Module 4: Topic Modeling

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.

1. Describe the Latent Dirichlet Allocation Process

• Understand the mathematical foundations of LDA
• Explain how LDA assigns topics to documents and words to topics
• Identify the key parameters in LDA models
• Compare LDA to other topic modeling approaches

2. Implement a Topic Model using the Gensim library

• Preprocess text data for topic modeling
• Create document-term matrices using Gensim
• Train LDA models with different parameter settings
• Evaluate and optimize topic model performance

3. Interpret Document Topic Distributions and summarize findings from a topic model

• Extract and label topics from LDA models
• Analyze document-topic distributions
• Visualize topic models using pyLDAvis and other tools
• Communicate insights derived from topic modeling

Topic Modeling with LDA

GitHub Repo | Slides

Guided Project File:

DS_414_Topic_Modeling_Lecture_GP.ipynb

Please read the assignment file in the GitHub repository for detailed instructions on completing your assignment tasks.

Assignment File:

DS_414_Topic_Modeling_Assignment.ipynb

In this assignment, you will apply Topic Modeling to analyze a corpus of Amazon reviews. Your tasks include:

• Loading in the Amazon Review dataset
• Cleaning the dataset
• Vectorizing the dataset
• Fitting a Gensim LDA topic model on Amazon Reviews
• Selecting appropriate number of topics
• Creating effective visualizations of the topics
• Writing a summary of your findings in markdown at the end

Assignment Solution Video

Complete the following items to test your understanding:

• Explain the key concepts of the Latent Dirichlet Allocation algorithm
• Preprocess a corpus of documents for topic modeling
• Implement an LDA model using Gensim
• Determine the optimal number of topics for a given corpus
• Visualize and interpret the results of a topic model

• Gensim LDA Model Documentation
• Topic Modeling with Gensim (Python)
• pyLDAvis: Interactive Topic Model Visualization
• LDA in Journal of Machine Learning Research