Conceptual Topic Aggregation
This addresses the need for better interpretability in topic modeling for researchers and analysts dealing with diverse data, though it appears incremental as it builds on existing methods.
The paper tackles the problem of interpretable topic modeling for large-scale textual datasets by proposing FAT-CAT, a method based on Formal Concept Analysis, which in a case study on the ETYNTKE dataset provides more meaningful and interpretable insights than existing techniques.
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.