Improving topic modeling performance on social media through semantic relationships within biomedical terminology.

ABSTRACT: Topic modeling utilizes unsupervised machine learning to detect underlying themes within texts and has been deployed routinely to analyze social media for insights into healthcare issues. However, the inherent messiness of social media hinders the full realization of this technique's potential. As such, we hypothesized that restricting medical concepts in social media texts to specific related semantic types and applying topic modeling to these concepts could be a feasible approach to overcome the challenge of traditional topic modeling for social media texts. Therefore, we developed a semantic-type-based topic modeling pipeline to discover self-reported health-related topics. This pipeline integrated semantic type information and Systematized Medical Nomenclature for Medicine (SNOMED) pre

SUBMITTER: Xin Y 

PROVIDER: S-EPMC11845042 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature