Fundamentals of Sentiment Analysis and Its Applications

The problem of identifying people’s opinions expressed in written language is a relatively new and very active field of research. Having access to huge amount of data due to the ubiquity of Internet, has enabled researchers in different fields—such as natural language processing, machine learning and data mining, text mining, management and marketing and even psychology—to conduct research in order to discover people’s opinions and sentiments from the publicly available data sources. Sentiment analysis and opinion mining are typically done at various level of abstraction: document, sentence and aspect. Recently researchers are also investigating concept-level sentiment analysis, which is a form of aspect-level sentiment analysis in which aspects can be multi terms. Also recently research has started addressing sentiment analysis and opinion mining by using, modifying and extending topic modeling techniques. Topic models are probabilistic techniques for discovering the main themes existing in a collection of unstructured documents. In this book chapter we aim at addressing recent approaches to sentiment analysis, and explain this in the context of wider use. We start the chapter with a brief contextual introduction to the problem of sentiment analysis and opinion mining and extend our introduction with some of its applications in different domains. The main challenges in sentiment analysis and opinion mining are discussed, and different existing approaches to address these challenges are explained. Recent directions with respect to applying sentiment analysis and opinion mining are discussed. We will review these studies towards the end of this chapter, and conclude the chapter with new opportunities for research.