Alocação latente de Dirichlet para modelagem de tópicos em dissertações de mestrado em estatística e áreas correlatas no Brasil

Abstract

This master’s thesis addresses the topic modeling of master’s theses in statistics and related areas in Brazil, through Latent Dirichlet Allocation models. The main objective of the work is to infer the latent topics covered in these theses. First, the construction of a corpus of documents is discussed and presented, composed of the most recent theses from different Higher Education Institutions in Brazil, manually extracted from the web pages of each of the analyzed master’s programs. The inferential procedure adopted for the Latent Dirichlet Allocation model consists of Markov chain Monte Carlo methods and variational inference. Different methods for choosing the number of topics are also discussed, including information criteria such as Akaike, Bayesian, Deviance, Watanabe-Akaike, and metrics based on the coherence of the inferred latent topics. The adopted methodology provides an in-depth understanding of the predominant topics in this corpus.