Hierarchical Topic

Hierarchical topic modeling aims to uncover latent thematic structures within text corpora, organizing topics into hierarchies that reflect semantic relationships and granularity. Current research focuses on improving the coherence, interpretability, and efficiency of these models, employing techniques like hyperbolic geometry, neural nonnegative matrix factorization, and Bayesian methods to enhance topic discovery and representation. These advancements are improving the accuracy and speed of topic modeling, leading to better insights from large text datasets and enabling more effective applications in areas such as document classification, knowledge graph construction, and trend analysis.