Aspect-based Sentiment Analysis (ABSA), also known as fine-grained sentiment analysis, aims to predict the sentiment polarity of specific aspect words in the sentence. Some studies have explored the semantic correlation between words in sentences through attention-based methods. Other studies have learned syntactic knowledge by using graph convolution networks to introduce dependency relations. These methods have achieved satisfactory results in the ABSA tasks. However, due to the complexity of language, effectively capturing semantic and syntactic knowledge remains a challenging research question. Therefore, we propose an Adaptive Dual Graph Convolution Fusion Network (AD-GCFN) for aspect-based sentiment analysis. This model uses two graph convolution networks: one for the semantic layer to learn semantic correlations by an attention mechanism, and the other for the syntactic layer to learn syntactic structure by dependency parsing. To reduce the noise caused by the attention mechanism, we designed a module that dynamically updates the graph structure information for adaptively aggregating node information. To effectively fuse semantic and syntactic information, we propose a cross-fusion module that uses the double random similarity matrix to obtain the syntactic features in the semantic space and the semantic features in the syntactic space, respectively. Additionally, we employ two regularizers to further improve the ability to capture semantic correlations. The orthogonal regularizer encourages the semantic layer to learn word semantics without overlap, while the differential regularizer encourages the semantic and syntactic layers to learn different parts. Finally, the experimental results on three benchmark datasets show that the AD-GCFN model is superior to the contrast models in terms of accuracy and macro-F1.

基于方面的情感分析（ABSA），也称为细粒度情感分析，旨在预测句子中特定方面词的情感极性。一些研究通过基于注意力的方法探索句子中单词之间的语义相关性。其他研究通过使用图卷积网络引入依赖关系来学习句法知识。这些方法在ABSA任务中取得了令人满意的结果。然而，由于语言的复杂性，有效捕获语义和句法知识仍然是一个具有挑战性的研究问题。因此，我们提出了一种自适应双图卷积融合网络（AD-GCFN），用于基于方面的情感分析。该模型使用两个图卷积网络：一个用于语义层，通过注意力机制学习语义相关性，另一个用于句法层，通过依存解析学习句法结构。为了减少注意力机制引起的噪声，我们设计了一个动态更新图结构信息的模块，以自适应聚合节点信息。为了有效地融合语义和句法信息，我们提出了一种交叉融合模块，该模块使用双随机相似度矩阵分别获取语义空间中的句法特征和句法空间中的语义特征。此外，我们采用两个正则化器来进一步提高捕获语义相关性的能力。正交正则化器鼓励语义层学习没有重叠的单词语义，而差分正则化器鼓励语义层和句法层学习不同的部分。最后，在三个基准数据集上的实验结果表明，AD-GCFN 模型在精度和宏观 F1 方面优于对比模型。