• Sharat Babu Jami
• Ajit Kumar Pasayat

Social media is an internet-based form of communication. Social media platforms allow users to have conversations, share information and create web content. Billions of people around the world use social media to share information and make connections. Facebook, Twitter, Instagram, etc. are the mostly used social media applications which we use daily. We post, share, tweet in different applications in situations we go through. We also respond to many posts or tweets which were posted by popular personalities in the society. Sentiment Analysis is growing exponentially due to the importance of the automation in mining, extracting and processing information in order to determine the general opinion of a person. The problem that this paper proposes to address is to determine what methods are more suitable to extract subjective impressions in real time from Twitter, since the opinions collected from Twitter are limited to certain amount of characters and it will happen in a real-time environment, this provides an interesting scenario; we will test using both the Machine Learning Approach and the Lexicon-based Approach, It investigates the most popular document ("tweet") representation methods which feed sentiment evaluation mechanisms.

Sentiment Analysis, Naive Bayes, Logistic regression, R programming

"SENTIMENT ANALYSIS OF TWITTER DATA IN R USING LEXICON, NAÏVE BAYES AND LOGESTIC REGRESSION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 5, page no.745-750, MAY-2018, Available :http://www.jetir.org/papers/JETIR1805726.pdf

"SENTIMENT ANALYSIS OF TWITTER DATA IN R USING LEXICON, NAÏVE BAYES AND LOGESTIC REGRESSION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 5, page no. pp745-750, MAY-2018, Available at : http://www.jetir.org/papers/JETIR1805726.pdf