sentiment_analysis_naive_byes.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
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        "t-8tGxdPXE4l",
        "XgDjE1gwXMa4",
        "IqVnnPJ1XWuA"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Import libraries"
      ],
      "metadata": {
        "id": "kJRfXdRwfJj6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import string\n",
        "import math"
      ],
      "metadata": {
        "id": "wdZX1QLhAT4w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from utils import process_tweet, lookup\n",
        "import pdb\n",
        "from nltk.corpus import stopwords, twitter_samples\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import nltk\n",
        "import string\n",
        "from nltk.tokenize import TweetTokenizer\n",
        "from os import getcwd\n",
        "from nltk.stem import PorterStemmer\n",
        "from matplotlib.patches import Ellipse\n",
        "import matplotlib.transforms as transforms\n",
        "#import w2_unittest\n",
        "\n",
        "nltk.download('twitter_samples')\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T0Oo6QXG-8DB",
        "outputId": "c40943b8-4da4-4aa0-98db-e51476bdb53b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package twitter_samples to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/twitter_samples.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define process_tweet(tweet)"
      ],
      "metadata": {
        "id": "t-8tGxdPXE4l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def process_tweet(tweet):\n",
        "    '''\n",
        "    Input:\n",
        "        tweet: a string containing a tweet\n",
        "    Output:\n",
        "        tweets_clean: a list of words containing the processed tweet\n",
        "\n",
        "    '''\n",
        "    stemmer = PorterStemmer()\n",
        "    stopwords_english = stopwords.words('english')\n",
        "    # remove stock market tickers like $GE\n",
        "    tweet = re.sub(r'\\$\\w*', '', tweet)\n",
        "    # remove old style retweet text \"RT\"\n",
        "    tweet = re.sub(r'^RT[\\s]+', '', tweet)\n",
        "    # remove hyperlinks\n",
        "    #tweet = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', tweet)\n",
        "    tweet = re.sub(r'https?://[^\\s\\n\\r]+', '', tweet)\n",
        "    # remove hashtags\n",
        "    # only removing the hash # sign from the word\n",
        "    tweet = re.sub(r'#', '', tweet)\n",
        "    # tokenize tweets\n",
        "    tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True,\n",
        "                               reduce_len=True)\n",
        "    tweet_tokens = tokenizer.tokenize(tweet)\n",
        "\n",
        "    tweets_clean = []\n",
        "    for word in tweet_tokens:\n",
        "        if (word not in stopwords_english and  # remove stopwords\n",
        "            word not in string.punctuation):  # remove punctuation\n",
        "            # tweets_clean.append(word)\n",
        "            stem_word = stemmer.stem(word)  # stemming word\n",
        "            tweets_clean.append(stem_word)\n",
        "\n",
        "    return tweets_clean"
      ],
      "metadata": {
        "id": "I_LXOqsOAO49"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test_lookup(func):\n",
        "    freqs = {('sad', 0): 4,\n",
        "             ('happy', 1): 12,\n",
        "             ('oppressed', 0): 7}\n",
        "    word = 'happy'\n",
        "    label = 1\n",
        "    if func(freqs, word, label) == 12:\n",
        "        return 'SUCCESS!!'\n",
        "    return 'Failed Sanity Check!'"
      ],
      "metadata": {
        "id": "auvKBUcFArbC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def lookup(freqs, word, label):\n",
        "    '''\n",
        "    Input:\n",
        "        freqs: a dictionary with the frequency of each pair (or tuple)\n",
        "        word: the word to look up\n",
        "        label: the label corresponding to the word\n",
        "    Output:\n",
        "        n: the number of times the word with its corresponding label appears.\n",
        "    '''\n",
        "    n = 0  # freqs.get((word, label), 0)\n",
        "\n",
        "    pair = (word, label)\n",
        "    if (pair in freqs):\n",
        "        n = freqs[pair]\n",
        "\n",
        "    return n"
      ],
      "metadata": {
        "id": "SIFJGygYAugs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load data and split into training and testing"
      ],
      "metadata": {
        "id": "XgDjE1gwXMa4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get the sets of positive and negative tweets\n",
        "all_positive_tweets = twitter_samples.strings('positive_tweets.json')\n",
        "all_negative_tweets = twitter_samples.strings('negative_tweets.json')\n",
        "\n",
        "# split the data into two pieces, one for training and one for testing (validation set)\n",
        "test_pos = all_positive_tweets[4000:]\n",
        "train_pos = all_positive_tweets[:4000]\n",
        "test_neg = all_negative_tweets[4000:]\n",
        "train_neg = all_negative_tweets[:4000]\n",
        "\n",
        "train_x = train_pos + train_neg\n",
        "test_x = test_pos + test_neg\n",
        "\n",
        "# avoid assumptions about the length of all_positive_tweets\n",
        "train_y = np.append(np.ones(len(train_pos)), np.zeros(len(train_neg)))\n",
        "test_y = np.append(np.ones(len(test_pos)), np.zeros(len(test_neg)))"
      ],
      "metadata": {
        "id": "3CcBdcjn_Apj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "custom_tweet = \"RT @Twitter @chapagain Hello There! Have a great day. :) #good #morning http://chapagain.com.np\"\n",
        "\n",
        "# print cleaned tweet\n",
        "print(process_tweet(custom_tweet))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uJttpc9-_JHf",
        "outputId": "31d880c5-8fe0-4e76-cb15-29a775e96470"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['hello', 'great', 'day', ':)', 'good', 'morn']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define count_tweets(results, tweets, ys):\n",
        "It will calculate the number of times each word appears in corpus"
      ],
      "metadata": {
        "id": "IqVnnPJ1XWuA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# UNQ_C1 GRADED FUNCTION: count_tweets\n",
        "\n",
        "def count_tweets(result, tweets, ys):\n",
        "    '''\n",
        "    Input:\n",
        "        result: a dictionary that will be used to map each pair to its frequency\n",
        "        tweets: a list of tweets\n",
        "        ys: a list corresponding to the sentiment of each tweet (either 0 or 1)\n",
        "    Output:\n",
        "        result: a dictionary mapping each pair to its frequency\n",
        "    '''\n",
        "    ### START CODE HERE ###\n",
        "    for y, tweet in zip(ys, tweets):\n",
        "        for word in process_tweet(tweet):\n",
        "            # define the key, which is the word and label tuple\n",
        "            pair = (word, y)\n",
        "            \n",
        "            # if the key exists in the dictionary, increment the count\n",
        "            if pair in result:\n",
        "                result[pair] += 1\n",
        "\n",
        "            # else, if the key is new, add it to the dictionary and set the count to 1\n",
        "            else:\n",
        "                result[pair] = 1\n",
        "    ### END CODE HERE ###\n",
        "\n",
        "    return result"
      ],
      "metadata": {
        "id": "8LBkpL2W_Tky"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing your function\n",
        "\n",
        "res = {}\n",
        "tweets = ['i am happy', 'i am tricked', 'i am sad', 'i am tired', 'i am tired']\n",
        "ys = [1, 0, 0, 0, 0]\n",
        "count_tweets(res, tweets, ys)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0M-mjmbZ_Xua",
        "outputId": "a578f1eb-c1be-437e-d5ca-8fa6b4665936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{('happi', 1): 1, ('trick', 0): 1, ('sad', 0): 1, ('tire', 0): 2}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = {}\n",
        "freqs = count_tweets(result, train_x, train_y)"
      ],
      "metadata": {
        "id": "c7iphtGyX7nQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Define train_naive_bayes(freqs, train_x, train_y)"
      ],
      "metadata": {
        "id": "zXQFx_4YXiHd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vocab = []\n",
