Day 95.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Twitter Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the libraries\n",
    "import tweepy\n",
    "import re\n",
    "import pickle\n",
    "from tweepy import OAuthHandler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initializing the keys\n",
    "consumer_key = 'yoIwFkjZGYDa49aO16XqSNqcN'\n",
    "consumer_secret = 'gl4LQOItV7Z1aFwNrlvaiKJ3t8o8h99blMIAmnmdHxYjzjRAxO' \n",
    "access_token = '624310916-E7fDF2IE8P6bfY1oVFglASf6F8RnxMd3vgSXFqnZ'\n",
    "access_secret ='ID9JcoXHsDcKtvNcnmBGcCQhUlO0wmwAxBJ6LCesiUAas'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# client authentication\n",
    "auth = OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "args = ['google'] \n",
    "api = tweepy.API(auth, timeout=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fetching real time tweets\n",
    "list_tweets = []\n",
    "query = args[0]\n",
    "if len(args) == 1:\n",
    "    for status in tweepy.Cursor(api.search, q=query+\" -filter:retweets\", lang='eng', result_type='recent').items(100):\n",
    "        list_tweets.append(status.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading TF-IDF model \n",
    "with open('tfidfmodel.pickle', 'rb') as f:\n",
    "    vectorizer = pickle.load(f)\n",
    "    \n",
    "with open('classifier.pickle', 'rb') as f:\n",
    "    clf = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_pos = 0\n",
    "total_neg = 0\n",
    "\n",
    "# Preprocessing the tweets and predicting sentiment\n",
    "for tweet in list_tweets:\n",
    "    tweet = re.sub(r\"^https://t.co/[a-zA-Z0-9]*\\s\", \" \", tweet)\n",
    "    tweet = re.sub(r\"\\s+https://t.co/[a-zA-Z0-9]*\\s\", \" \", tweet)\n",
    "    tweet = re.sub(r\"\\s+https://t.co/[a-zA-Z0-9]*$\", \" \", tweet)\n",
    "    tweet = tweet.lower()\n",
    "    tweet = re.sub(r\"that's\",\"that is\",tweet)\n",
    "    tweet = re.sub(r\"there's\",\"there is\",tweet)\n",
    "    tweet = re.sub(r\"what's\",\"what is\",tweet)\n",
    "    tweet = re.sub(r\"where's\",\"where is\",tweet)\n",
    "    tweet = re.sub(r\"it's\",\"it is\",tweet)\n",
    "    tweet = re.sub(r\"who's\",\"who is\",tweet)\n",
    "    tweet = re.sub(r\"i'm\",\"i am\",tweet)\n",
    "    tweet = re.sub(r\"she's\",\"she is\",tweet)\n",
    "    tweet = re.sub(r\"he's\",\"he is\",tweet)\n",
    "    tweet = re.sub(r\"they're\",\"they are\",tweet)\n",
    "    tweet = re.sub(r\"who're\",\"who are\",tweet)\n",
    "    tweet = re.sub(r\"ain't\",\"am not\",tweet)\n",
    "    tweet = re.sub(r\"wouldn't\",\"would not\",tweet)\n",
    "    tweet = re.sub(r\"shouldn't\",\"should not\",tweet)\n",
    "    tweet = re.sub(r\"can't\",\"can not\",tweet)\n",
    "    tweet = re.sub(r\"couldn't\",\"could not\",tweet)\n",
    "    tweet = re.sub(r\"won't\",\"will not\",tweet)\n",
    "    tweet = re.sub(r\"\\W\",\" \",tweet)\n",
    "    tweet = re.sub(r\"\\d\",\" \",tweet)\n",
    "    tweet = re.sub(r\"\\s+[a-z]\\s+\",\" \",tweet)\n",
    "    tweet = re.sub(r\"\\s+[a-z]$\",\" \",tweet)\n",
    "    tweet = re.sub(r\"^[a-z]\\s+\",\" \",tweet)\n",
    "    tweet = re.sub(r\"\\s+\",\" \",tweet)\n",
    "    clf.predict(vectorizer.transform([tweet]).toarray())\n",
    "    if sent[0] == 1:\n",
    "        total_pos += 1\n",
    "    else:\n",
    "        total_neg += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the Result\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "objects = ['Positive','Negative']\n",
    "y_pos = np.arange(len(objects))\n",
    "\n",
    "plt.bar(y_pos,[total_pos,total_neg],alpha=0.5)\n",
    "plt.xticks(y_pos,objects)\n",
    "plt.ylabel('Number')\n",
    "plt.title('Number of Postive and NEgative Tweets')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}