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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.22309 |
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| _version_ | 1866918073455345664 |
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| author | Gutekunst, Klara M. Dürrschnabel, Dominik Hirth, Johannes Stumme, Gerd |
| author_facet | Gutekunst, Klara M. Dürrschnabel, Dominik Hirth, Johannes Stumme, Gerd |
| contents | The vast growth of data has rendered traditional manual inspection infeasible, necessitating the adoption of computational methods for efficient data exploration. Topic modeling has emerged as a powerful tool for analyzing large-scale textual datasets, enabling the extraction of latent semantic structures. However, existing methods for topic modeling often struggle to provide interpretable representations that facilitate deeper insights into data structure and content. In this paper, we propose FAT-CAT, an approach based on Formal Concept Analysis (FCA) to enhance meaningful topic aggregation and visualization of discovered topics. Our approach can handle diverse topics and file types -- grouped by directories -- to construct a concept lattice that offers a structured, hierarchical representation of their topic distribution. In a case study on the ETYNTKE dataset, we evaluate the effectiveness of our approach against other representation methods to demonstrate that FCA-based aggregation provides more meaningful and interpretable insights into dataset composition than existing topic modeling techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conceptual Topic Aggregation Gutekunst, Klara M. Dürrschnabel, Dominik Hirth, Johannes Stumme, Gerd Artificial Intelligence Computation and Language Discrete Mathematics Machine Learning 06B99 I.2.4; I.2.7 The vast growth of data has rendered traditional manual inspection infeasible, necessitating the adoption of computational methods for efficient data exploration. Topic modeling has emerged as a powerful tool for analyzing large-scale textual datasets, enabling the extraction of latent semantic structures. However, existing methods for topic modeling often struggle to provide interpretable representations that facilitate deeper insights into data structure and content. In this paper, we propose FAT-CAT, an approach based on Formal Concept Analysis (FCA) to enhance meaningful topic aggregation and visualization of discovered topics. Our approach can handle diverse topics and file types -- grouped by directories -- to construct a concept lattice that offers a structured, hierarchical representation of their topic distribution. In a case study on the ETYNTKE dataset, we evaluate the effectiveness of our approach against other representation methods to demonstrate that FCA-based aggregation provides more meaningful and interpretable insights into dataset composition than existing topic modeling techniques. |
| title | Conceptual Topic Aggregation |
| topic | Artificial Intelligence Computation and Language Discrete Mathematics Machine Learning 06B99 I.2.4; I.2.7 |
| url | https://arxiv.org/abs/2506.22309 |