sentiment.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "sentiment.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPfYy8Xq8HSsQmv7tK5AKbD",
      "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/tejas2008/FakeAccountDetection/blob/master/sentiment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRgeFA_qzBTi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "19454ba0-eeec-44c7-a8e4-d09d856c8c23"
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import word_tokenize\n",
        "import re\n",
        "from nltk.tokenize import TweetTokenizer\n",
        "tknzr = TweetTokenizer(strip_handles=True)\n",
        "from nltk.stem import PorterStemmer "
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "keRQV-lx0Phz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "09e78166-9456-4246-f705-dafedb95ea5a"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from nltk.corpus import twitter_samples \n",
        "from nltk.corpus import stopwords \n",
        "nltk.download('twitter_samples')\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqmjgzaS0ZZo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "positive_tweets = twitter_samples.strings('positive_tweets.json')\n",
        "negative_tweets = twitter_samples.strings('negative_tweets.json')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UmssnySR0dai",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "91f7db3f-269c-4bfd-9be3-279674cba092"
      },
      "source": [
        "len(positive_tweets)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9z3p1Bl1Bk4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "test_pos = positive_tweets[4000:]\n",
        "train_pos = positive_tweets[:4000]\n",
        "test_neg = negative_tweets[4000:]\n",
        "train_neg = negative_tweets[:4000]\n",
        "train_x = train_pos + train_neg \n",
        "test_x = test_pos + test_neg"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cX9Vm1_J22L0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a6e240c0-cb04-434e-a68f-12f901130721"
      },
      "source": [
        "len(train_x)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkddCO0l3DQw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_y = np.append(np.ones((len(train_pos), 1)), np.zeros((len(train_neg), 1)), axis=0)\n",
        "test_y = np.append(np.ones((len(test_pos), 1)), np.zeros((len(test_neg), 1)), axis=0)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fx7yf5v14Pgz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "cc808796-fec3-48ca-ac43-728a1e542f52"
      },
      "source": [
        "train_y"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       ...,\n",
              "       [0.],\n",
              "       [0.],\n",
              "       [0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mOiiLTm4RGn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stemmer = PorterStemmer()\n",
        "def preprocess(tweet):\n",
        "  punc = '''!()-[]{};:'\"\\, <>./?@#$%^&*'''\n",
        "  tweet = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', tweet)\n",
        "  tweet = re.sub(r'#', '', tweet)\n",
        "  tokn_word = tknzr.tokenize(tweet)\n",
        "  stp_words = set(stopwords.words('english')) \n",
        "  out = []\n",
        "  for i in tokn_word:\n",
        "    if i not in stp_words and i not in punc:\n",
        "      j = stemmer.stem(i)\n",
        "      out.append(j)\n",
        "  return out\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LY0l1Lkg7z1W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "287815a5-a7d1-496f-f56a-4e01209ee43b"
