Topic Modeling of the SrpELTeC Corpus: A Comparison of NMF, LDA, and BERTopic

Topic modeling is an effective way to gain insight into large amounts of data. Some of the most widely used topic models are Latent Dirichlet allocation (LDA) and Nonnegative Matrix Factorization (NMF). However, new ways to mine topics have emerged with the rise of self-attention models and pre-trained language models. BERTopic represents the current state-of-the-art when it comes to modeling topics. In this paper, we compared LDA, NMF, and BERTopic performance on literary texts in the Serbian language, both quantitatively by measuring Topic Coherency (TC) and Topic Diversity (TD), and by conducting a qualitative evaluation of the obtained topics. Additionally, for BERTopic, we compared multilingual sentence transformer embeddings with the Jerteh-355 monolingual embeddings for Serbian. NMF yielded the best Topic Coherency results, while BERTopic with Jerteh-355 embeddings gave the best Topic Diveristy. The monolingual Serbian Jerteh-355 embeddings also outperformed sentence transformer embeddings in both TC and TD.