An Automatic Approach for Document-level Topic Model Evaluation

Abstract

Topic models jointly learn topics and document-level topic distribution. Extrinsic evaluation of topic models tends to focus exclusively on topic-level evaluation, e.g. by assessing the coherence of topics. We demonstrate that there can be large discrepancies between topic-and documentlevel model quality, and that basing model evaluation on topic-level analysis can be highly misleading. We propose a method for automatically predicting topic model quality based on analysis of documentlevel topic allocations, and provide empirical evidence for its robustness.