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

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Λεπτομέρειες βιβλιογραφικής εγγραφής
Εκδόθηκε σε:	arXiv.org (Dec 20, 2024), p. n/a
Κύριος συγγραφέας:	Xu, Jianfei
Έκδοση:	Cornell University Library, arXiv.org
Θέματα:	Feature extraction Algorithms Labels Cosmetics
Διαθέσιμο Online:	Citation/Abstract Full text outside of ProQuest
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Περιγραφή
Περίληψη:	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
Πηγή:	Engineering Database