In the previous chapter we clustered texts into groups. This is a very useful tool, but it is not always appropriate. Clustering results in each text belonging to exactly one cluster. This book is about machine learning and Python. Should it be grouped with other Python-related works or with machine-related works? In the paper book age, a bookstore would need to make this decision when deciding where to stock it. In the Internet store age, however, the answer is that this book is both about machine learning and Python, and the book can be listed in both sections. We will, however, not list it in the food section.

In this chapter, we will learn methods that do not cluster objects, but put them into a small number of groups called topics. We will also learn how to derive between topics that are central to the text and others only that are vaguely mentioned (this book mentions plotting every so often, but it is not a central topic such as machine learning is). The subfield of machine learning that deals with these problems is called topic modeling.