Bridging Topic Modelling Outputs to Bayesian Hierarchical Model Using LLM and WordNet Parameter Labelling

Abstract

This study investigates the challenge of generating accurate and interpretable topic labels for integration into Bayesian Hierarchical Models (BHM), a critical step for interpretable probabilistic risk modelling from unstructured textual data. Using a corpus of 35,667 Malaysian business news articles published between 2019 and 2023, four topic modelling approaches, such as Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), Top2Vec, and BERTopic, were evaluated. Among these, NMF produced the most coherent and thematically consistent topics. To address the topic-labelling challenge, this study proposes an NLP-BHM framework that maps topic model outputs into a hierarchical Bayesian structure comprising interpretable topic labels and higher-level risk categories. Two semantic labelling strategies were examined: Large Language Model (LLM) prompting and WordNet-based semantic analysis. The proposed approaches enabled systematic topic interpretation and semantic clustering within the BHM framework. A case study on Malaysian business risks demonstrates that LLM-based labelling produced more coherent and contextually relevant results, while WordNet-based labelling provided a semantically consistent but vocabulary-limited alternative. Comparative results based on Mean Opinion Scores (MOS) highlight the effectiveness of LLM-based semantic labelling in improving interpretability for probabilistic business risk analysis.