GPTopic: Dynamic and Interactive Topic Representations

MCML Authors

Sebastian Fischer

→ Group Bernd Bischl
 Statistical Learning and Data Science

Abstract

Topic modeling seems to be almost synonymous with generating lists of top words to represent topics within large text corpora. However, deducing a topic from such list of individual terms can require substantial expertise and experience, making topic modelling less accessible to people unfamiliar with the particularities and pitfalls of top-word interpretation. A topic representation limited to top-words might further fall short of offering a comprehensive and easily accessible characterization of the various aspects, facets and nuances a topic might have. To address these challenges, we introduce GPTopic, a software package that leverages Large Language Models (LLMs) to create dynamic, interactive topic representations. GPTopic provides an intuitive chat interface for users to explore, analyze, and refine topics interactively, making topic modeling more accessible and comprehensive.

misc

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Preprint

Mar. 2024

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Authors

A. Reuter • A. Thielmann • C. Weisser • S. F. Fischer • B. Säfken

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Links

GitHub

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Research Area

 A1 | Statistical Foundations & Explainability