计算机科学
对偶(语法数字)
图形
频道(广播)
人工智能
理论计算机科学
计算机网络
艺术
文学类
作者
Wanneng Shu,Cao Zhai,Ke Yu
标识
DOI:10.1109/tcss.2024.3418472
摘要
It is crucial to mine latent sentiments and opinions from comments on social media to comprehensively understand people's preferences and experiences. Aspect-based sentiment analysis aims to identify the sentiment polarities for specific aspects of a sentence. Mainstream methods generally struggle to process sentences with complex syntactic structures and multiple aspects of different sentiment polarities. To overcome this challenge, this article proposes a dual-channel and aspect-aware graph convolutional network (DAGCN) model, which fully utilizes the syntactic and semantic information to accurately capture the sentiment features for specific aspects. Specifically, a syntactic graph convolutional network module is designed to effectively learn syntactic information by constructing an aspect-oriented dependency tree and employing a gated aggregator. To reduce the semantic interference from multiple aspects, a semantic graph convolutional network module is developed to capture both local and global semantic correlations corresponding to specific aspects. Furthermore, a BiAffine module is integrated to facilitate the interaction between the syntactic and semantic information. Experiments on four benchmark datasets illustrate that our proposed DAGCN significantly outperforms state-of-the-art baselines.
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