What is Rapid Miner

With the increasing penetration of the internet, an ever-growing number of people are voicing their opinions in the numerous blogs, tweets, forums, social networking, and consumer review websites. Each such opinion has a sentiment (positive, negative, or neutral) associated with it. But the problem is that the amount of data is simply overwhelming. Methods like supervised machine learning and lexical-based approaches are available for measuring sentiments that have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services. This chapter presents sentiment analysis applications and challenges with their approaches and tools. The techniques and applications discussed in this chapter will provide a clear-cut idea to the sentiment analysis researchers to carry out their work in this field.