  Microbloging is an extremely prevalent broadcast medium amidst the Internet
fraternity these days. People share their opinions and sentiments about variety
of subjects like products, news, institutions, etc., every day on microbloging
websites. Sentiment analysis plays a key role in prediction systems, opinion
mining systems, etc. Twitter, one of the microbloging platforms allows a limit
of 140 characters to its users. This restriction stimulates users to be very
concise about their opinion and twitter an ocean of sentiments to analyze.
Twitter also provides developer friendly streaming API for data retrieval
purpose allowing the analyst to search real time tweets from various users. In
this paper, we discuss the state-of-art of the works which are focused on
Twitter, the online social network platform, for sentiment analysis. We survey
various lexical, machine learning and hybrid approaches for sentiment analysis
on Twitter.
