Abstract: Currently,the over-smoothing problem arises from deep convolution in syntactic dependency tree-based graph convolutional networks.This problem prevents the convolutional graph network from extracting the global node information of the syntactic dependency tree.Although the sequential model can extract information about the context of the sentence,the timing-dependent nature of the sequential model leads to the inability of the graph convolutional network to effectively distinguish the contribution of context features to aspect terms.This paper proposes a novel graph convolutional network model based on syntactic distance and aspect focus attention mechanisms to address the above problems.First,the model learns the contextual information of sentences and aspect terms separately using a bidirectional long short-term memory network and uses a convolutional graph network to learn the syntactic dependency information of sentences.Secondly,this model calculates the syntactic dependency distance among all nodes based on the syntactic dependency tree,sets a threshold to weaken the weight share of long-distance features,and improves the ability of the graph convolution model to distinguish context features.Finally,this paper also designs attention mechanisms with residual connectivity to automatically guide the aspect terms to focus on the critical information in the sentence.Experimental results demonstrate that the model exhibits better analytical performance on several publicly available datasets compared to the baseline approach,with sentiment classification accuracy as high as 75.94% and 78.59% on the Twitter and Laptop datasets,demonstrating the effectiveness of the proposed approach.

Key words: Graph convolutional networks, Syntactic dependency tree, Syntactic dependency distances, Attention mechanisms, Aspect-level sentiment analysis