Summary

In this chapter, we discussed topic modeling in detail. Without delving into advanced statistics, we reviewed various topic-modeling algorithms (such as LSA, LDA, and HDP) and how they can be used for topic modeling on a given dataset. We explored the challenges involved in topic modeling, how experimentation can help address those challenges, and, finally, broadly discussed the current state-of-the-art approaches to topic modeling.

In the next chapter, we will learn about vector representation of text, which helps us convert text into a numerical format to make it more easily understandable by machines.