Introduction
In the previous chapter, we looked at how text can be represented as vectors. We also learned about the different types of encoding. This chapter deals with the area of sentiment analysis. Sentiment analysis is the area of NLP that involves teaching computers to identify the sentiment behind written content or parsed audio. Adding this ability to automatically detect sentiment in large volumes of text and speech opens up 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. The common thread here is that we measure how sentiments vary in different directions. This is also called polarity.