Uncertainty-aware Topic Modeling Visualization
This addresses the issue of uncertainty in topic modeling for text analysts, but it is incremental as it builds on existing visualization methods.
They tackled the problem of uncertainty in LDA-based topic modeling by proposing a visual uncertainty-aware analysis that captures uncertainty through ensembles and enhances existing visualizations to convey it, applying it to a text corpus to document its impact.
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.