Module 4: Topic Modeling

Module Overview

Topic Modeling is an unsupervised machine learning technique that automatically identifies topics present in text and derives hidden patterns in a corpus of documents. In this module, we'll explore Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, and learn how to implement it using the Gensim library. We'll also cover how to interpret the results of topic models and extract meaningful insights from document collections. These techniques are crucial for organizing, searching, and understanding large volumes of unstructured text data.

Learning Objectives

• Describe the Latent Dirichlet Allocation Process
• Implement a Topic Model using the Gensim library
• Interpret Document Topic Distributions and summarize findings from a topic model

Guided Project

Open DS_414_Topic_Modeling_Lecture_GP.ipynb in the GitHub repository to follow along with the guided project.

Note: The guided project solution notebook in the GitHub repository is currently broken.

Module Assignment

Apply topic modeling to analyze Amazon reviews using Gensim LDA. Clean the dataset, fit the model, select appropriate number of topics, and create visualizations to summarize your findings.

Assignment Solution Video

Additional Resources

LDA and Topic Modeling Theory

• LDA in Journal of Machine Learning Research
• Topic Modeling with Gensim (Python)

Gensim Implementation

• Gensim LDA Python Module
• Gensim Documentation
• Gensim LDA Model Tutorial

Visualization and Interpretation

• pyLDAvis: Interactive Topic Model Visualization
• pyLDAvis Documentation
• Intuitive Guide to Latent Dirichlet Allocation