An empirical comparison of latent sematic models for applications in industry

Abstract

In recent years, topic models have been gaining popularity to perform classification of text from several web sources (from social networks to digital media). However, after working for many years in the web text mining area we have notice that assessing the quality of topics discovered is still an open problem, quite hard to solve. In this paper, we evaluated four latent semantic models using two metrics: coherence and interpretability which are the most used. We show how these pure mathematical metrics fall short to asses topics quality. Experiments were performed over a dataset of 21,863 text reclamation.