Python for NLP: Topic Modeling


This is the sixth article in my series of articles on Python for NLP. In my previous article, I talked about how to perform sentiment analysis of Twitter data using Python’s Scikit-Learn library. In this article, we will study topic modeling, which is another very important application of NLP. We will see how to do topic modeling with Python.

What is Topic Modeling

Topic modeling is an unsupervised technique that intends to analyze large volumes of text data by clustering the documents into groups. In the case of topic modeling, the text data do not have any labels attached to it. Rather, topic modeling tries to group the documents into clusters based on similar characteristics.

A typical example of topic modeling is clustering a large number of newspaper articles that belong to the same category. In other words, cluster documents that have the same topic. It is important to mention here that it is extremely difficult to evaluate the performance of topic modeling since there are no right answers. It depends upon the user to find similar characteristics between the documents of one cluster and assign it an appropriate label or topic.

Two approaches are mainly

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