Topic modeling using LDA

Let's explore another popular topic modeling algorithm, latent Dirichlet allocation (LDA). LDA is a generative probabilistic graphical model that explains each input document by means of a mixture of topics with certain probabilities. Again, topic in topic modeling means a collection of words with a certain connection. In other words, LDA basically deals with two probability values, P(term | topic) and P(topic | document). This can be difficult to understand at the beginning. So, let's start from the bottom, the end result of an LDA model.

Let's take a look at the following set of documents:

Document 1: This restaurant is famous for fish and chips.Document 2: I had fish and rice for lunch.Document 3: My sister bought ...

Get Python Machine Learning By Example - Second Edition now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.