A Fusion Approach of Dependency Syntax and Sentiment Polarity for Feature Label Extraction in Commodity Reviews
This addresses the problem of low robustness in feature extraction algorithms for e-commerce platforms, though it is incremental with limitations like dictionary dependence.
The paper tackles feature label extraction from product reviews by integrating dependency parsing with sentiment polarity analysis, achieving an accuracy of 0.7 and recall/F-score of 0.8 on a dataset of 13,218 reviews.
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.