  Selecting the number of topics in LDA models is considered to be a difficult
task, for which alternative approaches have been proposed. The performance of
the recently developed singular Bayesian information criterion (sBIC) is
evaluated and compared to the performance of alternative model selection
criteria. The sBIC is a generalization of the standard BIC that can be
implemented to singular statistical models. The comparison is based on Monte
Carlo simulations and carried out for several alternative settings, varying
with respect to the number of topics, the number of documents and the size of
documents in the corpora. Performance is measured using different criteria
which take into account the correct number of topics, but also whether the
relevant topics from the DGPs are identified. Practical recommendations for LDA
model selection in applications are derived.
