Part 18: Step by Step Guide to Master NLP – Topic Modelling using LDA (Probabilistic Approach)

This article was published as a part of the Data Science Blogathon

Introduction

This article is part of an ongoing blog series on Natural Language Processing (NLP).  In the previous part of this series, we completed our discussion on pLSA, which is a probabilistic framework for Topic Modelling. But we have seen some of the limitations of pLSA, so to resolve those limitations LDA comes into the picture.

So, In this article, we will discuss the probabilistic or Bayesian approach to understand the LDA. In the next article, we will also discuss the matrix factorization technique to understand the LDA and also see some more important concepts about Topic Modelling.

This is part-18 of the blog series on the Step by Step Guide to Natural Language Processing.

 

Table of Contents

1. What is Latent Dirichlet Allocation (LDA)?

2. LDA in a nutshell

3. Why do we need Dirichlet Distributions?

4. What are Dirichlet Distributions?

5. Probabilistic approach of LDA

 

To finish reading, please visit source site