Bin Choi, Brian Ondov, Huan He and Hua Xu
Abstract: The rapid growth of biomedical research has led to an overwhelming volume of literature, making it challenging for researchers to efficiently explore and analyze. While existing tools provide an overview of semantic maps and publication distributions, further refinement is needed to reveal fine-grained nuances and hierarchical topics. To address this, we propose a novel method for hierarchical topic modeling and label generation on 2D semantic maps. Our approach consists of three steps. First, we apply density-based hierarchical clustering using HDBSCAN to construct a topic tree. Second, we employ a novel tree-based TF-IDF method to refine topic representation using MeSH terms, capturing both general and local topic distinctions. Finally, we optimize label positioning using a centroid-based method to enhance visualization.