Twitter is a goldmine for data science, with interesting patterns and insights spread all across it. Its constant flow of user-generated content, coupled with unique, interest-based relationships, present opportunities to understand human dynamics up close. Sentiments Analysis is one such field where Twitter provides the right set of ingredients to understand what and how we present and share opinions about products, brands, people, and so on.
Throughout this chapter, we have looked at the basics of Sentiment Analysis, key terms, and areas of application. We have also looked into the various challenges posed while performing sentiment analysis. We have looked at various commonly-used feature extraction methods such as tf-idf, Ngrams, POS, negation, and so on for performing sentiment analysis (or textual analysis in general). We have built on our code base from the previous chapter to streamline and structure utility functions for reuse. We have performed polarity analysis using Twitter...