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main.py
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main.py
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# Author : Poshan Pandey
# Date : 6/3/2020
# Time : 3:21 PM
# Importing required libraries
import sys,tweepy,csv,re
from textblob import TextBlob
import matplotlib.pyplot as plt
# Sentiment Analysis class reads and analyzes all the tweets. Also It make use of cleanTweet and Percentage functions
class SentimentAnalysis:
def __init__(self):
self.tweets = []
self.tweetText = []
def DownloadData(self):
# authenticating with twitter keys
consumerKey = 'No Sharing of Keys for Privacy,Enter your own key'
consumerSecret = 'No Sharing of Keys for Privacy,Enter your own key'
accessToken = 'No Sharing of Keys for Privacy,Enter your own key'
accessTokenSecret = 'No Sharing of Keys for Privacy,Enter your own key'
auth = tweepy.OAuthHandler(consumerKey, consumerSecret)
auth.set_access_token(accessToken, accessTokenSecret)
api = tweepy.API(auth)
# User input for keyword to be searched and the number of tweets to be analyzed
searchTerm = input("Enter Keyword/Tag to search tweets: ")
NoOfTerms = int(input("Enter number of tweets to analyze: "))
# searching for tweets
self.tweets = tweepy.Cursor(api.search, q=searchTerm, lang = "en").items(NoOfTerms)
# Open/create a file to append data to
csvFile = open('result.csv', 'a')
# Use csv writer
csvWriter = csv.writer(csvFile)
# creating some variables to store info
polarity = 0
positive = 0
wpositive = 0
spositive = 0
negative = 0
wnegative = 0
snegative = 0
neutral = 0
# iterating through tweets fetched
for tweet in self.tweets:
#Append to temp so that we can store in csv later. I use encode UTF-8
self.tweetText.append(self.cleanTweet(tweet.text).encode('utf-8'))
# print (tweet.text.translate(non_bmp_map)) #print tweet's text
analysis = TextBlob(tweet.text)
# print(analysis.sentiment) # print tweet's polarity
polarity += analysis.sentiment.polarity # adding up polarities to find the average later
if (analysis.sentiment.polarity == 0): # adding reaction of how people are reacting to find average later
neutral += 1
elif (analysis.sentiment.polarity > 0 and analysis.sentiment.polarity <= 0.3):
wpositive += 1
elif (analysis.sentiment.polarity > 0.3 and analysis.sentiment.polarity <= 0.6):
positive += 1
elif (analysis.sentiment.polarity > 0.6 and analysis.sentiment.polarity <= 1):
spositive += 1
elif (analysis.sentiment.polarity > -0.3 and analysis.sentiment.polarity <= 0):
wnegative += 1
elif (analysis.sentiment.polarity > -0.6 and analysis.sentiment.polarity <= -0.3):
negative += 1
elif (analysis.sentiment.polarity > -1 and analysis.sentiment.polarity <= -0.6):
snegative += 1
# Write to csv and close csv file
csvWriter.writerow(self.tweetText)
csvFile.close()
# finding average of how people are reacting
positive = self.percentage(positive, NoOfTerms)
wpositive = self.percentage(wpositive, NoOfTerms)
spositive = self.percentage(spositive, NoOfTerms)
negative = self.percentage(negative, NoOfTerms)
wnegative = self.percentage(wnegative, NoOfTerms)
snegative = self.percentage(snegative, NoOfTerms)
neutral = self.percentage(neutral, NoOfTerms)
# finding average reaction
polarity = polarity / NoOfTerms
# printing out data
print("After Analyzing " + str(NoOfTerms) + " tweets of " + str(searchTerm) + ", On average we found:.")
print()
print("General Report: ")
if (polarity == 0):
print("Neutral")
elif (polarity > 0 and polarity <= 0.3):
print("Weakly Positive")
elif (polarity > 0.3 and polarity <= 0.6):
print("Positive")
elif (polarity > 0.6 and polarity <= 1):
print("Strongly Positive")
elif (polarity > -0.3 and polarity <= 0):
print("Weakly Negative")
elif (polarity > -0.6 and polarity <= -0.3):
print("Negative")
elif (polarity > -1 and polarity <= -0.6):
print("Strongly Negative")
print()
print("Detailed Report: ")
print(str(positive) + "% tweets has positive sentiment.")
print(str(wpositive) + "% tweets has weakly positive sentiment.")
print(str(spositive) + "% tweets has strongly positive sentiment.")
print(str(negative) + "% tweets has negative sentiment.")
print(str(wnegative) + "% tweets has weakly negative sentiment.")
print(str(snegative) + "% tweets has strongly negative sentiment.")
print(str(neutral) + "% tweets has neutral sentiment.")
self.plotPieChart(positive, wpositive, spositive, negative, wnegative, snegative, neutral, searchTerm, NoOfTerms)
def cleanTweet(self, tweet):
# Remove Links, Special Characters etc from tweet
return ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t]) | (\w +:\ / \ / \S +)", " ", tweet).split())
# function to calculate percentage
def percentage(self, part, whole):
temp = 100 * float(part) / float(whole)
return format(temp, '.2f')
def plotPieChart(self, positive, wpositive, spositive, negative, wnegative, snegative, neutral, searchTerm, noOfSearchTerms):
labels = ['Positive [' + str(positive) + '%]', 'Weakly Positive [' + str(wpositive) + '%]','Strongly Positive [' + str(spositive) + '%]', 'Neutral [' + str(neutral) + '%]',
'Negative [' + str(negative) + '%]', 'Weakly Negative [' + str(wnegative) + '%]', 'Strongly Negative [' + str(snegative) + '%]']
sizes = [positive, wpositive, spositive, neutral, negative, wnegative, snegative]
colors = ['green','lightgreen','darkgreen', 'yellow', 'red','lightsalmon','darkred']
patches, texts = plt.pie(sizes, colors=colors, startangle=90)
plt.legend(patches, labels, loc="best")
plt.title('After analyzing ' + str(noOfSearchTerms) + ' tweets on ' + searchTerm + ', On average we found:')
plt.axis('equal')
plt.tight_layout()
plt.show()
if __name__== "__main__":
sa = SentimentAnalysis()
sa.DownloadData()