Twitter sentiment analysis using a modified naïve bayes algorithm

Microblogging has emerged as a popular platform and a powerful communication tool among people nowadays. A clear majority of people share their opinions about various aspects of their lives online every day. Thus, microblogging websites offer rich sources of data in order to perform sentiment analysis and opinion mining. Because microblogging has emerged relatively recently there are only some research works which are devoted to this field. In this paper, the focus is on performing the task of sentiment analysis using Twitter which is one of the most popular microblogging platforms. Twitter is a very popular microblogging site where its users write status messages called tweets to express themselves. These status updates mostly express their opinions about various topics. The objective of this paper is to build a system that can classify these Twitter status updates as positive, negative, or neutral with respect to any query term thereby giving an idea about the overall sentiment of the people towards that topic. This type of sentiment analysis is useful for advertisers, consumers researching a service or product, companies, governments, marketers, or any organization who are researching public opinion.