Part 16 : Step by Step Guide to Master NLP – Topic Modelling using LSA

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 article, we completed a basic technique of Topic Modeling named Non-Negative Matrix Factorization. So, In continuation of that part now we will start our discussion on another Topic modeling technique named Latent Semantic Analysis.

So, In this article, we will deep dive into a Topic Modeling technique named Latent Semantic Analysis (LSA) and see how this technique uncovers these latent topics which become a very useful thing while we work on any NLP Problem statement.

This is part-16 of the blog series on the Step by Step Guide to Natural Language Processing.

 

Table of Contents

1. Recap of Topic Modeling

2. Why do we need Latent Semantic Analysis (LSA)?

3. What is Latent Semantic Analysis (LSA)?

4. Steps involved while Implementing LSA

5. Advantages and Disadvantages of LSA

6. How to choose

 

 

 

To finish reading, please visit source site