      },
      "source": [
        "for i in range(len(train_x)):\n",
        "  train_x[i] = preprocess(train_x[i])\n",
        "print(train_x[0])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['followfriday', 'top', 'engag', 'member', 'commun', 'week', ':)']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHgKM-rTIq4L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "8975b806-38c1-401d-8fbb-c9d96c7e0984"
      },
      "source": [
        "fre = {}\n",
        "def frequency(l):\n",
        "  freqs = {}\n",
        "  for i in range(4000):\n",
        "    for word in l[i]:\n",
        "      if (word,1) in freqs:\n",
        "        freqs[(word,1)] += 1\n",
        "      else:\n",
        "        freqs[(word,1)] = 1 \n",
        "  for i in range(4000,8000):\n",
        "    for word in l[i]:\n",
        "      if (word,0) in freqs:\n",
        "        freqs[(word,0)] += 1\n",
        "      else:\n",
        "        freqs[(word,0)] = 1 \n",
        "  return freqs\n",
        "fre = frequency(train_x)\n",
        "print(fre)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{('followfriday', 1): 23, ('top', 1): 30, ('engag', 1): 7, ('member', 1): 14, ('commun', 1): 27, ('week', 1): 72, (':)', 1): 2847, ('hey', 1): 60, ('jame', 1): 7, ('how', 1): 18, ('odd', 1): 2, (':/', 1): 5, ('pleas', 1): 80, ('call', 1): 27, ('contact', 1): 4, ('centr', 1): 1, ('02392441234', 1): 1, ('abl', 1): 6, ('assist', 1): 1, ('mani', 1): 28, ('thank', 1): 504, ('listen', 1): 14, ('last', 1): 39, ('night', 1): 55, ('As', 1): 3, ('you', 1): 95, ('bleed', 1): 2, ('amaz', 1): 41, ('track', 1): 5, ('when', 1): 10, ('scotland', 1): 2, ('congrat', 1): 15, ('yeaaaah', 1): 1, ('yippppi', 1): 1, ('accnt', 1): 2, ('verifi', 1): 2, ('rqst', 1): 1, ('succeed', 1): 1, ('got', 1): 57, ('blue', 1): 8, ('tick', 1): 1, ('mark', 1): 1, ('fb', 1): 1, ('profil', 1): 2, ('15', 1): 4, ('day', 1): 187, ('thi', 1): 31, ('one', 1): 90, ('irresist', 1): 2, ('flipkartfashionfriday', 1): 16, ('We', 1): 65, ('like', 1): 187, ('keep', 1): 55, ('love', 1): 336, ('custom', 1): 4, ('wait', 1): 55, ('long', 1): 27, ('hope', 1): 113, ('enjoy', 1): 57, ('happi', 1): 161, ('friday', 1): 91, ('lwwf', 1): 1, ('On', 1): 8, ('second', 1): 8, ('thought', 1): 21, ('’', 1): 17, ('enough', 1): 16, ('time', 1): 100, ('DD', 1): 1, ('but', 1): 30, ('new', 1): 116, ('short', 1): 6, ('enter', 1): 9, ('system', 1): 2, ('sheep', 1): 1, ('must', 1): 14, ('buy', 1): 9, ('jgh', 1): 4, ('go', 1): 112, ('bayan', 1): 1, (':D', 1): 498, ('bye', 1): 5, ('act', 1): 6, ('mischiev', 1): 1, ('etl', 1): 1, ('layer', 1): 1, ('in-hous', 1): 1, ('wareh', 1): 1, ('app', 1): 12, ('katamari', 1): 1, ('well', 1): 66, ('…', 1): 31, ('name', 1): 12, ('impli', 1): 1, (':p', 1): 103, ('influenc', 1): 16, ('who', 1): 10, (\"wouldn't\", 1): 3, ('these', 1): 6, ('big', 1): 27, ('...', 1): 227, ('juici', 1): 3, ('selfi', 1): 11, ('follow', 1): 319, ('perfect', 1): 17, ('alreadi', 1): 19, ('know', 1): 120, (\"what'\", 1): 14, ('great', 1): 134, ('opportun', 1): 17, ('junior', 1): 2, ('triathlet', 1): 1, ('age', 1): 2, ('12', 1): 5, ('13', 1): 5, ('gatorad', 1): 1, ('seri', 1): 4, ('get', 1): 164, ('entri', 1): 3, ('lay', 1): 3, ('greet', 1): 4, ('card', 1): 6, ('rang', 1): 2, ('print', 1): 3, ('today', 1): 86, ('job', 1): 34, (':-)', 1): 543, (\"friend'\", 1): 3, ('lunch', 1): 3, ('yummmm', 1): 1, ('nostalgia', 1): 1, ('tb', 1): 1, ('KU', 1): 1, ('id', 1): 4, ('conflict', 1): 1, ('help', 1): 37, (\"here'\", 1): 20, ('screenshot', 1): 2, ('work', 