A Neural Topic Method Using a Large-Language-Model-in-the-Loop for Business Research

2026-03-03

AI Summary

This study addresses the limitations of existing topic models in business research, where topics are often ambiguous, weakly interpretable, and lack standardization, hindering their reliability as measurement instruments. To overcome these challenges, we propose LX Topic, a novel approach that integrates large language models into neural topic modeling through a closed-loop framework. Built upon the FASTopic architecture, LX Topic enhances topic coherence via word-level semantic alignment and confidence-weighted mechanisms while preserving the original document-topic distribution, and outputs standardized document-level topic proportions. We further implement an end-to-end web-based system that unifies topic discovery, refinement, and measurement. Experiments on large-scale Amazon and Yelp review datasets demonstrate that LX Topic consistently outperforms state-of-the-art models in topic quality, clustering, and classification performance, significantly improving interpretability, stability, and measurement validity.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Semantic Web

Application Category

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

Abstract

The growing use of unstructured text in business research makes topic modeling a central tool for constructing explanatory variables from reviews, social media, and open-ended survey responses, yet existing approaches function poorly as measurement instruments. Prior work shows that textual content predicts outcomes such as sales, satisfaction, and firm performance, but probabilistic models often generate conceptually diffuse topics, neural topic models are difficult to interpret in theory-driven settings, and large language model approaches lack standardization, stability, and alignment with document-level representations. We introduce LX Topic, a neural topic method that conceptualizes topics as latent linguistic constructs and produces calibrated document-level topic proportions for empirical analysis. LX Topic builds on FASTopic to ensure strong document representativeness and integrates large language model refinement at the topic-word level using alignment and confidence-weighting mechanisms that enhance semantic coherence without distorting document-topic distributions. Evaluations on large-scale Amazon and Yelp review datasets demonstrate that LX Topic achieves the highest overall topic quality relative to leading models while preserving clustering and classification performance. By unifying topic discovery, refinement, and standardized output in a web-based system, LX Topic establishes topic modeling as a reproducible, interpretable, and measurement-oriented instrument for marketing research and practice.

Problem

• topic modeling
• measurement instrument
• large language models
• interpretability
• document-level representation

Innovation

• neural topic modeling
• large language model in the loop
• topic coherence
• document-level topic proportions
• measurement-oriented NLP