{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Classfier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/uzaycetin/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import re\n",
    "import pickle \n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.datasets import load_files\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using our classifier\n",
    "with open('pre-trained-model/tfidfmodel.pickle','rb') as f:\n",
    "    tfidf = pickle.load(f)\n",
    "    \n",
    "with open('pre-trained-model/classifier.pickle','rb') as f:\n",
    "    clf = pickle.load(f)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test on a new Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = [\"You are a nice person man, have a good life\"]\n",
    "sample = tfidf.transform(sample).toarray()\n",
    "sentiment = clf.predict(sample)\n",
    "sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = [\"Logistic regression is not good! \"]\n",
    "sample = tfidf.transform(sample).toarray()\n",
    "sentiment = clf.predict(sample)\n",
    "sentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test on Twitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import tweepy\n",
    "import time\n",
    "from tweepy import OAuthHandler\n",
    "\n",
    "\n",
    "consumer_key = 'yoIwFkjZGYDa49aO16XqSNqcN'\n",
    "consumer_secret = 'gl4LQOItV7Z1aFwNrlvaiKJ3t8o8h99blMIAmnmdHxYjzjRAxO' \n",
    "access_token = '624310916-E7fDF2IE8P6bfY1oVFglASf6F8RnxMd3vgSXFqnZ'\n",
    "access_token_secret ='ID9JcoXHsDcKtvNcnmBGcCQhUlO0wmwAxBJ6LCesiUAas'\n",
    " \n",
    "auth = OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    " \n",
    "#api = tweepy.API(auth)\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True, retry_count=3, retry_delay=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Bijoyan Das\n",
      "ID: 624310916\n",
      "Location: Calcutta, India\n"
     ]
    }
   ],
   "source": [
    "# Creates the user object. The me() method returns the user whose authentication keys were used.\n",
    "user = api.me()\n",
    " \n",
    "print('Name: ' + user.name)\n",
    "print('ID: ' + str(user.id))\n",
    "print('Location: ' + user.location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "args = ['Khashoggi'];\n",
    "api = tweepy.API(auth,timeout=10)\n",
    "\n",
    "# Fetching the tweets\n",
    "list_tweets = []\n",
    "\n",
    "query = args[0]\n",
    "if len(args) == 1:\n",
    "    for status in tweepy.Cursor(api.search,q=query+\" -filter:retweets\",lang='en',result_type='recent',geocode=\"22.1568,89.4332,500km\").items(100):\n",
    "        list_tweets.append(status.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Khashoggi killers ‘will be prosecuted in Saudi Arabia’: Saudi FM  https://t.co/guT8LwGo3T',\n",
       " 'Mattis: Khashoggi murder undermines Middle East stability https://t.co/m8AilpBnr7',\n",
       " \"Saudi Arabia says it is beacon of 'light' against Iran despite Khashoggi crisis https://t.co/TJD7WLOx4t\",\n",
       " 'French President gives a shut up call to his European allies? Interesting read in Real Politick, CSS aspirants must… https://t.co/KZcU8eIx7f']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_tweets[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {0:'positive', 1:'negative'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Twitter Data Claening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('khashoggi killers will be prosecuted in saudi arabia saudi fm ',\n",
       "  'negative'),\n",
       " ('mattis khashoggi murder undermines middle east stability ', 'positive'),\n",
       " ('saudi arabia says it is beacon of light against iran despite khashoggi crisis ',\n",
       "  'negative'),\n",
       " ('french president gives shut up call to his european allies interesting read in real politick css aspirants must ',\n",
       "  'negative')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocessing the tweets\n",
    "\n",
    "sent_tweets = []\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",
    "    \n",
    "    sent = clf.predict(tfidf.transform([tweet]).toarray())\n",
    "    sent_tweets.append((tweet, mapping[int(sent)]))\n",
    "\n",
    "sent_tweets[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Positive/negative Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 69)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = len([s for t, s in sent_tweets if s == 'positive'])\n",
    "neg = len(sent_tweets) - pos\n",
    "pos, neg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a8e4940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the results\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.bar(['Positive','Negative'], [pos, 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",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = [\"You are a nice person man, have a good life\"]\n",
    "sample = tfidf.transform(sample).toarray()\n",
    "sentiment = clf.predict(sample)\n",
    "sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = []\n",
    "yorum = \"Kampanya berbat. kötü ya!\"\n",
    "blob = TextBlob(yorum)\n",
    "sample.append(str(blob.translate(to=\"en\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['The campaign sucks. bad either!']\n"
     ]
    }
   ],
   "source": [
    "print(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = tfidf.transform(sample).toarray()\n",
    "sentiment = clf.predict(sample)\n",
    "sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
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