In this global village social media is in the front row to interact with people, Twitter is the ninth largest social networking website in the world, only because of microblogging people can share information by way of the short message up to 140 characters called tweets, It allows the registered users to search for the latest news on the topics they have an interest, Lakhs of tweets shared daily on a real-time basis by the members, it has more than 328 million active users per month , Twitter is the best source for the sentiment and opinion analysis on product reviews, movie reviews, and current issues in the world. In this paper, we present the sentiment analysis on the current twitters like Demonetization, Indians and all over the world people are sharing their opinions on Twitter about current news in the country. The sentiment analysis extracts positive and negative opinions from the twitter data set, R Studio provides the best environment for this Twitter sentiment analysis. Access Twitter data from Twitter API, data is written into txt files as the input dataset. Sentiment analysis is performed on the input dataset that initially performs data cleaning by removing the stop words, followed by classifying the tweets as positive and negative by polarity of the words. Generate the word cloud. Finally, that generates positive and negative word cloud, comparison of positive and negative scores to get the current public pulse and opinion

K.Arun, A.Srinagesh, & M.Ramesh. (2017). Twitter Sentiment Analysis on Demonetization tweets in India Using R language. International Journal of Computer Engineering In Research Trends, 4(6), 252-258. Retrieved from http://ijcert.org/ems/ijcert_papers/V4I6008.pdf

1)	 Efthymios Kouloumpis. Theresa Wilson, Johanna Moore “Twitter Sentiment Analysis: The Good the Bad and the OMG!” In the Proceedings of Fifth International AAAI Conference on Weblogs and Social Media ,2011.
2)	Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, “A Sentiment strength detection in short text”, Journal of the American Society for Information Science and Technology, 61(12), 2544–2558, 2010
3)	https://apps.twitter.com/app/13647643, Date accessed:12/04/2017
4)	https://about.twitter.com/company,Date  accessed:12/04/2017
5)	http://blog.revolutionanalytics.com/2017/01/cran-10000.html
6)	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.
7)	M De Choudhury, YR Lin, H Sundaram, KS Candan, L Xie, A Kelliher  “Authoritative Sources in a Hyperlinked Environment” International AAAI Conference on Weblogs and Social Media 2010, ed. Conference Program Committee, AAAI Press, New York., MAY 2010, pp-34-41.
8)	 Hu, M., & Liu, B. (2004). Mining opinion features in customer reviews. National Conference on Artificial Intelligence.
9)	https://lagunita.stanford.edu/c4x/Engineering/CS-224N/asset/slp4.pdf, Date accessed:21/04/2017
10)	Jockers, M. L. (2017). Syuzhet: Extract sentiment and plot arcs from text. Retrieved from  https://github.com/mjockers/syuzhet.
11)	Sunil B. Mane, Kruti Assar, Priyanka Sawant, & Monika Shinde. (2017). Product Rating using Opinion Mining. International Journal of Computer Engineering in Research Trends, 4(5), 161-168. Retrieved from http://ijcert.org/ems/ijcert_papers/V4I503.pdf.