Open Access Open Access  Restricted Access Subscription or Fee Access

Sentiment Analysis on Good Service Tax (GST) using Twitter Data

G. Raja Meenakshi, Dr. V. Srividhya


The social media has increased the opportunity to explore and track the response of new reforms and policies in India. Social Media helps in the analysis of stock market data, new product launch and movie release. One of the social networking websites is Twitter and it is the ninth largest website. 

With Twitter, the registered users can search for the latest information on the topics of their interest.  Since lakhs of tweets are shared on a real-time basis by the members every day, it has got more than 328 million active users a month. Twitter is the best source for the analysis of opinion and sentiment on movie reviews, product reviews and current issues in the world. Twitter is used widely as a forum for understanding the sentiments of Indians towards recently launched Goods and Services Tax by the Indian Government on 1st July 2017.

Sentiment analysis extracts positive and negative opinions from the twitter dataset and R Studio provides the best environment for this Twitter sentiment analysis. Data is written into text files as the input dataset so that Twitter data could be accessed from Twitter API. Sentiment analysis is performed on the input dataset that initially performs data cleaning by removing the stop words and then by classifying the tweets as positive and negative by considering the polarity of words. Finally, positive, negative and neutral is generated, comparing the polarity of the tweets


Twitter Data, Word Cloud, Sentiment Analysis, Social Media R-Studio.

Full Text:



The constitution (one hundred and first amendments) act, Amendment No. 101 of Retrieved from the Internet, 2016.

India’s midnight 'tryst with destiny': GST rolled out. Dynamite (ANI). Retrieved from Internet, 2017.

All your queries on GST answered. The Hindu. Retrieved, 2017-06-30

Hindustan Times News, Retrieved from the Internet. 10000.html

O’Connor B, Balasubramanyan R, Routledge BR, Smith NA. From tweets to polls: Linking text sentiment to public opinion time series. Proceedings of the International AAAI Conference on Weblogs and Social Media, Washington, DC, 2010.

Xin J, Gallagher A, Cao L. The wisdom of social multimedia: Using Flickr for prediction and forecast. ACM Multimedia, International Conference, New York, NY, 2010.

Jensen MJ, Jorba L, Anduiza E. Introduction. In E. Anduiza, M. Jensen, & L. Jorba (Eds.), Digital media and political engagement worldwide: A comparative study. New York, NY: Cambridge University Press, 2012, 1-15.

Tjong Kim Sang E, Bos J. Predicting the 2011 Dutch senate election results with twitter. Proceedings of SASN 2012, the EACL 2012 Workshop on Semantic Analysis in Social Networks, Avignon, France, 2012

Yu.H and Hatzivassiloglou.V “Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences” In the Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-03), NOV 2003.

Sunil B. Mane, Kruti Assar, PriyankaSawant, & Monika Shinde. (2017). Product Rating using Opinion Mining. International Journal of Computer Engineering in Research Trends, 4(5), 161-168., Date accessed: 12/04/2017


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.