Sentiment Analysis and Influence Tracking using Twitter

An overwhelming number of consumers are active in social media platforms. Within these platforms consumers are sharing their true feelings about a particular brand/product, its features, customer service and how it stands the competition. With the booming of microblogs on the Web, people have begun to express their opinions on a wide variety of topics on Twitter and other similar services. In a world where information can bias public opinion it is essential to analyse the propagation and influence of information in large-scale networks. Recent research studying social media data to rank users by topical relevance have largely focused on the “retweet", “following" and “mention" relations. We also perform linguistic analysis of the collected corpus and explain discovered phenomena. Using the corpus, we build a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document. This paper discusses how Twitter data is used as a corpus for analysis by the application of sentiment analysis and a study of different algorithms and methods that help to track influence and impact of a particular user/brand active on the social network.