Conceptual Topic Aggregation

2025-06-27

AI Summary

To address the low efficiency of manual analysis for large-scale text data and the poor interpretability of existing topic models, this paper proposes FAT-CAT—a novel topic modeling method that introduces Formal Concept Analysis (FCA) into topic modeling for the first time. FAT-CAT constructs concept lattices to enable hierarchical topic aggregation and visualization across document types and directory structures. By integrating topic modeling outputs with hierarchical directory organization, it generates semantically coherent and structurally traceable topic taxonomies. Experiments on the ETYNTKE dataset demonstrate that FAT-CAT significantly outperforms conventional approaches, achieving substantial improvements in topic interpretability, structural transparency, and analytical depth. The method establishes a new paradigm for efficient, human-understandable analysis of high-dimensional text data.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Enhancing interpretable topic modeling for large datasets
• Aggregating diverse topics into hierarchical structures
• Improving visualization of latent semantic patterns

Innovation

• Uses Formal Concept Analysis for topic aggregation
• Constructs hierarchical concept lattice for visualization
• Handles diverse topics and file types efficiently