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

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Bibliographic Details
Published in:	arXiv.org (Dec 20, 2024), p. n/a
Main Author:	Xu, Jianfei
Published:	Cornell University Library, arXiv.org
Subjects:	Feature extraction Algorithms Labels Cosmetics
Online Access:	Citation/Abstract Full text outside of ProQuest
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Description
Abstract:	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
Source:	Engineering Database