TaSC-LLM: A Large Language Model–Enabled Business Intelligence Framework for Topic Analytics in Live-Streaming E-Commerce Systems
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Le résumé fourni par la source
In live-streaming e-commerce systems, massive volumes of user-generated danmaku constitute a critical yet underutilized source of business intelligence. However, transforming such unstructured, noisy, and highly context-dependent textual data into structured and actionable knowledge remains a fundamental challenge for enterprise information systems. To address this issue, this study proposes TaSC-LLM, an LLM-enabled topic recognition method for constructing interpretable topic measurements from unstructured user-generated content. The proposed framework integrates topic taxonomy construction and zero-shot classification into a unified semantic reasoning pipeline. Unlike conventional topic modeling or supervised classification approaches, TaSC-LLM leverages chain-of-thought reasoning, multi-stage taxonomy induction, sliding window context modeling, and self-consistency verification to eliminate reliance on predefined label spaces and annotated training data. This design allows the system to dynamically construct and update topic taxonomies while ensuring interpretability, robustness, and cross-scenario adaptability. Empirical evaluation on three large-scale live-streaming e-commerce danmaku datasets shows that TaSC-LLM achieves strong taxonomy coverage, classification accuracy, and agreement with expert annotations. The findings suggest that LLM-based reasoning can help convert unstructured user-generated text into interpretable topic measures for downstream empirical and managerial analysis. While the present evaluation is conducted offline, TaSC-LLM provides a methodological foundation for future business applications that can be further examined under multi-session, multi-platform, and deployment-oriented conditions.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- TaSC-LLM: A Large Language Model–Enabled Business Intelligence Framework for Topic Analytics in Live-Streaming E-Commerce Systems
- Date Crossref
- 03/08/2026
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Chinese Academy of Sciences pays non établi dans la noticeUniversité ou école supérieure
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Beijing Institute of Mathematical Sciences and Applications pays non établi dans la noticeUniversité ou école supérieure
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Hebei Normal University pays non établi dans la noticeUniversité ou école supérieure
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School of Economics and Management pays non établi dans la noticeUniversité ou école supérieure
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Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems pays non établi dans la noticeStructure de recherche
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School of Mathematical Sciences pays non établi dans la noticeUniversité ou école supérieure
University of Chinese Academy of Sciences, Beijing Institute of Mathematical Sciences and Applications et Hebei Normal University, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.