        "for tweet in train_x:\n",
        "      for word in process_tweet(tweet):\n",
        "        if word not in vocab:\n",
        "          vocab.append(word)\n",
        "        else:\n",
        "          print\n",
        "V = len(vocab)\n",
        "print(vocab)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1cCDb1hk9Po",
        "outputId": "2f3009b2-31a6-4039-d36e-4418358febe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['followfriday', 'top', 'engag', 'member', 'commun', 'week', ':)', 'hey', 'jame', 'odd', ':/', 'pleas', 'call', 'contact', 'centr', '02392441234', 'abl', 'assist', 'mani', 'thank', 'listen', 'last', 'night', 'bleed', 'amaz', 'track', 'scotland', 'congrat', 'yeaaah', 'yipppi', 'accnt', 'verifi', 'rqst', 'succeed', 'got', 'blue', 'tick', 'mark', 'fb', 'profil', '15', 'day', 'one', 'irresist', 'flipkartfashionfriday', 'like', 'keep', 'love', 'custom', 'wait', 'long', 'hope', 'enjoy', 'happi', 'friday', 'lwwf', 'second', 'thought', '’', 'enough', 'time', 'dd', 'new', 'short', 'enter', 'system', 'sheep', 'must', 'buy', 'jgh', 'go', 'bayan', ':d', 'bye', 'act', 'mischiev', 'etl', 'layer', 'in-hous', 'wareh', 'app', 'katamari', 'well', '…', 'name', 'impli', ':p', 'influenc', 'big', '...', 'juici', 'selfi', 'follow', 'u', 'back', 'perfect', 'alreadi', 'know', \"what'\", 'great', 'opportun', 'junior', 'triathlet', 'age', '12', '13', 'gatorad', 'seri', 'get', 'entri', 'lay', 'greet', 'card', 'rang', 'print', 'today', 'job', ':-)', \"friend'\", 'lunch', 'yummm', 'nostalgia', 'tb', 'ku', 'id', 'conflict', 'help', \"here'\", 'screenshot', 'work', 'hi', 'liv', 'hello', 'need', 'someth', 'fm', 'twitter', '—', 'sure', 'thing', 'dm', 'x', \"i'v\", 'heard', 'four', 'season', 'pretti', 'dope', 'penthous', 'obv', 'gobigorgohom', 'fun', \"y'all\", 'yeah', 'suppos', 'lol', 'chat', 'bit', 'youth', '💅🏽', '💋', 'seen', 'year', 'rest', 'goe', 'quickli', 'bed', 'music', 'fix', 'dream', 'spiritu', 'ritual', 'festiv', 'népal', 'begin', 'line-up', 'left', 'see', 'sarah', 'send', 'us', 'email', 'bitsy@bitdefender.com', \"we'll\", 'asap', 'kik', 'hatessuc', '32429', 'kikm', 'lgbt', 'tinder', 'nsfw', 'akua', 'cumshot', 'come', 'hous', 'nsn_supplement', 'effect', 'press', 'releas', 'distribut', 'result', 'link', 'remov', 'pressreleas', 'newsdistribut', 'bam', 'bestfriend', 'lot', 'warsaw', '<3', 'x46', 'everyon', 'watch', 'documentari', 'earthl', 'youtub', 'support', 'buuut', 'oh', 'look', 'forward', 'visit', 'next', 'letsgetmessi', 'jo', 'make', 'feel', 'better', 'never', 'anyon', 'kpop', 'flesh', 'good', 'girl', 'best', 'wish', 'reason', 'epic', 'soundtrack', 'shout', 'ad', 'video', 'playlist', 'im', 'twitch', 'leagu', '1', '4', 'would', 'dear', 'jordan', 'okay', 'fake', 'gameplay', ';)', 'haha', 'kid', 'stuff', 'exactli', 'product', 'line', 'etsi', 'shop', 'check', 'boxroomcraft', 'vacat', 'recharg', 'normal', 'charger', 'asleep', 'talk', 'sooo', 'someon', 'text', 'ye', 'bet', \"he'll\", 'fit', 'hear', 'speech', 'piti', 'green', 'garden', 'midnight', 'sun', 'beauti', 'canal', 'dasvidaniya', 'till', 'scout', 'sg', 'futur', 'wlan', 'pro', 'confer', 'asia', 'chang', 'lollipop', '🍭', 'nez', 'agnezmo', 'oley', 'mama', 'stand', 'stronger', 'god', 'misti', 'babi', 'cute', 'woohoo', \"can't\", 'sign', 'yet', 'still', 'think', 'mka', 'liam', 'access', 'welcom', 'stat', 'arriv', 'unfollow', 'via', 'surpris', 'figur', 'happybirthdayemilybett', 'sweet', 'talent', '2', 'plan', 'drain', 'gotta', 'timezon', 'parent', 'proud', 'least', 'mayb', 'sometim', 'grade', 'al', 'grand', 'manila_bro', 'chosen', 'let', 'around', '..', 'side', 'world', 'eh', 'take', 'care', 'final', 'fuck', 'weekend', 'real', 'x45', 'join', 'hushedcallwithfraydo', 'gift', 'yeahhh', 'hushedpinwithsammi', 'event', 'might', 'luv', 'realli', 'appreci', 'share', 'wow', 'tom', '3', 'gym', 'monday', 'invit', 'scope', 'friend', 'nude', 'sleep', 'birthday', 'want', 't-shirt', 'cool', 'haw', 'phela', 'mom', 'obvious', 'princ', 'charm', 'stage', 'luck', 'tyler', 'hipster', 'glass', 'marti', 'glad', 'done', 'afternoon', 'read', 'kahfi', 'finish', 'ohmyg', 'yaya', 'dub', 'stalk', 'ig', 'gondooo', 'moo', 'tologooo', 'becom', 'detail', 'zzz', 'xx', 'physiotherapi', 'hashtag', '💪', 'monica', 'miss', 'sound', 