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

TL;DR

This paper introduces a new method combining dependency syntax and sentiment analysis to improve feature label extraction accuracy in commodity reviews, addressing robustness issues of previous algorithms.

Contribution

It presents a novel fusion approach that enhances feature extraction accuracy in product reviews by integrating dependency parsing with sentiment polarity analysis.

Findings

Achieved 0.7 accuracy in feature label extraction

Recall and F-score stabilized at 0.8

Demonstrated effectiveness over existing methods

Abstract

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.