topic-modeler

Extract latent topics from text collections using Latent Dirichlet Allocation.

86|18|Updated Dec 14, 2025

Search Tags:#document analysis #text analysis #topic modeling #natural language processing #keyword extraction #lda

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: topic-modeler
Source: https://github.com/dkyazzentwatwa/chatgpt-skills/tree/main/topic-modeler
Command: npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill topic-modeler

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires gensim, nltk, pandas, matplotlib, wordcloud, numpy, and includes scripts (resource) components.

What problem does it solve?

This Skill addresses the challenge of understanding large volumes of text by automatically identifying and summarizing the main themes present within a collection of documents.

Core Features & Use Cases

LDA Topic Modeling: Utilizes Latent Dirichlet Allocation to discover underlying topics.
Keyword Extraction: Identifies the most representative keywords for each topic.
Document Classification: Assigns documents to their most probable topics.
Visualization: Generates visual representations of topic distributions and word clouds.
Use Case: A researcher analyzing thousands of customer reviews can use this Skill to quickly identify the top 5 recurring themes (e.g., "customer service," "product quality," "pricing") and understand the sentiment associated with each.

Quick Start

Run the topic modeler script on the 'reviews.csv' file, using the 'text' column to extract 5 topics and save the results to 'topics.json'.

Frequently Asked Questions about topic-modeler

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract topics from a collection of text documents?▼

Topic modeling with LDA uncovers latent themes across a collection of documents by calculating word distributions. It identifies representative keywords for each topic, enabling document summarization and content categorization for large text volumes.

What is the best way to identify recurring themes in customer reviews?▼

Applying LDA topic modeling to customer reviews identifies recurring themes by extracting representative keywords. Running the script on a CSV text column outputs the top recurring themes and assigns each document to its most probable topic.

Can I use pandas and matplotlib for topic visualization?▼

Yes, topic modeling uses pandas for data manipulation and matplotlib for generating visual representations of topic distributions. The process also leverages the wordcloud library to generate visual word clouds for each extracted theme.

How do I run LDA topic modeling on a CSV file?▼

Execute the topic modeler script on a CSV file by specifying the target text column to analyze. The script extracts the defined number of topics and saves the resulting topic distributions and extracted keywords to a JSON file.

Does natural language processing topic modeling work with gensim?▼

Topic modeling uses the gensim library to perform Latent Dirichlet Allocation for natural language processing. It requires gensim and nltk for text processing to extract latent topics and identify representative keywords from document collections.