topic-modeler
Extract topics from text collections using LDA (Latent Dirichlet Allocation) with keyword extraction and topic visualization.
When & Why to Use This Skill
The Topic Modeler skill is a powerful NLP tool designed to uncover hidden thematic structures within large text datasets using Latent Dirichlet Allocation (LDA). It automates the process of identifying core subjects, extracting representative keywords, and visualizing topic distributions through word clouds. By providing coherence scores to evaluate model quality, it enables users to transform unstructured text into structured, actionable insights with high statistical reliability.
Use Cases
- Customer Feedback Analysis: Automatically group thousands of customer reviews or support tickets into themes like 'pricing,' 'usability,' or 'bugs' to prioritize product improvements.
- Market & Trend Research: Analyze large volumes of news articles or social media posts to identify emerging industry trends and shifts in public sentiment.
- Academic & Legal Discovery: Process massive document archives to map out research domains or identify key subjects in legal discovery phases.
- Content Audit & SEO: Evaluate a website's content library to ensure topical coverage and identify opportunities for creating new content clusters based on extracted themes.
| name | topic-modeler |
|---|---|
| description | Extract topics from text collections using LDA (Latent Dirichlet Allocation) with keyword extraction and topic visualization. |
Topic Modeler
Extract topics from text collections using LDA.
Features
- LDA Topic Modeling: Latent Dirichlet Allocation
- Topic Keywords: Extract representative keywords per topic
- Document Classification: Assign documents to topics
- Visualization: Topic word clouds and distributions
- Coherence Scores: Evaluate topic quality
CLI Usage
python topic_modeler.py --input documents.csv --column text --topics 5 --output topics.json
Dependencies
- gensim>=4.3.0
- nltk>=3.8.0
- pandas>=2.0.0
- matplotlib>=3.7.0
- wordcloud>=1.9.0