Uncertainty-aware Topic Modeling Visualization

TL;DR

This paper introduces a visualization approach that captures and displays uncertainties in topic modeling results, enhancing interpretability by visualizing ensembles of models and their uncertainties.

Contribution

It proposes a novel method to visualize uncertainty in topic modeling by computing ensembles and integrating uncertainty measures into visualization techniques.

Findings

Enables visualization of uncertainty in topic modeling results.

Improves interpretability of topic models by conveying uncertainty.

Applied to a text corpus to demonstrate impact of uncertainty on analysis.

Abstract

Topic modeling is a state-of-the-art technique for analyzing text corpora. It uses a statistical model, most commonly Latent Dirichlet Allocation (LDA), to discover abstract topics that occur in the document collection. However, the LDA-based topic modeling procedure is based on a randomly selected initial configuration as well as a number of parameter values than need to be chosen. This induces uncertainties on the topic modeling results, and visualization methods should convey these uncertainties during the analysis process. We propose a visual uncertainty-aware topic modeling analysis. We capture the uncertainty by computing topic modeling ensembles and propose measures for estimating topic modeling uncertainty from the ensemble. Then, we propose to enhance state-of-the-art topic modeling visualization methods to convey the uncertainty in the topic modeling process. We visualize the entire ensemble of topic modeling results at different levels for topic and document analysis. We apply our visualization methods to a text corpus to document the impact of uncertainty on the analysis.