Topic Modeling

Topic Modeling

Discovering abstract topics in document collections.

Learning Outcomes

By completing this topic, you will:

• Understand Latent Dirichlet Allocation (LDA)
• Preprocess text for topic modeling
• Choose the optimal number of topics
• Interpret and visualize topic models

Visual Guides

Topic Word Distribution

Document-Topic Mix

Finding Optimal Topics

Prerequisites

• NLP & Sentiment Analysis concepts
• Unsupervised Learning fundamentals
• Text preprocessing techniques

Key Concepts

Latent Dirichlet Allocation (LDA)

Probabilistic topic model:

• Documents are mixtures of topics
• Topics are distributions over words
• Discovers hidden thematic structure

Implementation Workflow

1. Preprocess and tokenize documents
2. Create document-term matrix
3. Train LDA with chosen K topics
4. Evaluate coherence and perplexity
5. Interpret and label topics

Evaluation Metrics

• Coherence score: Topic interpretability
• Perplexity: How well model fits held-out data
• Human evaluation: Topic quality assessment

When to Use

Topic modeling is valuable for:

• Document organization and tagging
• Content recommendation systems
• Research trend analysis
• Survey response analysis

Common Pitfalls

• Choosing number of topics arbitrarily
• Poor text preprocessing
• Ignoring stop words and rare terms
• Over-interpreting topic labels
• Not validating topic stability

(c) Joerg Osterrieder 2025