Data Science, Libraries, NLP, Python, Text, Unstructured Data

Part 2: Topic Modeling and Latent Dirichlet Allocation (LDA) using Gensim and Sklearn

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

Introduction

In the previous article, we had started with understanding the basic terminologies of text in Natural Language Processing(NLP), what is topic modeling, its applications, the types of models, and the different topic modeling techniques available.

Let’s continue from there, explore Latent Dirichlet Allocation (LDA), working of LDA, and its similarity to another very popular dimensionality reduction technique called Principal Component Analysis (PCA).

LDA image

 

Table of Contents

  • A Little Background about LDA
  • Latent Dirichlet Allocation (LDA) and its Process
    • How does LDA work and how will it derive the particular distributions?
    • Vector Space of LDA
    • How will LDA optimize the distributions?
    • LDA is an Iterative Process
  • The Similarity between LDA and PCA

A Little Background about LDA

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique to extract topics from a given corpus. The term latent conveys something that exists but

 

 

 

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