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topic-modeler

topic-modeler

github.com/dkyazzentwatwaData & databases12458 installsUpdated 29w ago
Repository

About topic-modeler

topic-modeler is a command-line tool for discovering themes in text collections with LDA. It also classifies documents and provides topic keywords, visualizations, and coherence scores.

Key features

  1. 01LDA topic modeling
  2. 02Representative topic keywords
  3. 03Document-to-topic classification
  4. 04Word cloud and distribution views
  5. 05Topic coherence scores

Use cases

  1. 01Identify themes across a text collection
  2. 02Assign topics to individual documents
  3. 03Review topic terms and distributions
  4. 04Assess topic quality with coherence scores

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill topic-modeler

After installing, type/topic-modelerin Claude Code to confirm it loaded.

Bundled filesscripts/requirements.txtscripts/topic_modeler.py

Details

From the project README

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

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