Twitter Sentiment Analysis with Python

Note: This article has also featured on geeksforgeeks.org.

This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python.

What is sentiment analysis?
Sentiment Analysis is the process of ‘computationally’ determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker.

Why sentiment analysis?

• Business: In marketing field companies use it to develop their strategies, to understand customers’ feelings towards products or brand, how people respond to their campaigns or product launches and why consumers don’t buy some
  products.
• Politics: In political field, it is used to keep track of political view, to detect consistency and inconsistency between statements and actions at the government level. It can be used to predict election results as well!
• Public Actions: Sentiment analysis also is used to monitor and analyse social phenomena, for the spotting of potentially dangerous situations and determining the general mood of the blogosphere.

Installation:

• Tweepy: tweepy is the python client for the official Twitter API.
  Install it using following pip command:

  pip install tweepy

• TextBlob: textblob is the python library for processing textual data.
  Install it using following pip command:

  pip install textblob

  Also, we need to install some NLTK corpora using following command:

  python -m textblob.download_corpora

  (Corpora is nothing but a large and structured set of texts.)

Authentication:
In order to fetch tweets through Twitter API, one needs to register an App through their twitter account. Follow these steps for the same:

• Open this link and click the button: ‘Create New App’
• Fill the application details. You can leave the callback url field empty.
• Once the app is created, you will be redirected to the app page.
• Open the ‘Keys and Access Tokens’ tab.
• Copy ‘Consumer Key’, ‘Consumer Secret’, ‘Access token’ and ‘Access Token Secret’.

Implementation:

import re
import tweepy
from tweepy import OAuthHandler
from textblob import TextBlob

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 = 'XXXXXXXXXXXXXXXXXXXXXXXX'
		consumer_secret = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXX'
		access_token = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXX'
		access_token_secret = 'XXXXXXXXXXXXXXXXXXXXXXXXX'

		# 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))

def main():
	# creating object of TwitterClient Class
	api = TwitterClient()
	# calling function to get tweets
	tweets = api.get_tweets(query = 'Donald Trump', count = 200)

	# picking positive tweets from tweets
	ptweets = [t for  in tweets if t['sentiment'] == 'positive']
	# percentage of positive tweets
	print("Positive tweets percentage: {} %".format(100*len(ptweets)/len(tweets)))
	# picking negative tweets from tweets
	ntweets = [t for t in tweets if t['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[:10]:
		print(tweet['text'])

	# printing first 5 negative tweets
	print("\n\nNegative tweets:")
	for tweet in ntweets[:10]:
		print(tweet['text'])

if __name__ == "__main__":
	# calling main function
	main()

Here is how a sample output looks like when above program is run:

Positive tweets percentage: 22 %
Negative tweets percentage: 15 %


Positive tweets:
RT @JohnGGalt: Amazing—after years of attacking Donald Trump the media managed
to turn #InaugurationDay into all about themselves.
#MakeAme…
RT @vooda1: CNN Declines to Air White House Press Conference Live YES! 
THANK YOU @CNN FOR NOT LEGITIMI…
RT @Muheeb_Shawwa: Donald J. Trump's speech sounded eerily familiar...
POTUS plans new deal for UK as Theresa May to be first foreign leader to meet new 
president since inauguration 
.@realdonaldtrump #Syria #Mexico #Russia & now #Afghanistan. 
Another #DearDonaldTrump Letter worth a read @AJEnglish 


Negative tweets:
RT @Slate: Donald Trump’s administration: “Government by the worst men.” 
RT @RVAwonk: Trump, Sean Spicer, et al. lie for a reason. 
Their lies are not just lies. Their lies are authoritarian propaganda.  
RT @KomptonMusic: Me: I hate corn 
Donald Trump: I hate corn too
Me: https://t.co/GPgy8R8HB5
It's ridiculous that people are more annoyed at this than Donald Trump's sexism.
RT @tony_broach: Chris Wallace on Fox news right now talking crap 
about Donald Trump news conference it seems he can't face the truth eithe…
RT @fravel: With False Claims, Donald Trump Attacks Media on Crowd Turnout 
Aziz Ansari Just Hit Donald Trump Hard In An Epic Saturday NIght Live Monologue

We follow these 3 major steps in our program:

• Authorize twitter API client.
• Make a GET request to Twitter API to fetch tweets for a particular query.
• Parse the tweets. Classify each tweet as positive, negative or neutral.

Now, let us try to understand the above piece of code:

• First of all, we create a TwitterClient class. This class contains all the methods to interact with Twitter API and parsing tweets. We use __init__ function to handle the authentication of API client.
• In get_tweets function, we use:

  fetched_tweets = self.api.search(q = query, count = count)

  to call the Twitter API to fetch tweets.

• In get_tweet_sentiment we use textblob module.

  analysis = TextBlob(self.clean_tweet(tweet))

  TextBlob is actually a high level library built over top of NLTK library. First we call clean_tweet method to remove links, special characters, etc. from the tweet using some simple regex.
  Then, as we pass tweet to create a TextBlob object, following processing is done over text by textblob library:

  • Tokenize the tweet ,i.e split words from body of text.
  • Remove stopwords from the tokens.(stopwords are the commonly used words which are irrelevant in text analysis like I, am, you, are, etc.)
  • Do POS( part of speech) tagging of the tokens and select only significant features/tokens like adjectives, adverbs, etc.
  • Pass the tokens to a sentiment classifier which classifies the tweet sentiment as positive, negative or neutral by assigning it a polarity between -1.0 to 1.0 .

  Here is how sentiment classifier is created:

  • TextBlob uses a Movies Reviews dataset in which reviews have already been labelled as positive or negative.
  • Positive and negative features are extracted from each positive and negative review respectively.
  • Training data now consists of labelled positive and negative features. This data is trained on a Naive Bayes Classifier.

  Then, we use sentiment.polarity method of TextBlob class to get the polarity of tweet between -1 to 1.
  Then, we classify polarity as:

  if analysis.sentiment.polarity > 0:
         return 'positive'
  elif analysis.sentiment.polarity == 0:
         return 'neutral'
  else:
         return 'negative'

• Finally, parsed tweets are returned. Then, we can do various type of statistical analysis on the tweets. For example, in above program, we tried to find the percentage of positive, negative and neutral tweets about a query.

References:

• http://www.ijcaonline.org/research/volume125/number3/dandrea-2015-ijca-905866.pdf
• https://textblob.readthedocs.io/en/dev/quickstart.html#sentiment-analysis
• textblob.readthedocs.io/en/dev/_modules/textblob/en/sentiments.html