Introduction

In the previous chapter, we learned about different ways to collect data from local files and online resources. In this chapter, we will focus on topic modeling, which is an important area within natural language processing. Topic modeling is a simple way to capture the sense of what a document or a collection of documents is about. Note that in this case, documents are any coherent collection of words, which could be as short as a tweet or as long as an encyclopedia.

Topic modeling may be thought of as a way to automate the manual task of reading given document(s) to write an abstract, which you will then use to map the document(s) to a set of topics. Topic modeling is mostly done using unsupervised learning algorithms that detect topics on their own. Topic-modeling algorithms operate by performing statistical analysis on words or tokens in documents and using those statistics to automatically assign each document to multiple topics. A topic is represented by an arbitrary...