[2509.16835] Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstorming

Computer Science > Computation and Language arXiv:2509.16835 (cs) [Submitted on 20 Sep 2025 (v1), last revised 19 Mar 2026 (this version, v2)] Title:Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstorming Authors:Melkamu Abay Mersha, Jugal Kalita View a PDF of the paper titled Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstorming, by Melkamu Abay Mersha and 1 other authors View PDF HTML (experimental) Abstract:Virtual brainstorming sessions have become a central component of collaborative problem solving, yet the large volume and uneven distribution of ideas often make it difficult to extract valuable insights efficiently. Manual coding of ideas is time-consuming and subjective, underscoring the need for automated approaches to support the evaluation of group creativity. In this study, we propose a semantic-driven topic modeling framework that integrates four modular components: transformer-based embeddings (Sentence-BERT), dimensionality reduction (UMAP), clustering (HDBSCAN), and topic extraction with refinement. The framework captures semantic similarity at the sentence level, enabling the discovery of coherent themes from brainstorming transcripts while filtering noise and identifying outliers. We evaluate our approach on structured Zoom brainstorming sessions involving student groups tasked with improving their university. Results demonstrate that our model achieves higher topic coherence compared to established meth...