• Nikita Dandwate
• Sarika Solanke

Sentiment analysis is defined as the category of natural language processing-based and computational technique. It is then used to sense, extract and illustrate information, which is subjective, and is expressed in a given part of text. The main intention of sentiment analysis is to classify the writer’s attitude towards different topics into various categories like positive, negative or neutral. In past few years, on the other side, have witnessed the arrival of social networking websites, microblogs and Web applications and accordingly, an extraordinary growth in user-generated data is composed for sentiment mining. Data like Tweets, web-postings etc., all express thoughts on numerous topics and events, offer vast opportunities to study and analyse human feelings and sentiment. Twitter sentiment analysis however has emerged as a hot research topic in past few years. Most of existing solutions to Twitter sentiment analysis only consider textual information of Twitter messages, and probably fails when short or confusing messages or conversations appear. Current studies display that sentiment diffusion patterns on Twitter have very close relations with sentiment polarities of the Twitter messages. Therefore, in this paper we focus on how to fuse textual information of Twitter messages and sentiment Analysis patterns to obtain better performance on Twitter data.

– Sentiment Analysis, Aspects, Opinion Mining, Social Network, Text Classification, Sentiment Polarities, Twitter Data

"SENTIMENT ANALYSIS AND OPINION MINING FOR TWITTER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.380-386, December 2018, Available :http://www.jetir.org/papers/JETIRDT06048.pdf

"SENTIMENT ANALYSIS AND OPINION MINING FOR TWITTER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp380-386, December 2018, Available at : http://www.jetir.org/papers/JETIRDT06048.pdf