num_words = len(word_index) + 1\n",
        "embedding_matrix = np.zeros((num_words,EMBEDDING_DIM))\n",
        "\n",
        "for word,i in word_index.items():\n",
        "    if i > num_words:\n",
        "        continue\n",
        "        embedding_vector = embeddings_index.get(word)\n",
        "        if embedding_vector is not None:\n",
        "            embedding_matrix[i] = embedding_vector"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KkjovhgDt7At",
        "outputId": "69fe046b-1cc2-4936-ce09-4ff50801f321"
      },
      "source": [
        "print(num_words)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4909\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Ya6syCtuCTl"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Embedding,LSTM,GRU\n",
        "from keras.layers.embeddings import Embedding\n",
        "from keras.initializers import Constant\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense, Flatten, LSTM, Dropout, Activation, Embedding, Bidirectional\n",
        "\n",
        "\n",
        "\n",
        "model = Sequential()\n",
        "embedding_layer = Embedding(num_words ,EMBEDDING_DIM)\n",
        "                             #embeddings_initializer=Constant(embedding_matrix),\n",
        "                             #input_length=max_length  ,\n",
        "                             #trainable=False \n",
        "                            \n",
        "\n",
        "model.add(embedding_layer)\n",
        "model.add(Dropout(0.5))\n",
        "model.add(LSTM(1, activation=\"tanh\"))\n",
        "model.add(Dense(6,activation='softmax'))\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy',optimizer='adam' , metrics=['accuracy'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axq4niqswduR"
      },
      "source": [
        "VALIDATION_SPLIT = 0.2\n",
        "indices = np.arange(tweet_pad.shape[0])\n",
        "np.random.shuffle(indices)\n",
        "tweet_pad = tweet_pad[indices]\n",
        "emotion_score = emotion_score[indices]\n",
        "\n",
        "num_validation_samples = int(VALIDATION_SPLIT * tweet_pad.shape[0])\n",
        "\n",
        "x_train_pad = tweet_pad[:-num_validation_samples]\n",
        "y_train = emotion_score[:-num_validation_samples]\n",
        "x_test_pad = tweet_pad[-num_validation_samples:]\n",
        "y_test = emotion_score[-num_validation_samples:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fMRmKnCb10T7"
      },
      "source": [
        "x_train_pad = np.asarray(x_train_pad).astype(np.float32)\n",
        "y_train = np.asarray(y_train).astype(np.float32)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y1SS4WVYrKgl"
      },
      "source": [
        "x_test_pad = np.asarray(x_test_pad).astype(np.float32)\n",
        "y_test= np.asarray(y_test).astype(np.float32)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gToHw1k7tdQ9",
        "outputId": "a62c4771-6984-4195-bbdb-55db14651c6b"
      },
      "source": [
        "print(len(x_test_pad))\n",
        "print(len(x_train_pad))\n",
        "print(len(y_train))\n",
        "print(len(y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "399\n",
            "1600\n",
            "1600\n",
            "399\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YTd0aqH0J46",
        "outputId": "3347922f-709f-4e5a-b195-4fe68bd7a1c0"
      },
      "source": [
        "model.fit(x_train_pad,y_train, batch_size=10, epochs=20 , validation_data=(x_test_pad,y_test),verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "160/160 - 4s - loss: 1.6233 - accuracy: 0.5800 - val_loss: 1.4439 - val_accuracy: 0.5965\n",
            "Epoch 2/20\n",
            "160/160 - 2s - loss: 1.3227 - accuracy: 0.6206 - val_loss: 1.2390 - val_accuracy: 0.5965\n",
            "Epoch 3/20\n",
            "160/160 - 2s - loss: 1.1499 - accuracy: 0.6206 - val_loss: 1.1218 - val_accuracy: 0.5965\n",
            "Epoch 4/20\n",
            "160/160 - 2s - loss: 1.0346 - accuracy: 0.6206 - val_loss: 1.0521 - val_accuracy: 0.5965\n",
            "Epoch 5/20\n",
            "160/160 - 2s - loss: 0.9516 - accuracy: 0.6206 - val_loss: 1.0035 - val_accuracy: 0.5965\n",
            "Epoch 6/20\n",
            "160/160 - 2s - loss: 0.8879 - accuracy: 0.6281 - val_loss: 0.9612 - val_accuracy: 0.5965\n",
            "Epoch 7/20\n",
            "160/160 - 2s - loss: 0.8382 - accuracy: 0.6431 - val_loss: 0.9269 - val_accuracy: 0.5990\n",
            "Epoch 8/20\n",
            "160/160 - 2s - loss: 0.7963 - accuracy: 0.6619 - val_loss: 0.9003 - val_accuracy: 0.6015\n",
            "Epoch 9/20\n",
            "160/160 - 2s - loss: 0.7636 - accuracy: 0.6719 - val_loss: 0.8689 - val_accuracy: 0.6165\n",
            "Epoch 10/20\n",
            "160/160 - 2s - loss: 0.7334 - accuracy: 0.6769 - val_loss: 0.8524 - val_accuracy: 0.6140\n",
