Detailed_Analysis_on_Twitter _data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Twitter Sentiment Analysis\n",
    "\n",
    "- Here we analyse the sentiment by define intervel as per user.\n",
    "- we can calculate the very small detail of any topic by defining its interval\n",
    "- Useful for sales analysis, product reviews, demand of things as per regions, etc..\n",
    "- Need Twitter development account\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tweepy\n",
    "import matplotlib.pyplot as plt\n",
    "from textblob import TextBlob\n",
    "import sys, csv, re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the class and preprocess the Tweet for Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SentimentAnalysis:\n",
    "\n",
    "    def __init__(self):\n",
    "        self.tweets = []\n",
    "        self.tweetText = []\n",
    "\n",
    "    def DownloadData(self):\n",
    "        # authenticating\n",
    "        consumerKey = 'Enter Here'\n",
    "        consumerSecret = 'Enter Here'\n",
    "        accessToken = 'Enter Here'\n",
    "        accessTokenSecret = 'Enter Here'\n",
    "        auth = tweepy.OAuthHandler(consumerKey, consumerSecret)\n",
    "        auth.set_access_token(accessToken, accessTokenSecret)\n",
    "        api = tweepy.API(auth)\n",
    "\n",
    "        # input for term to be searched and how many tweets to search\n",
    "        searchTerm = input(\"Enter Keyword/Tag to search about: \")\n",
    "        NoOfTerms = int(input(\"Enter how many tweets to search: \"))\n",
    "\n",
    "        # searching for tweets\n",
    "        self.tweets = tweepy.Cursor(api.search, q=searchTerm, lang = \"en\").items(NoOfTerms)\n",
    "\n",
    "        # Open/create a file to append data to\n",
    "        csvFile = open('result.csv', 'a')\n",
    "\n",
    "        # Use csv writer\n",
    "        csvWriter = csv.writer(csvFile)\n",
    "\n",
    "\n",
    "        # creating some variables to store info\n",
    "        polarity = 0\n",
    "        positive = 0\n",
    "        week_positive = 0\n",
    "        strong_positive = 0\n",
    "        negative = 0\n",
    "        wnegative = 0\n",
    "        snegative = 0\n",
    "        neutral = 0\n",
    "\n",
    "\n",
    "        # iterating through tweets fetched\n",
    "        for tweet in self.tweets:\n",
    "            #Append to temp so that we can store in csv later. I use encode UTF-8\n",
    "            self.tweetText.append(self.cleanTweet(tweet.text).encode('utf-8'))\n",
    "             #print tweet's text\n",
    "            analysis = TextBlob(tweet.text)\n",
    "            # print(analysis.sentiment)  # print tweet's polarity\n",
    "            polarity += analysis.sentiment.polarity  # adding up polarities to find the average later\n",
    "            \n",
    "            #ploarity lie between -1 and 1\n",
    "            if (analysis.sentiment.polarity == 0):  # adding reaction of how people are reacting to find average later\n",
    "                neutral += 1\n",
    "            elif (analysis.sentiment.polarity > 0 and analysis.sentiment.polarity <= 0.3):\n",
    "                week_positive += 1\n",
    "            elif (analysis.sentiment.polarity > 0.3 and analysis.sentiment.polarity <= 0.6):\n",
    "                positive += 1\n",
    "            elif (analysis.sentiment.polarity > 0.6 and analysis.sentiment.polarity <= 1):\n",
    "                strong_positive += 1\n",
    "            elif (analysis.sentiment.polarity > -0.3 and analysis.sentiment.polarity <= 0):\n",
    "                wnegative += 1\n",
    "            elif (analysis.sentiment.polarity > -0.6 and analysis.sentiment.polarity <= -0.3):\n",
    "                negative += 1\n",
    "            elif (analysis.sentiment.polarity > -1 and analysis.sentiment.polarity <= -0.6):\n",
    "                snegative += 1\n",
    "            #we can also break the interval as per precision and requirement\n",
    "\n",
    "        # Write to csv and close csv file\n",
    "        csvWriter.writerow(self.tweetText)\n",
    "        csvFile.close()\n",
    "\n",
    "        # finding average of how people are reacting\n",
    "        positive = self.percentage(positive, NoOfTerms)\n",
    "        week_positive = self.percentage(week_positive, NoOfTerms)\n",
    "        strong_positive = self.percentage(strong_positive, NoOfTerms)\n",
    "        negative = self.percentage(negative, NoOfTerms)\n",
    "        wnegative = self.percentage(wnegative, NoOfTerms)\n",
    "        snegative = self.percentage(snegative, NoOfTerms)\n",
    "        neutral = self.percentage(neutral, NoOfTerms)\n",
