Part- 19: Step by Step Guide to Master NLP – Topic Modelling using LDA (Matrix Factorization 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 LDA, in probabilistic terms. Probably, this article is the last part on Topic modelling since we covered almost all important techniques used for Topic Modelling. 

So, In this article, we will discuss another approach, named matrix factorization to understand the LDA which is similar to that of Singular Value Decomposition (SVD) which we discussed in our previous article.

This is part-19 of the blog series on the Step by Step Guide to Natural Language Processing.

 

Table of Contents

1. Matrix Factorization Approach for LDA

2. Parameters involved in LDA

3. Advantages and disadvantages of LDA

4. Tips to improve results of Topic Modelling using LDA

Matrix Factorization approach for LDA

Let’ see the step-by-step procedure of the matrix factorization approach for LDA.

Step-1

 

 

 

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