Interactive Visual Exploration of Topic Models using Graphs
This addresses the need for better visualization tools in topic modeling, particularly for researchers and analysts working with large text datasets, though it is incremental as it builds on existing topic modeling methods.
The paper tackles the problem of visualizing topic models by introducing a graph-based design that connects topic nodes with keyterms to reveal topic similarities, meanings, and ambiguous terms, and demonstrates its utility for exploring financial patent corpora.
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.