A Fusion Approach of Dependency Syntax and Sentiment Polarity for Feature Label Extraction in Commodity Reviews

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Detaylı Bibliyografya
Yayımlandı:	arXiv.org (Dec 20, 2024), p. n/a
Yazar:	Xu, Jianfei
Baskı/Yayın Bilgisi:	Cornell University Library, arXiv.org
Konular:	Feature extraction Algorithms Labels Cosmetics
Online Erişim:	Citation/Abstract Full text outside of ProQuest
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

Diğer Bilgiler
Özet:	This study analyzes 13,218 product reviews from JD.com, covering four categories: mobile phones, computers, cosmetics, and food. A novel method for feature label extraction is proposed by integrating dependency parsing and sentiment polarity analysis. The proposed method addresses the challenges of low robustness in existing extraction algorithms and significantly enhances extraction accuracy. Experimental results show that the method achieves an accuracy of 0.7, with recall and F-score both stabilizing at 0.8, demonstrating its effectiveness. However, challenges such as dependence on matching dictionaries and the limited scope of extracted feature tags require further investigation in future research.
ISSN:	2331-8422
Kaynak:	Engineering Database