# Part 3: Topic Modeling and Latent Dirichlet Allocation (LDA) using Gensim and Sklearn

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

**Overview**

In the previous two installments, we had understood in detail the common text terms in Natural Language Processing (NLP), what are topics, what is topic modeling, why it is required, its uses, types of models and dwelled deep into one of the important techniques called Latent Dirichlet Allocation (LDA).

In this last leg of the Topic Modeling and LDA series, we shall see how to extract topics through the LDA method in Python using the packages gensim and sklearn.

**Table of Contents**

- Data and Steps for Working with Text
- The Work Flow for executing LDA in Python

- Implementation of LDA using gensim
- Parameters for LDA model in gensim

- Implementation of LDA using sklearn
- Parameters for LDA model in sklearn

**Data and Steps for Working with Text**

We will apply LDA on the corpus that we have seen in the