Syntax Dependency and Semantic Enhancement for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis is a fine-grained sentiment analysis task. In this domain, graph neural models based on dependency trees are widely used. However, effectively utilizing the semantic and syntactic structural information of dependency trees remains a challenging research problem. To address this issue, this paper proposes a Syntax Dependency and Semantics Enhancement (SDSE) model. This model aims to enhance the understanding of specific aspect-related syntactic dependency relations and sentence semantics. Specifically, the SDSE model integrates self-attention mechanisms and aspect-aware attention mechanisms to obtain the attention score matrix of the sentence. Then, leveraging graph convolutional networks on the attention score matrix, the model extracts semantic feature information of the sentence. This approach not only learns semantic associations related to aspects but also captures the overall semantic information of the sentence. Moreover, to mitigate dependency parsing errors, the SDSE model introduces aspect word merging. This involves parsing the sentence after merging aspect words to obtain the merged syntactic dependency graph, thereby enhancing the model’s focus on opinion entities. Finally, the dependency graph and processed sentence encodings are fed into graph convolutional networks for training. Experimental results demonstrate that our proposed SDSE model outperforms the current state-of-the-art methods on benchmark datasets.