Thirty years of public speaking anxiety research: topic modeling and semantic trend forecasting using LDA-Word2Vec integration.

Public speaking anxiety (PSA) is a widespread condition with profound psychological, educational, and occupational consequences. Despite increasing academic attention, few studies have systematically mapped the thematic development of PSA research or anticipated future directions. This study employs a semantically enriched framework integrating latent Dirichlet allocation (LDA) with Word2Vec to analyze 990 peer-reviewed journal articles (1995-2024) retrieved from the Web of Science Core Collection, spanning SSCI, SCI-Expanded, ESCI, and A&HCI indices. Nine major thematic clusters are identified, covering topics such as mental health distress, physiological reactivity, language apprehension, social phobia, and virtual reality interventions. Thematic evolution across four historical phases reveals growing conceptual convergence and an increasing focus on digital anxiety and assessment technologies. A Sankey diagram illustrates patterns of thematic continuity and transformation, highlighting both enduring topics and emerging lines of inquiry. To predict future research frontiers, two novelty indicators-Cluster Novelty and Topic Novelty-are introduced. Clusters focused on language learning (Topic 4), virtual therapy (Topic 6), and psychometric tools (Topic 8) emerge as the most temporally novel. This study provides a comprehensive, data-driven mapping of PSA research and offers methodological guidance for future interdisciplinary bibliometric analyses.