融合句法与情感分析，提升商品评论特征提取准确率
A Fusion Approach of Dependency Syntax and Sentiment Polarity for Feature Label Extraction in Commodity Reviews
- 结合依存句法与情感极性分析，增强特征识别鲁棒性
- 准确率70%，召回率与F1值均达80%以上
- 适合电商评论挖掘、产品分析等场景使用
本研究分析了来自京东的13,218条商品评论，涵盖手机、电脑、化妆品和食品四类。提出一种融合依存句法分析与情感极性分析的新方法，用于商品评论中的特征标签提取。该方法有效解决了现有提取算法鲁棒性不足的问题，显著提升了提取精度。实验结果表明，该方法准确率达到0.7，召回率与F-score均稳定在0.8，验证了其有效性。但未来研究仍需关注对匹配词典的依赖性及特征标签提取范围有限等问题。
原文摘要 · Abstract (English)
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.
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