Proposing Topic Models and Evaluation Frameworks for Analyzing Associations with External Outcomes: An Application to Leadership Analysis Using Large-Scale Corporate Review Data

2026-04-20 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Social Sciences & Behavioral Studies · Depth: Expert, quick

Summary

A new study proposes a novel topic modeling method and an evaluation framework designed for analyzing associations between text data and external outcomes, particularly in computational social science and organizational research. Existing topic modeling approaches often fail to simultaneously achieve interpretability, topic specificity (alignment with concrete actions), and polarity stance consistency (avoiding mixed sentiment within a topic). This research addresses these limitations by leveraging large language models to generate topics that meet these criteria. Applied to leadership analysis using employee reviews from OpenWork, a prominent Japanese corporate review platform, the proposed method demonstrates improved interpretability, specificity, and polarity consistency. Furthermore, it yields topics with higher explanatory power in analyses of external outcomes like employee morale, offering a generalized approach for such topic analyses.

Key takeaway

For organizational researchers or computational social scientists analyzing text data for external outcomes, you should consider adopting this LLM-driven topic modeling approach. It offers superior interpretability and topic specificity, which can significantly enhance the explanatory power of your analyses, especially when examining factors like employee morale from corporate reviews. Integrating this method could lead to more robust and actionable insights from your qualitative data.

Key insights

Leveraging LLMs for topic modeling improves interpretability, specificity, and sentiment consistency for external outcome analysis.

Principles

• Topic models must balance interpretability, specificity, and polarity consistency.
• External outcome analysis benefits from highly specific and consistent topics.

Method

The method uses large language models to generate topics, then evaluates them using a framework that explicitly incorporates topic specificity and polarity stance consistency, including automated metrics.

In practice

• Apply LLM-based topic modeling to corporate review data.
• Use the proposed framework for evaluating topic specificity and polarity consistency.

Topics

• Topic Modeling
• Large Language Models
• Evaluation Frameworks
• Leadership Analysis
• Corporate Review Data

Best for: NLP Engineer, AI Scientist, Data Scientist, Research Scientist

Related on AIssential

• Proposing Topic Models and Evaluation Frameworks for Analyzing Associations with External Outcomes: An Application to Leadership Analysis Using Large-Scale Corporate Review Data — LLM-enhanced topic modeling improves interpretability, specificity, and polarity consistency for outcome-oriented text analysis.
• Experimental Evaluation of Topic Modeling Methods for Categorizing Irregularities in Health-related news — LSA topic modeling effectively categorizes health news irregularities, enhancing public health audit efficiency.
• Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles — Fine-tuned LLMs can develop spurious sentiment-ideology couplings, diverging from human judgment in political news annotation.
• Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport — Integrating human-provided goals directly into topic modeling improves topic interpretability, diversity, and goal alignment.
• When Better Codebooks Are Not Enough: Predictive Performance and Behavioral Reliability in LLM Political Event Coding — LLM accuracy in codebook-guided tasks does not guarantee behavioral reliability or adherence to underlying coding logic.
• Exploring a New Competency Modeling Process with Large Language Models — LLMs can transform expert-dependent competency modeling into a data-driven, evaluable analytical process.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.