TriTopic: Tri-Modal Graph-Based Topic Modeling with Iterative Refinement and Archetypes
arXiv:2602.19079v1 Announce Type: new Abstract: Topic modeling extracts latent themes from large text collections, but leading approaches like BERTopic face critical limitations: stochastic instability, loss of lexical precision ("Embedding Blur"), and reliance on a single data perspective. We present TriTopic, a framework that addresses these weaknesses through a tri-modal graph fusing semantic embeddings, TF-IDF, and metadata. Three core innovations drive its performance: hybrid graph construction via Mutual kNN and Shared Nearest Neighbors to eliminate noise and combat the curse of dimensionality; Consensus Leiden Clustering for reproducible, stable partitions; and Iterative Refinement that sharpens embeddings through dynamic centroid-pulling. TriTopic also replaces the "average document" concept with archetype-based topic representations defined by boundary cases rather than centers alone. In benchmarks across 20 Newsgroups, BBC News, AG News, and Arxiv, TriTopic achieves the
arXiv:2602.19079v1 Announce Type: new Abstract: Topic modeling extracts latent themes from large text collections, but leading approaches like BERTopic face critical limitations: stochastic instability, loss of lexical precision ("Embedding Blur"), and reliance on a single data perspective. We present TriTopic, a framework that addresses these weaknesses through a tri-modal graph fusing semantic embeddings, TF-IDF, and metadata. Three core innovations drive its performance: hybrid graph construction via Mutual kNN and Shared Nearest Neighbors to eliminate noise and combat the curse of dimensionality; Consensus Leiden Clustering for reproducible, stable partitions; and Iterative Refinement that sharpens embeddings through dynamic centroid-pulling. TriTopic also replaces the "average document" concept with archetype-based topic representations defined by boundary cases rather than centers alone. In benchmarks across 20 Newsgroups, BBC News, AG News, and Arxiv, TriTopic achieves the highest NMI on every dataset (mean NMI 0.575 vs. 0.513 for BERTopic, 0.416 for NMF, 0.299 for LDA), guarantees 100% corpus coverage with 0% outliers, and is available as an open-source PyPI library.
Executive Summary
The article introduces TriTopic, a novel framework for topic modeling that addresses the limitations of existing approaches like BERTopic. TriTopic utilizes a tri-modal graph, combining semantic embeddings, TF-IDF, and metadata, and incorporates innovative techniques such as hybrid graph construction, Consensus Leiden Clustering, and Iterative Refinement. The framework achieves state-of-the-art performance on various benchmarks, ensuring 100% corpus coverage with 0% outliers. The open-source library is available on PyPI, offering a significant improvement over existing topic modeling methods.
Key Points
- ▸ TriTopic addresses stochastic instability, Embedding Blur, and single data perspective limitations
- ▸ The framework utilizes a tri-modal graph, combining semantic embeddings, TF-IDF, and metadata
- ▸ Innovative techniques include hybrid graph construction, Consensus Leiden Clustering, and Iterative Refinement
Merits
Improved Performance
TriTopic achieves the highest NMI on every dataset, outperforming BERTopic, NMF, and LDA
Robustness and Stability
The framework guarantees 100% corpus coverage with 0% outliers, ensuring reliable results
Demerits
Computational Complexity
The use of hybrid graph construction and Iterative Refinement may increase computational requirements
Interpretability
The introduction of archetype-based topic representations may require additional expertise to interpret results
Expert Commentary
The introduction of TriTopic marks a significant advancement in topic modeling, addressing long-standing limitations of existing approaches. The framework's innovative techniques, such as hybrid graph construction and Iterative Refinement, demonstrate a deep understanding of the complexities involved in extracting latent themes from large text collections. While the increased computational complexity and potential interpretability challenges may require careful consideration, the benefits of improved performance and robustness make TriTopic an attractive solution for various applications.
Recommendations
- ✓ Further research should focus on optimizing TriTopic's computational efficiency to facilitate wider adoption
- ✓ The development of user-friendly interfaces and documentation can help non-experts leverage the framework's capabilities
