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.

翻译：本研究分析了京东平台的13,218条商品评论，涵盖手机、电脑、化妆品和食品四大品类。通过融合依存句法分析与情感极性分析，提出了一种新颖的特征标签提取方法。该方法解决了现有提取算法鲁棒性不足的问题，显著提升了提取准确率。实验结果表明，该方法准确率达到0.7，召回率与F值均稳定在0.8，验证了其有效性。然而，对匹配词典的依赖性以及提取特征标签范围的局限性等问题，仍需在后续研究中进一步探索。