'morn', \"that'\", 'x43', 'definit', 'tri', 'tonight', 'took', 'advic', 'treviso', 'concert', 'citi', 'countri', \"i'll\", 'start', 'fine', 'gorgeou', 'xo', 'oven', 'roast', 'garlic', 'oliv', 'oil', 'dri', 'tomato', 'basil', 'centuri', 'tuna', 'right', 'atchya', 'even', 'almost', 'chanc', 'cheer', 'po', 'ice', 'cream', 'agre', '100', 'heheheh', 'that', 'point', 'stay', 'home', 'soon', 'promis', 'web', 'whatsapp', 'volta', 'funcionar', 'com', 'iphon', 'jailbroken', 'later', '34', 'min', 'leia', 'appear', 'hologram', 'r2d2', 'w', 'messag', 'obi', 'wan', 'sit', 'luke', 'inter', 'ucl', 'arsen', 'small', 'team', 'pass', '🚂', 'dewsburi', 'railway', 'station', 'dew', 'west', 'yorkshir', '430', 'smh', '9:25', 'live', 'strang', 'imagin', 'megan', 'masaantoday', 'a4', 'shweta', 'tripathi', '5', '20', 'kurta', 'half', 'number', 'wsalelov', 'ah', 'larri', 'anyway', 'kinda', 'goood', 'life', 'enn', 'could', 'warmup', '15th', 'bath', 'dum', 'andar', 'ram', 'sampath', 'sona', 'mohapatra', 'samantha', 'edward', 'mein', 'tulan', 'razi', 'wah', 'josh', 'alway', 'smile', 'pictur', '16.20', 'giveitup', 'given', 'ga', 'subsidi', 'initi', 'propos', 'delight', 'yesterday', 'x42', 'lmaoo', 'song', 'ever', 'shall', 'littl', 'throwback', 'outli', 'island', 'cheung', 'chau', 'mui', 'wo', 'total', 'differ', 'kfckitchentour', 'kitchen', 'clean', \"i'm\", 'cusp', 'test', 'water', 'reward', 'arummzz', \"let'\", 'drive', 'travel', 'yogyakarta', 'jeep', 'indonesia', 'instamood', 'wanna', 'skype', 'may', 'nice', 'friendli', 'pretend', 'film', 'congratul', 'winner', 'cheesydelight', 'contest', 'address', 'guy', 'market', '24/7', 'regret', '14', 'hour', 'leav', 'without', 'delay', 'actual', 'easi', 'guess', 'train', 'wd', 'shift', 'engin', 'etc', 'sunburn', 'peel', 'blog', 'huge', 'warm', '☆', 'complet', 'triangl', 'northern', 'ireland', 'sight', 'smthng', 'fr', 'hug', 'xoxo', 'uu', 'jaann', 'topnewfollow', 'connect', 'wonder', 'made', 'fluffi', 'insid', 'pirouett', 'moos', 'trip', 'philli', 'decemb', \"i'd\", 'dude', 'x41', 'question', 'flaw', 'pain', 'negat', 'strength', 'went', 'solo', 'move', 'fav', 'nirvana', 'smell', 'teen', 'spirit', 'rip', 'ami', 'winehous', 'coupl', 'tomhiddleston', 'elizabetholsen', 'yaytheylookgreat', 'goodnight', 'vid', 'wake', 'gonna', 'shoot', 'itti', 'bitti', 'teeni', 'bikini', 'much', '4th', 'togeth', 'end', 'xfile', 'content', 'rain', 'fabul', 'fantast', '♡', 'jb', 'forev', 'belieb', 'nighti', 'bug', 'bite', 'bracelet', 'idea', 'foundri', 'game', 'sens', 'pic', 'ef', 'phone', 'woot', 'derek', 'use', 'parkshar', 'gloucestershir', 'aaaahhh', 'man', 'traffic', 'stress', 'reliev', \"how'r\", 'arbeloa', 'turn', '17', 'omg', 'say', 'europ', 'rise', 'find', 'hard', 'believ', 'uncount', 'coz', 'unlimit', 'cours', 'teamposit', 'aldub', '☕', 'rita', 'info', \"we'd\", 'way', 'boy', 'x40', 'true', 'sethi', 'high', 'exe', 'skeem', 'saam', 'peopl', 'polit', 'izzat', 'wese', 'trust', 'khawateen', 'k', 'sath', 'mana', 'kar', 'deya', 'sort', 'smart', 'hair', 'tbh', 'jacob', 'g', 'upgrad', 'tee', 'famili', 'person', 'two', 'convers', 'onlin', 'mclaren', 'fridayfeel', 'tgif', 'squar', 'enix', 'bissmillah', 'ya', 'allah', \"we'r\", 'socent', 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'backstori', 'gimm', 'meal', 'neat-o', 'wru', 'scissor', 'creation', 'amtir', 'imysm', 'tut', 'trop', 'tard', 'deadlin', 'st', 'premiun', 'notcool', '2/3', 'lahat', 'araw', 'nage', 'gyu', 'lmfaooo', 'mashup', 'eu', 'lc', 'yass', 'rel', 'sydney', 'perf', 'omfg', 'combat', 'dosent', \"sod'\", '20min', 'yahoo', 'yodel', 'jokingli', 'gahd', '26th', '12.00', 'obyun', 'wayhh', 'preval', 'controversi', '🍵', 'tube', 'meck', 'mcfc', 'ucan', 'poc', 'specif', 'sinhala', 'billionair', '1645', '1190', 'maldiv', 'dheena', 'fasgadah', 'alvadhaau', 'countdown', 'function', 'desktop', 'evelineconrad', 'kikmsn', 'selfshot', 'backkk', 'transfer', 'dull', 'overcast', 'missin', 'hangin', 'wiff', 'cherri', 'bakewel', 'teal', 'sect', 'tennunb', 'skip', 'doomsday', 'neglect', 'posti', 'bellami', 'raven', 'helmi', 'uh', 'cnt', 'whereisthesun', 'summerismiss', 'longgg', 'ridicul', 'stocko', 'lucozad', 'explos', 'beh', 'half-rememb', \"melody'\", 'recal', 'difficult', 'expo', 'jisoo', 'anon', 'mager', 'wi', 'wht', 'distant', 'insan', 'charli', 'gana', 'studio', 'arch', 'lyin', 