AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women.

ABSTRACT: Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditional topic modeling, such as Latent Dirichlet Allocation (LDA), faces limitations in capturing the semantic relationships in the text document although it has been widely applied in text mining. BERTopic, created in 2022, leveraged advances in deep learning and can capture the contextual relationships between words. In this work, we integrated Artificial Intelligence (AI) modules to LDA and BERTopic and provided a comprehensive comparison on the analysis of prescription opioid-related cardiovascular risks in women. Opioid use can increase the risk of cardiovascular problems in women such as arrhythmia, hypotension etc. 1,837 abstracts were ret

SUBMITTER: Ma L 

PROVIDER: S-EPMC11906279 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature