Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstorming Article Swipe
Melkamu Abay Mersha
,
Jayantee Kalita
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2509.16835
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2509.16835
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 methods such as LDA, ETM, and BERTopic, with an average coherence score of 0.687 (CV), outperforming baselines by a significant margin. Beyond improved performance, the model provides interpretable insights into the depth and diversity of topics explored, supporting both convergent and divergent dimensions of group creativity. This work highlights the potential of embedding-based topic modeling for analyzing collaborative ideation and contributes an efficient and scalable framework for studying creativity in synchronous virtual meetings.
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- Type
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- en
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- http://arxiv.org/abs/2509.16835
- https://arxiv.org/pdf/2509.16835
- OA Status
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- OpenAlex ID
- https://openalex.org/W4415252678
All OpenAlex metadata
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https://openalex.org/W4415252678Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2509.16835Digital Object Identifier
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Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual BrainstormingWork title
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2025Year of publication
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2025-09-20Full publication date if available
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Melkamu Abay Mersha, Jayantee KalitaList of authors in order
- Landing page
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https://arxiv.org/abs/2509.16835Publisher landing page
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https://arxiv.org/pdf/2509.16835Direct link to full text PDF
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