Introduction

In the previous chapter, we looked at text generation, paraphrasing, and summarization, all of which can be immensely useful in helping us focus on only the essential and meaningful parts of the text corpus. This, in turn, helps us to further refine the results of our NLP project. In this chapter, we will look at sentiment analysis, which, as the name suggests, is the area of NLP that involves teaching computers how to identify the sentiment behind written content or parsed audio—that is, audio converted to text. Adding this ability to automatically detect sentiment in large volumes of text and speech opens new possibilities for us to write useful software.

In sentiment analysis, we try to build models that detect how people feel. This starts with determining what kind of feeling we want to detect. Our application may attempt to determine the level of human emotion (most often, whether a person is sad or happy; satisfied or dissatisfied; or interested or disinterested...