Summary of A Fusion Approach Of Dependency Syntax and Sentiment Polarity For Feature Label Extraction in Commodity Reviews, by Jianfei Xu
A Fusion Approach of Dependency Syntax and Sentiment Polarity for Feature Label Extraction in Commodity Reviews
by Jianfei Xu
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel method integrates dependency parsing and sentiment polarity analysis to extract features from product reviews. The study analyzed 13,218 reviews across four categories: mobile phones, computers, cosmetics, and food. The method enhances extraction accuracy, achieving an accuracy of 0.7, with recall and F-score stabilizing at 0.8. While the approach shows promise, future research is needed to address challenges such as dependence on matching dictionaries and limited scope of extracted feature tags. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers analyzed over 13,000 product reviews to improve how features are extracted from these reviews. They came up with a new way to do this using two techniques: looking at the relationships between words in sentences (dependency parsing) and determining if the sentence is positive or negative (sentiment polarity analysis). This new method did better than previous methods, correctly identifying about 70% of the features. However, there are still some challenges that need to be fixed before this method can be used in real-world applications. |
Keywords
» Artificial intelligence » Dependency parsing » Recall
