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

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Xuất bản năm:	arXiv.org (Dec 20, 2024), p. n/a
Tác giả chính:	Xu, Jianfei
Được phát hành:	Cornell University Library, arXiv.org
Những chủ đề:	Feature extraction Algorithms Labels Cosmetics
Truy cập trực tuyến:	Citation/Abstract Full text outside of ProQuest
Các nhãn:	Thêm thẻ Không có thẻ, Là người đầu tiên thẻ bản ghi này!

Miêu tả
Bài tóm tắt:	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.
số ISSN:	2331-8422
Nguồn:	Engineering Database