# Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstorming

arXiv: https://arxiv.org/abs/2509.16835 · HTML: https://mlsanity.com/paper/2509.16835/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Cross-lingual Contextualized Topic Models with Zero-shot Learning](https://mlsanity.com/paper/2004.07737/) (2020)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction](https://mlsanity.com/paper/2409.00265/) (2024)
- [Topic Modeling over Short Texts by Incorporating Word Embeddings](https://mlsanity.com/paper/1609.08496/) (2016)
- [Semantic-Driven Topic Modeling Using Transformer-Based Embeddings and Clustering Algorithms](https://mlsanity.com/paper/2410.00134/) (2024)
- [Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI](https://mlsanity.com/paper/2410.02609/) (2024)
- [SMART-Vision: Survey of Modern Action Recognition Techniques in Vision](https://mlsanity.com/paper/2501.13066/) (2025)
- [First Attempt at Building Parallel Corpora for Machine Translation of Northeast India's Very Low-Resource Languages](https://mlsanity.com/paper/2312.04764/) (2023)
- [Evaluating the Effectiveness of XAI Techniques for Encoder-Based Language Models](https://mlsanity.com/paper/2501.15374/) (2025)
- [A Unified Framework with Novel Metrics for Evaluating the Effectiveness of XAI Techniques in LLMs](https://mlsanity.com/paper/2503.05050/) (2025)
- [Explainability in Neural Networks for Natural Language Processing Tasks](https://mlsanity.com/paper/2412.18036/) (2024)
- [Explainable AI: XAI-Guided Context-Aware Data Augmentation](https://mlsanity.com/paper/2506.03484/) (2025)

## Built on by (most cited first, 2 of 2)

- [Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models](https://mlsanity.com/paper/2602.16608/) (2026)
- [Intelligent Scientific Literature Explorer using Machine Learning (ISLE)](https://mlsanity.com/paper/2512.12760/) (2025)

## Abstract (arXiv)

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.