Aim of this project:
1) Find the percentage of Positive, Negative and Neutral Tweets
2) Analyze the tweets and understand the customer behavior for taking Decisions for optimizing customer satisfaction
3) Suggest the ideas to the business team
In [14]:
'''
Importing the dependencies
"re" - for regular expression functionalities to handle the textual data
tweepy - this is the python client for the official Twitter API
tweepy's OAuthHandler - Twitter has the developer account who can download the tweets from twitter website. Every developer
has a customer key and access token. When this valid combination is given in the program, it makes a call to the twitter API
and download the required number of tweets accordingly
textblob - this is the NLP library to handle the polarity(sentiment) of the textual data (here text data is tweets) '''
import re
import tweepy
from tweepy import OAuthHandler
from textblob import TextBlob
Step -2 : Functionalities covered
1) Validate the developer's account to download the tweets
2) Clean tweets using python regex
3) obtain the tweet's sentiment using python's textblob nlp library
4) Function to download tweets and parse them
In [15]:
class TwitterClient(object):
'''
Generic Twitter Class for sentiment analysis.
'''
def __init__(self):
'''
Class constructor or initialization method.
'''
# keys and tokens from the Twitter Dev Console
consumer_key = 'SnnT3pizFAHViBKU6qE013XuY'
consumer_secret = 'dY7Gksp2yCvV5YxQMJjeTbAGvYRsBx831aQfccHhAsoRjOo7RJ'
access_token = '166258894-zjBbDnhNJPwL0b2jDGERUIyoa6QOrQF8iLfRzcrP'
access_token_secret = 'r7PVqYRAuI1Wo2awFwgJDxgFq1KHW4pn5jQrb3FWeAWbT'
# attempt authentication
try:
# create OAuthHandler object
self.auth = OAuthHandler(consumer_key, consumer_secret)
# set access token and secret
self.auth.set_access_token(access_token, access_token_secret)
# create tweepy API object to fetch tweets
self.api = tweepy.API(self.auth)
except:
print("Error: Authentication Failed")
def clean_tweet(self, tweet):
'''
Utility function to clean tweet text by removing links, special characters
using simple regex statements.
'''
return ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)", " ", tweet).split())
def get_tweet_sentiment(self, tweet):
'''
Utility function to classify sentiment of passed tweet
using textblob's sentiment method
'''
# create TextBlob object of passed tweet text
analysis = TextBlob(self.clean_tweet(tweet))
# set sentiment
if analysis.sentiment.polarity > 0:
return 'positive'
elif analysis.sentiment.polarity == 0:
return 'neutral'
else:
return 'negative'
def get_tweets(self, query, count = 10):
'''
Main function to fetch tweets and parse them.
'''
# empty list to store parsed tweets
tweets = []
try:
# call twitter api to fetch tweets
fetched_tweets = self.api.search(q = query, count = count)
# parsing tweets one by one
for tweet in fetched_tweets:
# empty dictionary to store required params of a tweet
parsed_tweet = {}
# saving text of tweet
parsed_tweet['text'] = tweet.text
# saving sentiment of tweet
parsed_tweet['sentiment'] = self.get_tweet_sentiment(tweet.text)
# appending parsed tweet to tweets list
if tweet.retweet_count > 0:
# if tweet has retweets, ensure that it is appended only once
if parsed_tweet not in tweets:
tweets.append(parsed_tweet)
else:
tweets.append(parsed_tweet)
# return parsed tweets
return tweets
except tweepy.TweepError as e:
# print error (if any)
print("Error : " + str(e))
In [16]:
def main():
# creating object of TwitterClient Class
api = TwitterClient()
# calling function to get tweets
tweets = api.get_tweets(query = '@synovus', count = 1000)
# picking positive tweets from tweets
ptweets = [tweet for tweet in tweets if tweet['sentiment'] == 'positive']
# percentage of positive tweets
print("Positive tweets percentage: {} %".format(100*len(ptweets)/len(tweets)))
# picking negative tweets from tweets
ntweets = [tweet for tweet in tweets if tweet['sentiment'] == 'negative']
# percentage of negative tweets
print("Negative tweets percentage: {} %".format(100*len(ntweets)/len(tweets)))
# percentage of neutral tweets
print("Neutral tweets percentage: {} %".format(100*(len(tweets) - len(ntweets) - len(ptweets))/len(tweets)))
# printing first 5 positive tweets
print("\n\nPositive tweets:")
for tweet in ptweets[:5]:
print(tweet['text'])
# printing first 5 negative tweets
print("\n\nNegative tweets:")
for tweet in ntweets[:
5]:
print(tweet['text'])
if __name__ == "__main__":
# calling main function
main()