1): 88, ('Hi', 1): 130, ('liv', 1): 2, ('hello', 1): 49, ('I', 1): 720, ('need', 1): 62, ('someth', 1): 25, ('u', 1): 123, ('fm', 1): 2, ('twitter', 1): 25, ('—', 1): 22, ('sure', 1): 37, ('thing', 1): 48, ('dm', 1): 17, ('x', 1): 46, (\"i'v\", 1): 25, ('heard', 1): 9, ('four', 1): 5, ('season', 1): 5, ('pretti', 1): 17, ('dope', 1): 2, ('penthous', 1): 1, ('obv', 1): 1, ('gobigorgohom', 1): 1, ('have', 1): 75, ('fun', 1): 45, (\"y'all\", 1): 3, ('yeah', 1): 30, ('suppos', 1): 6, ('lol', 1): 48, ('chat', 1): 9, ('bit', 1): 16, ('youth', 1): 14, ('💅', 1): 1, ('🏽', 1): 1, ('💋', 1): 2, ('seen', 1): 6, ('year', 1): 33, ('rest', 1): 9, ('goe', 1): 4, ('quickli', 1): 3, ('bed', 1): 8, ('music', 1): 15, ('fix', 1): 6, ('dream', 1): 17, ('spiritu', 1): 1, ('ritual', 1): 1, ('festiv', 1): 7, ('népal', 1): 1, ('begin', 1): 4, ('line-up', 1): 4, ('It', 1): 33, ('left', 1): 10, ('see', 1): 156, ('sarah', 1): 4, ('send', 1): 17, ('us', 1): 85, ('email', 1): 22, ('bitsy@bitdefender.com', 1): 1, (\"we'll\", 1): 12, ('asap', 1): 5, ('MY', 1): 10, ('kik', 1): 16, ('hatessuc', 1): 1, ('32429', 1): 1, ('kikm', 1): 1, ('lgbt', 1): 2, ('tinder', 1): 1, ('nsfw', 1): 1, ('akua', 1): 1, ('cumshot', 1): 1, ('come', 1): 63, ('hous', 1): 5, ('nsn_supplement', 1): 1, ('effect', 1): 2, ('press', 1): 1, ('releas', 1): 11, ('distribut', 1): 1, ('result', 1): 2, ('link', 1): 13, ('remov', 1): 3, ('pressreleas', 1): 1, ('newsdistribut', 1): 1, ('bam', 1): 44, ('can', 1): 57, ('bestfriend', 1): 50, ('she', 1): 50, ('lot', 1): 80, ('warsaw', 1): 44, ('<3', 1): 118, ('x46', 1): 1, ('everyon', 1): 45, ('watch', 1): 32, ('documentari', 1): 1, ('earthl', 1): 1, ('youtub', 1): 8, ('support', 1): 25, ('buuuuuuuut', 1): 1, ('oh', 1): 20, ('look', 1): 109, ('forward', 1): 20, ('visit', 1): 25, ('next', 1): 37, ('letsgetmessi', 1): 1, ('Jo', 1): 1, ('make', 1): 69, ('feel', 1): 33, ('better', 1): 40, ('never', 1): 31, ('anyon', 1): 7, ('kpop', 1): 1, ('flesh', 1): 1, ('good', 1): 191, ('girl', 1): 34, ('best', 1): 49, ('wish', 1): 29, ('A', 1): 29, ('reason', 1): 10, ('epic', 1): 1, ('soundtrack', 1): 1, ('shout', 1): 11, ('ad', 1): 10, ('video', 1): 29, ('playlist', 1): 5, ('would', 1): 70, ('dear', 1): 15, ('jordan', 1): 1, ('okay', 1): 31, ('fake', 1): 1, ('gameplay', 1): 1, (';)', 1): 22, ('haha', 1): 44, ('Im', 1): 12, ('kid', 1): 13, ('im', 1): 25, ('stuff', 1): 11, ('exactli', 1): 5, ('our', 1): 10, ('product', 1): 11, ('line', 1): 6, ('etsi', 1): 1, ('shop', 1): 12, ('check', 1): 38, ('vacat', 1): 5, ('recharg', 1): 1, ('normal', 1): 5, ('charger', 1): 2, ('asleep', 1): 7, ('talk', 1): 37, ('sooo', 1): 3, ('someon', 1): 29, ('text', 1): 12, ('ye', 1): 59, ('bet', 1): 6, (\"he'll\", 1): 2, ('fit', 1): 2, ('hear', 1): 24, ('speech', 1): 1, ('piti', 1): 2, ('green', 1): 2, ('garden', 1): 5, ('midnight', 1): 1, ('sun', 1): 6, ('beauti', 1): 45, ('canal', 1): 1, ('dasvidaniya', 1): 1, ('till', 1): 16, ('scout', 1): 1, ('SG', 1): 1, ('futur', 1): 9, ('wlan', 1): 1, ('pro', 1): 4, ('confer', 1): 1, ('asia', 1): 1, ('chang', 1): 20, ('lollipop', 1): 1, ('🍭', 1): 1, ('nez', 1): 1, ('agnezmo', 1): 1, ('oley', 1): 1, ('mama', 1): 1, ('stand', 1): 6, ('stronger', 1): 1, ('OH', 1): 1, ('god', 1): 14, ('misti', 1): 1, ('babi', 1): 17, ('IS', 1): 5, ('SO', 1): 7, ('cute', 1): 21, ('woohoo', 1): 3, (\"can't\", 1): 31, ('sign', 1): 9, ('yet', 1): 12, ('still', 1): 37, ('think', 1): 48, ('mka', 1): 5, ('liam', 1): 5, ('access', 1): 3, ('welcom', 1): 54, ('stat', 1): 51, ('arriv', 1): 57, ('1', 1): 60, ('NO', 1): 52, ('unfollow', 1): 53, ('via', 1): 