Conceptual Topic Aggregation

TL;DR

This paper introduces FAT-CAT, a Formal Concept Analysis-based method for aggregating and visualizing topics in large textual datasets, improving interpretability over traditional topic modeling techniques.

Contribution

The paper presents a novel FCA-based approach for hierarchical topic aggregation and visualization, enhancing interpretability of large-scale textual data.

Findings

FAT-CAT produces more meaningful topic groupings.

FCA-based visualization offers clearer insights.

Outperforms existing methods in interpretability.

Abstract

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.