Industry-Aligned Granular Topic Modeling

2026-01-16

AI Summary

Existing topic models struggle to generate fine-grained and interpretable topics that meet industrial requirements, limiting their deep integration into real-world business applications. To address this challenge, this work proposes TIDE, a novel framework that systematically incorporates large language models into fine-grained topic modeling for the first time. By integrating business-oriented modules—including text summarization, hierarchical topic construction, and knowledge distillation—TIDE substantially enhances topic granularity, interpretability, and practical utility. Extensive experiments on multiple public and real-world commercial datasets demonstrate that TIDE consistently outperforms state-of-the-art methods, confirming its effectiveness and superiority in industrial settings.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Topic modeling has extensive applications in text mining and data analysis across various industrial sectors. Although the concept of granularity holds significant value for business applications by providing deeper insights, the capability of topic modeling methods to produce granular topics has not been thoroughly explored. In this context, this paper introduces a framework called TIDE, which primarily provides a novel granular topic modeling method based on large language models (LLMs) as a core feature, along with other useful functionalities for business applications, such as summarizing long documents, topic parenting, and distillation. Through extensive experiments on a variety of public and real-world business datasets, we demonstrate that TIDE's topic modeling approach outperforms modern topic modeling methods, and our auxiliary components provide valuable support for dealing with industrial business scenarios. The TIDE framework is currently undergoing the process of being open sourced.

Problem

• granular topic modeling
• topic modeling
• business applications
• large language models
• industrial text mining

Innovation

• granular topic modeling
• large language models
• TIDE framework
• topic parenting
• industrial text mining