60, ('surpris', 1): 10, ('figur', 1): 5, ('happybirthdayemilybett', 1): 1, ('sweet', 1): 16, ('talent', 1): 4, ('2', 1): 41, ('plan', 1): 21, ('drain', 1): 1, ('gotta', 1): 4, ('timezon', 1): 1, ('parent', 1): 4, ('proud', 1): 11, ('least', 1): 14, ('mayb', 1): 17, ('sometim', 1): 11, ('grade', 1): 4, ('al', 1): 1, ('grand', 1): 4, ('manila_bro', 1): 1, ('chosen', 1): 1, ('let', 1): 57, ('around', 1): 14, ('..', 1): 100, ('side', 1): 13, ('world', 1): 23, ('eh', 1): 1, ('take', 1): 30, ('care', 1): 12, ('final', 1): 24, ('fuck', 1): 20, ('weekend', 1): 61, ('real', 1): 18, ('x45', 1): 1, ('join', 1): 21, ('hushedcallwithfraydo', 1): 1, ('gift', 1): 7, ('yeahhh', 1): 1, ('DM', 1): 14, ('hushedpinwithsammi', 1): 2, ('event', 1): 7, ('might', 1): 21, ('luv', 1): 4, ('U', 1): 13, ('realli', 1): 66, ('appreci', 1): 28, ('share', 1): 41, ('wow', 1): 14, ('that', 1): 30, ('tom', 1): 5, ('gym', 1): 3, ('monday', 1): 7, ('invit', 1): 15, ('scope', 1): 5, ('those', 1): 1, ('friend', 1): 40, ('nude', 1): 1, ('sleep', 1): 35, ('My', 1): 48, ('birthday', 1): 53, ('want', 1): 69, ('t-shirt', 1): 2, ('they', 1): 10, ('cool', 1): 29, ('haw', 1): 1, ('phela', 1): 1, ('mom', 1): 7, ('obvious', 1): 1, ('princ', 1): 1, ('charm', 1): 1, ('stage', 1): 2, ('luck', 1): 26, ('tyler', 1): 1, ('hipster', 1): 1, ('glass', 1): 3, ('marti', 1): 2, ('glad', 1): 41, ('done', 1): 40, ('it', 1): 15, ('afternoon', 1): 7, ('read', 1): 27, ('Al', 1): 1, ('kahfi', 1): 1, ('finish', 1): 15, ('ohmyg', 1): 1, ('yaya', 1): 3, ('dub', 1): 1, ('stalk', 1): 2, ('ig', 1): 1, ('gondooo', 1): 1, ('moo', 1): 2, ('tologooo', 1): 1, ('becom', 1): 8, ('detail', 1): 8, ('zzz', 1): 1, ('xx', 1): 32, ('physiotherapi', 1): 1, ('hashtag', 1): 3, ('💪', 1): 1, ('monica', 1): 1, ('miss', 1): 17, ('sound', 1): 20, ('morn', 1): 68, (\"that'\", 1): 49, ('x43', 1): 1, ('definit', 1): 20, ('tri', 1): 34, ('tonight', 1): 15, ('took', 1): 7, ('advic', 1): 6, ('treviso', 1): 1, ('concert', 1): 23, ('citi', 1): 26, ('countri', 1): 22, 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('19th', 0): 1, ('toss', 0): 1, ('co-work', 0): 1, ('mia', 0): 1, ('push', 0): 2, ('dare', 0): 1, ('unsettl', 0): 1, ('GH', 0): 1, ('18c', 0): 1, ('rlli', 0): 1, ('hamster', 0): 2, ('sheeran', 0): 1, ('preform', 0): 1, ('monash', 0): 1, ('hitmark', 0): 1, ('glitch', 0): 1, ('safaa', 0): 1, (\"selena'\", 0): 1, ('galat', 0): 1, ('tum', 0): 1, ('ab', 0): 2, ('non', 0): 1, ('lrka', 0): 1, ('bna', 0): 1, ('kia', 0): 1, ('bhook', 0): 1, ('jai', 0): 1, ('social', 0): 1, ('afterschool', 0): 1, ('bilal', 0): 1, ('ashraf', 0): 1, ('icu', 0): 1, ('thankssss', 0): 1, ('annnd', 0): 1, ('winchest', 0): 1, ('{:', 0): 1, ('grepe', 0): 1, ('grepein', 0): 1, ('panem', 0): 1, ('lover', 0): 1, ('sulli', 0): 1, ('f', 0): 1, ('cpm', 0): 1, ('condemn', 0): 1, ('✔', 0): 1, ('occur', 0): 1, ('unagi', 0): 1, ('7elw', 0): 1, ('mesh', 0): 1, ('beyt', 0): 1, ('3a2ad', 0): 1, ('fluent', 0): 1, ('varsiti', 0): 1, ('sengenza', 0): 1, ('context', 0): 1, ('movnat', 0): 1, ('yield', 0): 1, ('nbhero', 0): 1, (\"it'd\", 0): 1, ('background', 0): 1, ('agov', 0): 1, ('brasileirao', 0): 1, ('abus', 0): 1, ('unpar', 0): 1, ('bianca', 0): 1, ('Ur', 0): 1, ('bun', 0): 1, ('dislik', 0): 1, ('burdensom', 0): 1, ('clear', 0): 1, ('amelia', 0): 1, ('melon', 0): 1, ('useless', 0): 1, ('soccer', 0): 1, ('interview', 0): 2, ('thursday', 0): 1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1mTQYLKmmIC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def vec_for_model(fre,l):\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}