Florian Eichin, Carolin M. Schuster, Georg Groh, Michael A. Hedderich
Topic modeling is a key method in text analysis, but existing approaches are limited by assuming one topic per document or fail to scale efficiently for large, noisy datasets of short texts. We introduce Semantic Component Analysis (SCA), a novel topic modeling technique that overcomes these limitations by discovering multiple, nuanced semantic components beyond a single topic in short texts which we accomplish by introducing a decomposition step to the clustering-based topic modeling framework. We evaluate SCA on Twitter datasets in English, Hausa and Chinese. It achieves competetive coherence and diversity compared to BERTopic, while uncovering at least double the semantic components and maintaining a noise rate close to zero. Furthermore, SCA is scalable and effective across languages, including an underrepresented one.
| Comments: | 5 pages, 3 figures, code: this https URL |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2410.21054 [cs.CL] |
| (or arXiv:2410.21054v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2410.21054Focus to learn more |
Submission history
From: Florian Eichin [view email]
[v1] Mon, 28 Oct 2024 14:09:52 UTC (7,222 KB)
[v2] Mon, 16 Dec 2024 13:43:50 UTC (7,225 KB)
Here is an evaluation of the provided abstract on Semantic Component Analysis (SCA) for topic modeling:
Strengths:
- Clear Identification of Limitations in Prior Work:
The abstract succinctly identifies two key limitations in existing topic modeling approaches: the assumption of one topic per document and inefficiency in scaling to large, noisy short-text datasets. - Novelty and Technical Contribution:
It introduces Semantic Component Analysis (SCA) as a new topic modeling technique that overcomes those limitations by discovering multiple nuanced semantic components per document through a decomposition step in clustering-based topic modeling. - Multilingual and Cross-Domain Evaluation:
The evaluation on diverse Twitter datasets, including English, Hausa (a low-resource language), and Chinese, demonstrates the model’s scalability and effectiveness across languages, enhancing the practical relevance of this work. - Strong Empirical Claims:
SCA reportedly achieves competitive topic coherence and diversity relative to BERTopic, a recognized state-of-the-art method, while uncovering at least twice the semantic components and maintaining a very low noise rate. - Focus on Short Texts:
Emphasizing improved topic modeling in the challenging domain of short texts addresses a critical gap where many traditional approaches perform poorly.
Areas for Improvement:
- Typographical Consistency:
“competetive” should be corrected to “competitive.” Minor typographical issues can distract from the professionalism of the abstract. - Quantitative Details:
The abstract could be strengthened by including specific metrics or quantitative results to substantiate claims on coherence, diversity, and noise reduction. - More Explanation of Decomposition:
The term “decomposition step” is briefly mentioned but could benefit from a clearer, lay explanation in the abstract to help readers grasp how it enables multiple semantic components to be discovered. - Broader Context of Use:
Mentioning potential applications or domains where SCA could be impactful beyond Twitter data (such as other short-text domains) would strengthen the significance.
Overall Impression:
The abstract presents a novel and promising topic modeling method that addresses critical limitations of existing approaches for short texts. It highlights strong multilingual capabilities and improved semantic granularity while maintaining robustness against noise. Including more quantitative evaluation highlights and a clearer explanation of the decomposition technique would make this contribution even more compelling.