    "\n",
    "        # finding average reaction\n",
    "        polarity = polarity / NoOfTerms\n",
    "\n",
    "        # printing out data\n",
    "        print(\"How people are reacting on \" + searchTerm + \" by analyzing \" + str(NoOfTerms) + \" tweets.\")\n",
    "        print()\n",
    "        print(\"General Report: \")\n",
    "\n",
    "        if (polarity == 0):\n",
    "            print(\"Neutral\")\n",
    "        elif (polarity > 0 and polarity <= 0.3):\n",
    "            print(\"Weakly Positive\")\n",
    "        elif (polarity > 0.3 and polarity <= 0.6):\n",
    "            print(\"Positive\")\n",
    "        elif (polarity > 0.6 and polarity <= 1):\n",
    "            print(\"Strongly Positive\")\n",
    "        elif (polarity > -0.3 and polarity <= 0):\n",
    "            print(\"Weakly Negative\")\n",
    "        elif (polarity > -0.6 and polarity <= -0.3):\n",
    "            print(\"Negative\")\n",
    "        elif (polarity > -1 and polarity <= -0.6):\n",
    "            print(\"Strongly Negative\")\n",
    "\n",
    "        print()\n",
    "        print(\"Detailed Report: \")\n",
    "        print(str(positive) + \"% people thought it was positive\")\n",
    "        print(str(week_positive) + \"% people thought it was weakly positive\")\n",
    "        print(str(strong_positive) + \"% people thought it was strongly positive\")\n",
    "        print(str(negative) + \"% people thought it was negative\")\n",
    "        print(str(wnegative) + \"% people thought it was weakly negative\")\n",
    "        print(str(snegative) + \"% people thought it was strongly negative\")\n",
    "        print(str(neutral) + \"% people thought it was neutral\")\n",
    "\n",
    "        self.plotPieChart(positive, week_positive, strong_positive, negative, wnegative, snegative, neutral, searchTerm, NoOfTerms)\n",
    "    \n",
    "    #clean the Tweet removing the link, special characters, etc..\n",
    "    def cleanTweet(self, tweet):\n",
    "        return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w +:\\ / \\ / \\S +)\", \" \", tweet).split())\n",
    "    \n",
    "    # calculate the percentage\n",
    "    def percentage(self, part, whole):\n",
    "        temp = 100 * float(part) / float(whole)\n",
    "        return format(temp, '.2f')\n",
    "    \n",
    "    #Analyse the tweet by pie chart\n",
    "    def plotPieChart(self, positive, week_positive, strong_positive, negative, wnegative, snegative, neutral, searchTerm, noOfSearchTerms):\n",
    "        labels = ['Positive [' + str(positive) + '%]', 'Weakly Positive [' + str(week_positive) + '%]',\n",
    "                  'Strongly Positive [' + str(strong_positive) + '%]', 'Neutral [' + str(neutral) + '%]',\n",
    "                  'Negative [' + str(negative) + '%]', 'Weakly Negative [' + str(wnegative) + '%]', \n",
    "                  'Strongly Negative [' + str(snegative) + '%]']\n",
    "        sizes = [positive, week_positive, strong_positive, neutral, negative, wnegative, snegative]\n",
    "        colors = ['yellowgreen','lightgreen','darkgreen', 'gold', 'red','lightsalmon','darkred']\n",
    "        patches, texts = plt.pie(sizes, colors=colors, startangle=90)\n",
    "        plt.legend(patches, labels, loc=\"best\")\n",
    "        plt.title('How people are reacting on ' + searchTerm + ' by analyzing ' + str(noOfSearchTerms) + ' Tweets.')\n",
    "        plt.axis('equal')\n",
    "        plt.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the object and calling the function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter Keyword/Tag to search about: Bitcoin\n",
      "Enter how many tweets to search: 1000\n",
      "How people are reacting on Bitcoin by analyzing 1000 tweets.\n",
      "\n",
      "General Report: \n",
      "Weakly Positive\n",
      "\n",
      "Detailed Report: \n",
      "15.40% people thought it was positive\n",
      "19.60% people thought it was weakly positive\n",
      "4.40% people thought it was strongly positive\n",
      "2.30% people thought it was negative\n",
      "9.30% people thought it was weakly negative\n",
      "0.90% people thought it was strongly negative\n",
      "48.00% people thought it was neutral\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    sentiment = SentimentAnalysis()\n",
    "    sentiment.DownloadData()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie chart help to view the people reaction in tweet very well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment and polarity lie between -1 and 1."
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}