            "Epoch 11/20\n",
            "160/160 - 2s - loss: 0.7030 - accuracy: 0.6800 - val_loss: 0.8259 - val_accuracy: 0.6291\n",
            "Epoch 12/20\n",
            "160/160 - 2s - loss: 0.6709 - accuracy: 0.6869 - val_loss: 0.8110 - val_accuracy: 0.6391\n",
            "Epoch 13/20\n",
            "160/160 - 2s - loss: 0.6351 - accuracy: 0.7088 - val_loss: 0.7906 - val_accuracy: 0.6441\n",
            "Epoch 14/20\n",
            "160/160 - 2s - loss: 0.5952 - accuracy: 0.7531 - val_loss: 0.7642 - val_accuracy: 0.6792\n",
            "Epoch 15/20\n",
            "160/160 - 2s - loss: 0.5547 - accuracy: 0.8125 - val_loss: 0.7359 - val_accuracy: 0.7018\n",
            "Epoch 16/20\n",
            "160/160 - 2s - loss: 0.5130 - accuracy: 0.8750 - val_loss: 0.7140 - val_accuracy: 0.7444\n",
            "Epoch 17/20\n",
            "160/160 - 2s - loss: 0.4723 - accuracy: 0.9350 - val_loss: 0.6991 - val_accuracy: 0.7494\n",
            "Epoch 18/20\n",
            "160/160 - 2s - loss: 0.4371 - accuracy: 0.9556 - val_loss: 0.6873 - val_accuracy: 0.7594\n",
            "Epoch 19/20\n",
            "160/160 - 2s - loss: 0.4045 - accuracy: 0.9550 - val_loss: 0.6835 - val_accuracy: 0.7669\n",
            "Epoch 20/20\n",
            "160/160 - 2s - loss: 0.3778 - accuracy: 0.9400 - val_loss: 0.6735 - val_accuracy: 0.7569\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f7f01192ad0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QavzjDRzKNgc",
        "outputId": "3befe46d-332f-4bbe-8e6d-acfdcd02b49f"
      },
      "source": [
        "model.fit(x_train_pad,y_train, batch_size=20, epochs=20 , validation_data=(x_test_pad,y_test),verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "80/80 - 2s - loss: 0.3579 - accuracy: 0.9175 - val_loss: 0.6641 - val_accuracy: 0.7619\n",
            "Epoch 2/20\n",
            "80/80 - 1s - loss: 0.3479 - accuracy: 0.9131 - val_loss: 0.6640 - val_accuracy: 0.7594\n",
            "Epoch 3/20\n",
            "80/80 - 1s - loss: 0.3379 - accuracy: 0.9131 - val_loss: 0.6626 - val_accuracy: 0.7519\n",
            "Epoch 4/20\n",
            "80/80 - 1s - loss: 0.3293 - accuracy: 0.9137 - val_loss: 0.6645 - val_accuracy: 0.7519\n",
            "Epoch 5/20\n",
            "80/80 - 1s - loss: 0.3215 - accuracy: 0.9131 - val_loss: 0.6683 - val_accuracy: 0.7494\n",
            "Epoch 6/20\n",
            "80/80 - 1s - loss: 0.3138 - accuracy: 0.9137 - val_loss: 0.6695 - val_accuracy: 0.7519\n",
            "Epoch 7/20\n",
            "80/80 - 1s - loss: 0.3062 - accuracy: 0.9150 - val_loss: 0.6715 - val_accuracy: 0.7519\n",
            "Epoch 8/20\n",
            "80/80 - 1s - loss: 0.3001 - accuracy: 0.9150 - val_loss: 0.6704 - val_accuracy: 0.7519\n",
            "Epoch 9/20\n",
            "80/80 - 1s - loss: 0.2954 - accuracy: 0.9150 - val_loss: 0.6746 - val_accuracy: 0.7544\n",
            "Epoch 10/20\n",
            "80/80 - 1s - loss: 0.2898 - accuracy: 0.9150 - val_loss: 0.6707 - val_accuracy: 0.7494\n",
            "Epoch 11/20\n",
            "80/80 - 1s - loss: 0.2857 - accuracy: 0.9150 - val_loss: 0.6753 - val_accuracy: 0.7419\n",
            "Epoch 12/20\n",
            "80/80 - 1s - loss: 0.2808 - accuracy: 0.9162 - val_loss: 0.6849 - val_accuracy: 0.7419\n",
            "Epoch 13/20\n",
            "80/80 - 1s - loss: 0.2768 - accuracy: 0.9162 - val_loss: 0.6862 - val_accuracy: 0.7368\n",
            "Epoch 14/20\n",
            "80/80 - 1s - loss: 0.2740 - accuracy: 0.9156 - val_loss: 0.6945 - val_accuracy: 0.7368\n",
            "Epoch 15/20\n",
            "80/80 - 1s - loss: 0.2707 - accuracy: 0.9162 - val_loss: 0.7067 - val_accuracy: 0.7343\n",
            "Epoch 16/20\n",
            "80/80 - 1s - loss: 0.2681 - accuracy: 0.9162 - val_loss: 0.7116 - val_accuracy: 0.7368\n",
            "Epoch 17/20\n",
            "80/80 - 1s - loss: 0.2657 - accuracy: 0.9162 - val_loss: 0.7139 - val_accuracy: 0.7293\n",
            "Epoch 18/20\n",
            "80/80 - 1s - loss: 0.2630 - accuracy: 0.9162 - val_loss: 0.7116 - val_accuracy: 0.7293\n",
            "Epoch 19/20\n",
            "80/80 - 1s - loss: 0.2607 - accuracy: 0.9162 - val_loss: 0.7235 - val_accuracy: 0.7368\n",
            "Epoch 20/20\n",
            "80/80 - 1s - loss: 0.2587 - accuracy: 0.9150 - val_loss: 0.7290 - val_accuracy: 0.7343\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f7ef6530e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IY5eb1lNPUc",
        "outputId": "ef15da9c-a25f-4f16-c8df-422e50b30c92"
      },
      "source": [
        "txt=[\"who new life would be so hard this fast\"]\n",
        "seq = tok_obj.texts_to_sequences(txt)\n",
        "padded = pad_sequences(seq, maxlen=max_length)\n",
        "pred = model.predict(padded)\n",
        "print(np.argmax(pred))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}