'kian', 'supercar', 'gurgaon', 'locat', '9:15', 'satir', 'peanut', 'viner', 'palembang', 'sorrryyi', 'fani', 'boner', 'yuki', '2500k', 'jake', 'gyllenha', 'impact', \"ledger'\", 'b4', 'deplet', 'mbasa', 'aah', 'pa-copi', 'biom', 'mosqu', 'smelli', 'emptier', \"ciara'\", \"everything'\", 'tall', 'intent', 'amb', \"harry'\", 'mayday', 'parad', 'lyf', '13th', 'chri', 'brown', 'riski', 'cologn', 'duo', 'ballad', 'bish', 'yumyum', \"cathy'\", 'missyou', 'rubi', 'pora', 'karlia', 'khatam', 'bandi', '👑', 'pyaari', 'gawd', 'massi', 'thatselfiethough', 'loop', 'aishhh', 'viewer', 'sighsss', 'toffe', 'honesti', 'cheatday', 'protein', 'sissi', 'tote', 'slowli', 'church', 'pll', 'sel', 'serbia', 'serbian', 'selen', 'motav', 'zayyyn', 'happend', 'imper', 'pana', 'quickest', 'sake', 'hamstr', 'rodwel', 'trace', 'tp', 'powder', 'wider', 'bruno', '1.8', 'ed', 'croke', 'toll', 'shape', 'unluckiest', 'bettor', 'nstp', 'sem', 'chipotl', 'chick-fil-a', 'stole', 'ramadhan', 'stexpert', 'ripstegi', 'nickyyi', '¿', 'centralis', 'discontinu', 'sniff', \"i't\", 'theres', 'cred', 't_t', 'elimin', 'teamzip', 'smtm', 'assingn', 'editi', 'nakaka', 'beastmod', 'gaaawd', 'colombia', 'yot', 'labyo', 'pano', 'nalamannn', 'hardhead', \"zach'\", 'xpress', 'hopkin', 'melatonin', '2-4', 'hahaah', 'frequent', 'jail', 'weirddd', 'donghyuk', 'bek', 'reynoldsgrl', 'ole', 'beardi', 'kaussi', 'bummer', 'fightingmciren', \"michael'\", 'miser', '💦', '💃🏽', 'shouldv', 'saffron', 'peasant', 'wouldv', 'nfinit', 'admin_myung', 'slp', 'saddest', 'laomma', 'kebaya', 'bandung', '7df89150', '62', '08962464174', 'laomma_coutur', 'haizzz', 'urghhh', 'working-on-a-tight-schedul', 'ganbarimasu', 'livid', 'whammi', 'quuuee', 'friooo', 'stereo', 'chwang', 'lorm', '823', 'unhappi', 'lolzz', 'corey', 'mahirap', 'noodl', 'veeerri', 'orig', 'starholicxx', '07:17', '@the', 'notr', 'hwi', 'niall', 'fraud', 'diplomaci', 'fittest', 'zero', 'toler', 'pier', 'approach', 'rattl', 'robe', 'emphasi', 'chose', 'abby.can', 'persuad', 'lyric', \"emily'\", 'kamiss', 'mwa', 'cafe', 'melbourn', 'anyonnee', 'fricken', 'rito', 'friendzon', 'panel', 'hsm', 'canario', 'ukiss', 'kurt', \"fatma'm\", 'lmfao', 'flapjack', 'countthecost', 'ihop', 'infra', 'lq', 'sotir', 'mybrainneedstoshutoff', 'macci', '510', 'silicon', 'kbye', 'ini', 'citizen', 'mcountdown', '5h', 'thapelo', 'civ', 'wooden', 'mic', 'embarrass', 'mecha-totem', 'nak', 'tgk', 'townsss', 'jokid', 'rent', 'inconsider', 'softbal', 'tomcat', 'chel', 'jemma', 'matchi', 'elsa', 'postpon', 'karin', 'vist', 'unhealthi', 'propa', 'knockin', 'pre-holiday', 'meani', 'deathbybaconsmel', 'init', 'destin', 'victoria', 'luna', 'krystal', 'sarajevo', 'haix', 'sp', 'wii', 'bayonetta', 'doabl', 'drove', 'agenc', 'story.miss', 'everon', 'jp', 'mamabear', 'imintoh', 'underr', \"slovakia'\", 'd:', 'saklap', 'rizal', 'lib', 'advisori', 'period', 'dit', 'du', 'harsh', 'ohgod', 'abligaverin', 'sexygirlbypreciouslemmi', 'ripsandrabland', 'edel', 'salam', 'mubark', 'dong', 'tammirossm', 'speck', 'abbymil', 'ion', '5min', 'hse', 'noob', 'fck', 'nae', 'whit', 'van', 'bristol', 'subserv', 'oo', 'tub', 'penyfan', 'breconbeacon', 'tittheir', '42', 'hotti', 'fuzzi', 'antonio', 'kang', 'junhe', 'couldv', 'pz', 'somerset', 'sunburnt', 'safer', 'k3g', 'input', 'gamestomp', \"angelo'\", 'yna', 'fiver', 'sakho', 'threat', 'goalscor', '10:59', '11.00', 'sham', 'tricki', 'baao', 'nisrina', 'ladygaga', \"you'\", 'marrish', \"otp'\", 'edomnt', 'qih', 'shxb', 'chilton', 'creepi', 'boohoo', 'roar', 'victori', 'tweepsmatchout', 'nein', '404', 'willlow', 'sowwi', '3000', 'gear', '0.001', 'mode', '11:11', 'shanzay', 'salabrati', 'journo', 'lure', 'mashaket', 'bapak', 'prima', 'mune', '874', 'plisss', 'sunway', 'petal', 'jaya', 'selangor', 'huhuu', 'margo', 'konga', 'wa', 'ode', 'disvirgin', 'bride', 'yulin', 'imma', 'syawal', 'lapar', 'foundat', 'facil', 'dh', 'chalet', 'suay', 'anot', 'bugger', 'एक', 'बार', 'फिर', 'सेँ', 'धोखा', 'chandauli', 'majhwar', 'tito', 'tita', 'critic', 'steal', 'narco', 'regen', 'unfav', 'benadryl', 'arent', 'yg', 'gg', 'sxrew', 'dissappear', 'swap', 'ishal', 'thaank', 'jhezz', 'defenc', 'defens', 'nrltigersroost', 'indiana', 'hibb', 'biblethump', 'rlyyi', 'septum', 'pierc', 'venom', 'carriag', 'fur-trim', 'stetson', 'error', '59', 'xue', 'midori', 