A Statistical Approach for Optimal Topic Model Identification

Latent Dirichlet Allocation is a popular machine-learning technique that identifies latent structures in a corpus of documents. This paper addresses the ongoing concern that formal procedures for determining the optimal LDA configuration do not exist by introducing a set of parametric tests that rely on the assumed multinomial distribution specification underlying the original LDA model. Our methodology defines a set of rigorous statistical procedures that identify and evaluate the optimal topic model. The U.S. Presidential Inaugural Address Corpus is used as a case study to show the numerical results. We find that 92 topics best describe the corpus. We further validate the method through a simulation study confirming the superiority of our approach compared to other standard heuristic metrics like the perplexity index. [abs] [ pdf ][ bib ] © JMLR 2022. (edit, beta)