Interactive Visual Exploration of Topic Models using Graphs

TL;DR

This paper introduces a graph-based visualization method for probabilistic topic models, enhancing interpretability and aiding information retrieval in large text corpora, exemplified with financial patents.

Contribution

It presents a novel graph visualization approach for topic models that reveals topic relationships and supports document retrieval, addressing visualization gaps in existing methods.

Findings

Graph visualization reveals topic similarities and meanings.

Supports document retrieval by topic or subset.

Applied to financial patents for demonstration.

Abstract

Probabilistic topic modeling is a popular and powerful family of tools for uncovering thematic structure in large sets of unstructured text documents. While much attention has been directed towards the modeling algorithms and their various extensions, comparatively few studies have concerned how to present or visualize topic models in meaningful ways. In this paper, we present a novel design that uses graphs to visually communicate topic structure and meaning. By connecting topic nodes via descriptive keyterms, the graph representation reveals topic similarities, topic meaning and shared, ambiguous keyterms. At the same time, the graph can be used for information retrieval purposes, to find documents by topic or topic subsets. To exemplify the utility of the design, we illustrate its use for organizing and exploring corpora of financial patents.