Sentiment-Analysis-Twitter/Sentiment_Analysis.py at master · yacine555/Sentiment-Analysis-Twitter · GitHub

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#Sentiment Analysis usng Twitter API and TextBlob for sentiment Analysis
#
#https://apps.twitter.com
#Owner	ybouakkaz
import csv
import os
import re
import tweepy
from textblob import TextBlob
import numpy as np
import matplotlib.pyplot as plt

class SearchPolarities:
  positive = 0.0
  neutral = 0.0
  negative = 0.0


consumer_key = os.environ['CONSUMER_KEY']
consumer_secret = os.environ['CONSUMER_SECRET']

access_token = os.environ['ACCESS_TOKEN']
access_token_secret = os.environ['ACCESS_TOKEN_SECRET']

try:
	auth = tweepy.OAuthHandler(consumer_key,consumer_secret)
	auth.set_access_token(access_token,access_token_secret)
	api = tweepy.API(auth)
except:
    print("Error: Authentication Failed")

#Set Application parameters
analysisQuery = input("Enter keyword you want to apply the sentiment analysis: ")
print('Term to analyze is: ' + analysisQuery )
tweetLang = 'English'

#Search the tweet to analyze based on input
#http://docs.tweepy.org/en/v3.6.0/api.html#API.search
tweets = api.search(analysisQuery)
#tweets = api.search(analysisQuery,lang=tweetLang)

if not tweets:
	exit()


search_polarities = SearchPolarities()


#Function of labelisation of analysis
def get_tweet_label(analysis):
    if analysis.sentiment.polarity > 0:
        return 'positive'
    elif analysis.sentiment.polarity == 0:
        return 'neutral'
    else:
        return 'negative'

#Function to clean tweet
def clean_tweet(tweet):
    return ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)", " ", tweet).split())

#create csv file to store tweets
with open('csv/%s_tweets.csv' % analysisQuery, 'w') as csvFile:
    csvWriter = csv.writer(csvFile, delimiter='\t', lineterminator='\n')
    csvWriter.writerow(['tweet,tweet cleaned,sentiment_label'])
    #Perform Sentiment Analysis on Tweets
    for tweet in tweets:
        print(tweet.text)
        analysis = TextBlob(tweet.text)
        #search_polarities.append(analysis.sentiment.polarity)


        print(analysis.sentiment.polarity)

        if (analysis.sentiment.polarity > 0.00):
            search_polarities.positive +=1
        elif (analysis.sentiment.polarity < 0.00):
            search_polarities.negative +=1
        else:
            search_polarities.neutral +=1

        tweet_cleaned = clean_tweet(tweet.text)
        csvWriter.writerow([tweet.text,',',tweet_cleaned ,',', get_tweet_label(analysis)])


search_polarities.positive = search_polarities.positive/len(tweets)*100
search_polarities.negative = search_polarities.negative/len(tweets)*100
search_polarities.neutral = search_polarities.neutral/len(tweets)*100

print('\n Result of sentiment analysis for term ' + analysisQuery + ':\n')
print('%s : %0.3f' % ('Positive', search_polarities.positive))

#use matplotlib to display values on graph
labels = ['Positive ['+ str(search_polarities.positive)+'%]','Negative ['+ str(search_polarities.negative)+'%]','Neutral  ['+ str(search_polarities.neutral)+'%]']
sizes = [search_polarities.positive,search_polarities.negative,search_polarities.neutral]
colors = ['green','red','gray']

patches = plt.pie(sizes, colors=colors)
plt.legend(patches[0], labels,loc="best")
plt.title('Twitter sentiment analysis of term ' + analysisQuery)
plt.tight_layout()
plt.show()