Chrono Clustering: A Novel Methodology for Dynamic Topic Trend Analysis

We develop a new framework, Chrono Clustering, to uncover and portray the progression of topics over time, where dual clustering, enhanced keyword extraction and hypergraph visualization are integrated. With K-Means on embedding space for base topic discovery and one-iteration K-Medoids for aligning base topics to other timeframes, our novel method could effectively quantify temporal topic shifts. Evaluated on AI conference datasets, Chrono Clustering boosts detection of trending topics that match real-world advances. It generates novel Chrono Graph to intuitively show dynamic topic progressions, demonstrating promising values for temporal topic modeling