'sakit', 'mateo', 'bartend', 'despair', 'insta', 'iwantin', '___', 'fault', 'help@veryhq.co.uk', 'benedictervent', '221b', 'popcorn', 'joyc', 'ooop', 'paalam', 'sazbal', 'incid', 'aaahh', \"stomach'\", 'growl', 'beard', 'nooop', 'hundr', 'meg', \"verity'\", 'rupert', 'pleaaas', '👆🏻', 'woaah', 'solvo', 'twin', 'lego', 'barefoot', 'twelvyy', 'boaz', 'myhil', 'takeov', 'wba', \"taeyeon'\", 'derp', 'pd', 'zoom', \"sunny'\", 'besst', 'plagu', 'pit', 'frail', 'twurkin', 'razzist', 'tumblr', 'shek', '609', 'mugshot', 'plsss', 'taissa', 'farmiga', 'softwar', 'restor', 'momo', 'pharma', 'immov', 'messi', 'ansh', 'f1', 'rand', 'bein', 'tla', 'tweng', 'gene', 'up.com', 'counti', 'cooler', 'minhyuk', '1900', '😪', 'hz', 'emta', 'hatigii', 'b2aa', 'anesthesia', 'penrith', 'plain', 'untouch', 'brienn', 'lsh', 'gunna', 'former', 'darn', 'juudiciari', \"horton'\", 'dunkin', 'socialis', 'cara', \"delevingne'\", 'lace', 'fank', 'takfaham', 'ufff', 'sr', 'dard', 'katekyn', 'ehh', 'hacharatt', 'niwll', 'goa', 'lini', 'kasi', 'rhd', 'wae', 'subsid', '20th', 'youngja', 'harumph', 'soggi', 'sakura', 'flavour', 'chokki', '🌸', 'unavail', 'richard', 'satya', 'aditya', '🍜', 'vibrat', 'cu', 'dhaka', 'jam', 'cornetto', 'noseble', 'nintendo', 'wew', 'ramo', 'ground', 'shawn', 'mend', 'dinghi', 'skye', 'colleagu', 'gagal', 'txt', 'sim', 'nooot', 'notch', 'tht', 'starv', '\\U000fe196', 'pyjama', 'swifti', 'sorna', 'lurgi', '6gb', 'fenestoscop', 'etienn', 'bandana', 'vagina', 'suriya', 'dangl', 'mjhe', 'aaj', 'kisi', 'kiya', 'eyesight', '25x30', 'aftenoon', 'booor', 'boyfriend', 'garag', 'ce', 'gw', 'anatomi', 'no1', \"morisette'\", 'non-trial', 'sayhernam', 'lootcrat', 'inca', 'trail', 'sandboard', 'derbi', 'unabl', 'signatur', 'dish', 'unfamiliar', 'coldest', \"old'\", '14518344', '61', 'thirdwheel', 'lovebird', 'imo', '@juliettemaughan', 'sensiesha', 'eldest', '😟', 'keedz', 'taybigail', 'ps4', 'kink', 'streak', 'srski', 'tdc', 'in-sensit', 'cooper', 'thurston', 'collin', 'quietli', 'kennel', '911', 'pluckersss', 'gion', '886', 'kidschoiceaward', 'ming', 'pbr', 'periscop', 'ut', 'shawti', 'naw', \"sterling'\", '9muse', 'hrryok', 'wnt', '9:30', '9:48', '9/11', 'bueno', 'receptionist', 'ella', 'ketchup', 'tasteless', 'deantd', 'justgotkanekifi', 'notgonnabeactivefor', '2weeksdontmissittoomuch', '2013', 'vlog', 'turtl', 'cnn', 'straplin', 'theatr', 'guncontrol', 'stung', \"thát'\", 'powerpoint', 'diner', 'no-no', 'hind', 'circuit', 'secondari', 'sodder', 'mobitel', 'playstat', 'exp', 'misspelt', 'hyungwon', 'needicecreamnow', 'repeatedli', 'nu-uh', 'jace', 'mostest', 'urgh', \"grigson'\", 'carrot', '>:-(', 'ughh', 'otter', 'argh', 'pon', 'otl', 'sleepov', 'fabina', \"barrista'\", 'pup', 'brolli', 'dey', 'bitin', 'pretzel', 'bb17', 'bblf', 'fuckin', 'vanilla', 'latt', 'skulker', 'thread', 'pg279q', 'hungrrryyi', 'icloud', 'ipod', 'hallyu', 'über', 'oki', '8p', 'harlo', 'torrentialrain', 'lloyd', 'knowww', 'runni', 'sweater', 'intoler', 'xenophob', 'wtfff', 'tone', '1pm', 'newer', 'pish', 'comparison', 'remast', 'fe14', 'strawberri', 'kapatidkongpogi', 'mel', 'longest', 'carmen', 'login', '00128835', 'wingstop', 'budg', 'fuq', 'ilhoon', 'getthescoop', 'hearess', '677', 'txt_shot', 'standbi', 'inatal', 'zenmat', 'namecheck', 'whistl', 'junmyeon', 'ddi', 'arini', 'je', 'igbo', 'blamehoney', 'whhr', 'snuggl', 'usag', 'warn', 'vertigo', 'panic', 'dual', 'carriageway', 'aragalang', '08', 'tam', 'bose', 'theo', 'anymoree', 'cactu', 'sorrri', 'bowel', 'tumour', 'puffi', 'eyelid', 'musica', 'campsit', 'miah', 'hahay', 'churro', 'montana', 'reign', 'exampl', 'inflat', 'sic', 'reset', 'entlerbountli', 'dirtykik', 'sexcam', 'spray', 'swollen', 'postcod', 'kafi', 'mene', 'koi', 'rewert', 'bunta', 'warnaaa', 'tortur', 'iran', 'irand', 'us-iran', 'nuclear', \"mit'\", 'li', 's2e12', 'rumpi', 'gallon', 'dandia', 'rbi', 'cage', 'parrot', '1li', 'commiss', 'cag', 'stripe', 'ily.melani', 'unlik', 'deepxcap', 'doin', '5:08', 'thesi', 'gtg', 'compet', 'vv', 'opt-out', 'vam', 'spece', 'ell', 'sexyameli', 'fineandyu', 'imsorri', 'koe', 'emyu', 'confetti', 'sini', 'dipoppo', 'hop', 'bestweekend', 'okay-ish', 'html', 'geneva', 'patml', '482', 'abouti', '797', 'reaalli', 'meter', 'unansw', 'bri', 'magcon', 'sinuend', 'laper', 'rage', 'brendon', \"urie'\", 'sumer', 'repackag', \":'d\", 