A Comparative Study of Fuzzy Topic Models and LDA in terms of Interpretability

Abstract

In many domains that employ machine learning models, both high performing and interpretable models are needed. A typical machine learning task is text classification, where models are hardly interpretable. Topic models, used as topic embeddings, carry the potential to better understand the decisions made by text classification algorithms. With this goal in mind, we propose two new fuzzy topic models; FLSA-W and FLSA-V. Both models are derived from the topic model Fuzzy Latent Semantic Analysis (FLSA). After training each model ten times, we use the mean coherence score to compare the different models with the benchmark models Latent Dirichlet Allocation (LDA) and FLSA. Our proposed models generally lead to higher coherence scores and lower standard deviations than the benchmark models. These proposed models are specifically useful as topic embeddings in text classification, since the coherence scores do not drop for a high number of topics, as opposed to the decay that occurs with LDA and FLSA.