基于语义的课题建模用于分析虚拟头脑风暴中的创造力
Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstorming
AI总结：
本文提出一种语义驱动的课题建模框架，通过整合Transformer嵌入、降维、聚类和课题提取模块，提升虚拟头脑风暴中创造力分析的效率与准确性。
摘要：
虚拟头脑风暴会议已成为协作问题解决的核心组成部分，但大量且分布不均的想法使高效提取有价值见解变得困难。手动编码想法耗时且主观，凸显了需要自动化方法来支持群体创造力评估的必要性。本文提出一种语义驱动的课题建模框架，整合四个模块：基于Transformer的嵌入（Sentence-BERT）、降维（UMAP）、聚类（HDBSCAN）和课题提取与精炼。该框架在句子层面捕捉语义相似性，使从头脑风暴记录中发现连贯主题并过滤噪声和识别异常值成为可能。我们在结构化的Zoom头脑风暴会议中评估了该方法，涉及学生小组的任务是改进大学。结果表明，我们的模型在主题连贯性方面优于LDA、ETM和BERTopic等现有方法，平均连贯性得分为0.687（CV），显著优于基线。除了改进的性能，该模型还提供了对探索主题深度和多样性可解释的见解，支持群体创造力的收敛和发散维度。本工作突显了基于嵌入的课题建模在分析协作构想中的潜力，并贡献了一个高效且可扩展的框架，用于研究同步虚拟会议中的创造力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