'yongb', 'sued', 'warm-up', 'rub', 'belli', 'jannatul', 'ferdou', 'ekta', 'kharap', 'manush', 'mart', 'gua', 'can', \"khloe'\", 'nhe', 'yar', 'minkyuk', 'hol', 'sensor', 'broker', 'wna', 'flaviana', 'chickmt', '123', 'letsfootbal', 'atk', 'greymind', 'gayl', 'mood-dump', 'livestream', 'felton', 'veriti', \"standen'\", '😆', 'takoyaki', 'aisyah', 'ffvi', 'youtu.be/2_gpctsojkw', 'donutsss', '50p', 'spars', 'lagi', 'rider', 'hueee', 'thingi', 'georg', 'chew', 'stella', 'theaccidentalcoupl', 'smooth', 'handov', 'spick', 'bebii', 'happenend', 'dr', 'balm', 'hmph', 'bubba', 'floor', 'oi', 'bengali', 'masterchef', 'whatchya', 'petrol', 'diesel', 'cock', 'nyquil', 'poootek', '1,500', 'bobbl', 'leak', 'thermo', 'tae', 'kita', 'ia', 'corrupt', 'anything.sur', 'octob', 'ene', '3k', 'zehr', 'khani', 'groceri', 'hubba', 'gum', 'closet', 'jhalak', 'bakwa', '. ...', 'seehiah', 'goy', 'nacho', 'braid', 'ruth', 'boong', 'gta', 'cwnt', 'trivia', 'rohingya', 'indict', 'thailand', 'savetherohingya', 'rumbl', 'kumbl', 'scold', 'phrase', 'tfw', 'jest', 'offend', 'sleepingwithsiren', '17th', 'bringmethehorizon', 'carva', 'regularli', 'revamp', 'headphon', 'wacha', 'niend', '2hr', '13m', 'kk', 'calibraksaep', 'darlin', \"doedn't\", 'meaning', 'horrif', 'scoup', 'sweedi', 'nam', \"sacconejoly'\", 'bethesda', 'fallout', 'minecon', 'katee', 'iloveyouu', 'linux', 'nawww', 'chikka', 'ug', 'rata', 'soonest', 'mwamwa', 'faggot', 'fyi', 'nicest', 'mehendi', 'dash', 'bookmark', 'whay', 'shaa', 'prami', '😚', 'ngee', 'crikey', 'snit', 'nathanielhinanakit', 'naya', 'spinni', 'wheel', 'albeit', 'athlet', 'gfriend', 'fugli', 'jongda', 'tlist', 'budget', 'pabebegirl', 'pabeb', 'sandra', 'bland', 'storifi', 'mtvhottest', 'gaga', '😵', 'hulkamania', 'unlov', 'ihhh', 'stackar', 'remedi', 'ov', 'raiz', 'nvr', 'gv', 'up.wt', 'wt', 'thr', 'soln', \"sister'\", 'pipe', 'lawn', \"cupid'\", 'torn', 'retain', 'clown', 'lipstick', 'haiss', 'todayi', 'thoo', 'everday', 'hangout', 'steven', 'william', 'umboh', 'jadin', 'thiz', 'iz', 'emeg', 'kennat', 'abi', 'arctic', 'chicsirif', 'cumbia', 'badlif', '4-5', 'kaslkdja', '3wk', 'feverfew', 'weddingflow', 'diyflow', 'fitn', 'wolverin', 'innoc', '🙏🏻', '🎂', 'memem', 'krystoria', 'snob', 'zumba', 'greekcrisi', 'remain', 'dutch', 'legibl', 'isra', 'passport', 'froze', '23rd', 'stomachach', 'ཀ', 'again', 'otani', '3-0', 'niaaa', '2/4', 'scheme', 'fckin', 'vin', 'plss', 'rpli', 'rat', 'mac', 'backup', 'lune', 'robinhood', 'robinhoodi', '🚙', 'docopenhagen', 'setter', 'swipe', 'bbygurl', 'caribbean', '6yr', 'takraw', 'fersure', 'angi', 'sheriff', 'aaag', \"i'mo\", 'sulk', 'selfish', 'nonc', 'bison', 'motiv', \"q'don\", 'cheat', 'stomp', 'aaaaaaaaah', 'kany', 'jdjdjdjd', \"jimin'\", 'fancaf', 'waffl', '87.7', '2fm', 'himseek', 'kissm', 'glo', 'cori', 'monteith', 'hashbrown', 'pg', 'msc', 'hierro', 'shirleycam', 'gilet', 'cheek', 'squishi', 'lahhh', 'eon', 'sunris', 'beeti', '697', 'getaway', 'crimin', '☼', 'amiibo', 'habe', 'siannn', 'chuckin', 'ampsha', 'nia', 'strap', 'dz9055', 'entlead', '590', '07:02', 'ifsc', 'mayor', 'biodivers', 'taxonom', 'collabor', 'speci', 'suppl', 'collar', '3:03', 'belt', 'smith', 'eyelin', 'therefor', 'netherland', 'el', 'jeb', 'blacklivesmatt', 'slogan', 'msnbc', 'jebbush', 'famish', 'marino', 'qualifi', 'suzi', 'skirt', 'tama', 'warrior', 'wound', 'iraq', 'camara', 'coveral', 'inspi', 'sneezi', 'rogerwatch', 'stalker', 'velvet', 'tradit', 'beheaviour', \"robert'\", '.\\n.', 'aaron', 'jelous', 'mtg', 'thoughtseiz', 'playabl', 'oldi', 'goodi', 'mcg', 'inspirit', 'ise', 'assum', 'waist', 'guin', 'venu', 'pepper', 'thessidew', '877', 'genesi', 'novemb', 'mash', 'whattsap', 'inuyasha', 'outfwith', 'myungsoo', 'satisfi', 'challo', 'pliss', 'juliana', 'enrol', 'darlen', 'emoji', 'brisban', 'merlin', 'nawwwe', 'hyperbulli', 'tong', 'nga', 'seatmat', 'rajud', 'ore', 'kayla', 'ericavan', 'jong', 'dongwoo', 'photocard', 'wh', 'dw', 'tumor', 'vivian', 'mmsmalubhangsakit', 'jillcruz', 'qt', '19th', 'co-work', 'unsettl', 'gh', '18c', 'rlli', 'hamster', 'sheeran', 'preform', 'monash', 'hitmark', 'glitch', 'safaa', \"selena'\", 'galat', 'tum', 'lrka', 'bna', 'bhook', 'afterschool', 'bilal', 'ashraf', 'icu', 'annnd', 'winchest', '{:', 'grepe', 'grepein', 'panem', 'sulli', 'cpm', 'condemn', '✔', 'occur', 'unagi', '7elw', 'mesh', 'beyt', '3a2ad', 'fluent', 'varsiti', 'sengenza', 'movnat', 'yield', 'nbhero', 'agov', 'brasileirao', 'abus', 'unpar', 'bianca', 'bun', 'dislik', 'burdensom', 'amelia', 'melon', 'soccer']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# UNQ_C2 GRADED FUNCTION: train_naive_bayes\n",
