Integrating Technical Analysis into Sentiment Analysis: An ASTE Framework for Electric Car Purchase Decision Support Based on LLMs and Semantic BNF

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URI: http://hdl.handle.net/10952/9362
DOI: 10.3390/electronics14051020

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electronics-14-01020.pdf (1.14 MB)

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2025-03-04

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Abstract

This study enhances Aspect Sentiment Triplet Extraction (ASTE) by integrating Large Language Models (LLMs) and a semantic Backus-Naur Form (BNF) framework to analyze expert reviews on electric vehicles. A 2-tuple fuzzy linguistic model improves sentiment representation, ensuring interpretability. Applied to YouTube reviews of top-selling EVs, the method effectively structures technical and qualitative data for decision support.

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Administración y Dirección de EmpresasMarketingIngeniería, Industria y Construcción

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UniversidadUniveristyRepositorioRepositoryMarketingAspect sentiment triplet extractionLarge language modelsSemantic Backus–Naur FormFuzzy linguistic modelsElectric vehiclesExpert reviews

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journal article

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Carrasco-Aguilar, Á.; Carmona-Martínez, M.M.; Parra-Meroño, M.C.; Souto-Romero, M. Integrating Technical Analysis into Sentiment Analysis: An ASTE Framework for Electric Car Purchase Decision Support Based on LLMs and Semantic BNF. Electronics 2025, 14, 1020.

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional