Sentiment Analysis in Twitter

Abstract—Microblogging today has become a very popular communication tool among Internet users. Millions of users share opinions on different aspects of life everyday in popular websites such as Twitter, Tumblr and Facebook. Spurred by this growth, companies and media organisation are increasingly seeking ways to mine these social media for information about what people think about their companies and products. Political parties may be interested to know if people support their program or not. Social organizations may ask people’s opinion on current debates. All this information can be obtained from microblogging services, as their users post their opinions on many aspects of their life regularly. In this work, we present a method which performs 3-class classification of tweet sentiment in Twitter [7]. We present an end to end system which can determine the sentiment of a tweet at two levels- phrase level and message level. We leverage the features of tweets to build the classifier which achieves an accuracy of 77.90 % at phrase level and 58.13 % at message level. Keywords—sentiment analysis, classification, twitter, SVM, fea-tur