        "\n",
        "def train_naive_bayes(freqs, train_x, train_y):\n",
        "    '''\n",
        "    Input:\n",
        "        freqs: dictionary from (word, label) to how often the word appears\n",
        "        train_x: a list of tweets\n",
        "        train_y: a list of labels correponding to the tweets (0,1)\n",
        "    Output:\n",
        "        logprior: the log prior. (equation 3 above)\n",
        "        loglikelihood: the log likelihood of you Naive bayes equation. (equation 6 above)\n",
        "    '''\n",
        "    loglikelihood = {}\n",
        "    logprior = 0\n",
        "    vocab = []\n",
        "    for tweet in train_x:\n",
        "      for word in process_tweet(tweet):\n",
        "        if word not in vocab:\n",
        "          vocab.append(word)\n",
        "        else:\n",
        "          print\n",
        "    V = len(vocab)\n",
        "\n",
        "    # calculate N_pos, N_neg, V_pos, V_neg\n",
        "    N_pos = N_neg = 0\n",
        "    for pair in freqs.keys():\n",
        "        # if the label is positive (greater than zero)\n",
        "        if pair[1] > 0:\n",
        "          # Increment the number of positive words by the count for this (word, label) pair\n",
        "          N_pos += freqs[pair]\n",
        "\n",
        "        # else, the label is negative\n",
        "        else:\n",
        "          # increment the number of negative words by the count for this (word,label) pair\n",
        "          N_neg += freqs[pair]\n",
        "    # For each word in the vocabulary...\n",
        "    freq_pos = freq_neg = 0\n",
        "    for word in vocab:\n",
        "      # get the positive and negative frequency of the word\n",
        "      if (word, 1.0) in freqs.keys():\n",
        "        freq_pos = freqs[word, 1.0] \n",
        "    \n",
        "      elif (word, 0.0) in freqs.keys():       \n",
        "        freq_neg = freqs[word, 0.0]\n",
        "\n",
        "        # calculate the probability that each word is positive, and negative\n",
        "      p_w_pos = (freq_pos+1)/(N_pos+V)\n",
        "      p_w_neg = (freq_neg+1)/(N_neg+V)\n",
        "      \n",
        "        # calculate the log likelihood of the word\n",
        "      loglikelihood[word] = math.log(((p_w_pos)/(p_w_neg)),10)\n",
        "    ### END CODE HERE ###\n",
        "\n",
        "    return loglikelihood"
      ],
      "metadata": {
        "id": "zlBjPyO8_e_y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Predict"
      ],
      "metadata": {
        "id": "ZhPIFdtnXnLK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#make prediction\n",
        "def predict(tweet):\n",
        "  word_l = process_tweet(tweet)\n",
        "  p = 0\n",
        "  for word in word_l:\n",
        "    if word in loglikelihood:\n",
        "      p += loglikelihood[word]\n",
        "  # if the loglikelihood of words is > one then it is a positive tweet\n",
        "  if p > 1:\n",
        "    print(f'The sentence is positive\\n{p}')\n",
        "  else: print(f'The sentence is negative\\n{p}')"
      ],
      "metadata": {
        "id": "0iGeGjvA6_0j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# UNQ_C3 (UNIQUE CELL IDENTIFIER, DO NOT EDI\n",
        "loglikelihood = train_naive_bayes(freqs, train_x, train_y)\n",
        "print(len(loglikelihood))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYa19EB0_kNI",
        "outputId": "63158ab9-5f0b-4a9c-e075-f89e635dfec8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9162\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweet = \"idiot\"\n",
        "predict(tweet)"
      ],
      "metadata": {
        "id": "aLvrOf4U7wTO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e84aba2c-db01-43d6-d68d-f23b52c15a2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The sentence is negative\n",
            "0.29618759622856994\n"
          ]
        }
      ]
    }
  ]
}