Attempting to identify and determine the sentiment polarity of one or more aspects (i.e., aspect words) in a sentence, Aspect-Level Sentiment Classification (abbreviated as ALSC) is a fine-grained sentiment classification task. Graph convolutional networks on dependency trees are now widely being used in related research to improve the accuracy of ALSC. The key to determining the polarity of aspectual emotions is to find the opinion, i.e., the opinion word, that is, most relevant to the aspectual emotion. However, in the dependency tree, a significant portion of aspect words and opinion words are not directly connected. And long-distance connections can lead to the model not paying enough attention to opinion words and losing information. In order to address this issue, by examining dependency syntactic structure and syntactic knowledge, we propose Aspect-opinion Sentence pattern Connection (ASC) to strengthen sentiment dependency graphs. We then develop the ASC-GCN to efficiently use the strengthened dependencies. Experimental results on four public benchmark datasets indicate that our approach achieves excellent performance on a lightweight model.

Aspect-Level Sentiment Classification（简称ALSC）试图识别和确定句子中一个或多个方面（即方面词）的情感极性，是一种细粒度的情感分类任务。依赖树上的图卷积网络现在被广泛应用于相关研究中，以提高 ALSC 的准确性。确定方面情感极性的关键是找到与方面情感最相关的观点，即观点词。然而，在依存树中，很大一部分方面词和观点词并不直接连接。而长距离连接可能会导致模型对意见词关注不够而导致信息丢失。为了解决这个问题，通过检查依存句法结构和句法知识，我们提出了方面意见句子模式连接（ASC）来强化情感依存图。然后我们开发 ASC-GCN 以有效地使用增强的依赖关系。四个公共基准数据集的实验结果表明，我们的方法在轻量级模型上实现了出色的性能。