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The studies of ABSA mainly focus on the attention-based approaches and the graph neural network approaches based on dependency trees. However, the attention-based methods usually face difficulties in capturing long-distance syntactic dependencies. Additionally, existing approaches using graph neural networks have not made sufficient exploit the syntactic dependencies among aspects and opinions. In this paper, we propose a novel Syntactic Dependency Graph Convolutional Network (SD-GCN) model for ABSA. We employ the Biaffine Attention to model the sentence syntactic dependencies and build syntactic dependency graphs from aspects and emotional words. This allows our SD-GCN to learn both the semantic relationships of aspects and the overall semantic meaning. According to these graphs, the long-distance syntactic dependency relationships are captured by GCNs, which facilitates SD-GCN to capture the syntactic dependencies between aspects and viewpoints more comprehensively, and consequently yields enhanced aspect features. We conduct extensive experiments on four aspect-level sentiment datasets. The experimental results show that our SD-GCN outperforms other methodologies. Moreover, ablation experiments and visualization of attention further substantiate the effectiveness of SD-GCN.<\/jats:p>","DOI":"10.1007\/s44196-024-00419-6","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T09:04:51Z","timestamp":1708506291000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Graph Convolutional Network with Syntactic Dependency for Aspect-Based Sentiment Analysis"],"prefix":"10.1007","volume":"17","author":[{"given":"Fan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9183-796X","authenticated-orcid":false,"given":"Wenbin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujie","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,21]]},"reference":[{"issue":"2","key":"419_CR1","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1109\/TAFFC.2020.2970399","volume":"13","author":"A Nazir","year":"2022","unstructured":"Nazir, A., Rao, Y., Wu, L., Sun, L.: Issues and challenges of aspect-based sentiment analysis: A comprehensive survey. 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