In this chapter, we studied corpus building techniques that consists of sentences and words, which includes a bag of words to make the texts usable for the algorithms. You also learned about TF-IDF and how important a term is with respect to a document and the entire corpus. We went over sentiment analysis, along with classification and TF-IDF feature extraction.

You were also introduced to topic modeling and evaluating models, which includes visualizing LDA. We covered the Bayes theorem and working with the Naive Bayes classifier. In the next chapter, you will learn about temporal and sequential pattern discovery.