Abstract:

Aspect-Based Sentiment Analysis （ABSA） aims to infer the sentiment polarity towards specific aspects. The existing methods often adopt an equal-weight strategy when aggregating neighbor node information， failing to distinguish the differences in node importance， which leads to insufficient semantic-syntactic coordination. Graph ATtention network （GAT） relies on indirect syntactic associations to capture the relationships between aspect words and sentiment words， resulting in a lack of global features and low distinguishability in the sentiment representation space， thereby causing ambiguous sentiment space. To address the above issues， a graph convolutional network model integrating Mutual Information （MI） alignment and supervised contrastive learning， MIC-GCN （Mutual Information and Contrastive learning-based Graph Convolutional Network）， was proposed. First， text words were converted into vector representations through an embedding layer. Second， a dual-attention dynamic routing module was designed to combine the dynamic interaction of self-attention and aspect-aware attention， so as to generate an attention score matrix， and a dynamic masking matrix was constructed on the basis of syntactic distance， so as to optimize the utilization of structural information. Third， the MIC-GCN module was used to construct the original graph and pseudo-graph. After extracting local features through convolution， local and global features were aligned by MI maximization. Finally， the supervised contrastive learning was introduced to optimize the sentiment representation space and enhance the ability to distinguish sentiment polarity， so as to complete sentiment polarity judgment through pooling and classification layers. Experimental results demonstrate that on three public datasets — Twitter， Lap， and Rest， the proposed model has the accuracy 0.10， 1.71， 0.30 percentage points higher than the best baseline models SSEGCN （Syntactic and Semantic Enhanced Graph Convolutional Network）， T-GCN（Type-aware Graph Convolutional Network）， and DGEDT （Dependency Graph Enhanced Dual-Transformer network）， respectively. It can be seen that the proposed model enhances semantic-syntactic coordination effectively， improves the efficiency of capturing global features and the distinguishability of sentiment polarity， demonstrating excellent performance in ABSA tasks.

Key words: Aspect-Based Sentiment Analysis (ABSA), Mutual Information (MI) maximization, dual-attention dynamic routing, contrastive learning, Graph Convolutional Network (GCN)

摘要：

方面级情感分析（ABSA）旨在针对特定方面推断情感极性。现有的方法在聚合邻居节点信息时多采用等权重策略，而未能充分区分节点重要性的差异，导致语义-句法的协同不足；图注意力网络（GAT）依赖间接句法关联捕捉方面词与情感词的关系，造成全局特征缺失和情感表示空间区分度低，进而引发情感空间模糊问题。针对上述问题，提出一种融合互信息（MI）对齐与监督对比学习的图卷积网络模型MIC-GCN （Mutual Information and Contrastive learning-based Graph Convolutional Network）。首先，通过嵌入层将文本词汇转换为向量表示；其次，设计双注意力动态路由模块，以结合自注意力与方面感知注意力的动态交互生成注意力分数矩阵，并基于句法距离构建动态掩码矩阵优化结构信息的利用；再次，利用MIC-GCN模块构建原图及伪图，而经卷积提取局部特征后，借助MI最大化实现局部与全局的特征对齐；最后，引入监督对比学习来优化情感表示空间，并增强情感极性区分能力，从而通过池化与分类层完成情感极性判断。实验结果表明，在Twitter、Lap和Rest 3个公开数据集上，MIC-GCN模型的准确率比最优基线模型SSEGCN （Syntactic and Semantic Enhanced Graph Convolutional Network）、T-GCN （Type-aware Graph Convolutional Network）和DGEDT （Dependency Graph Enhanced Dual-Transformer network）分别提高了0.10、1.71和0.30个百分点。可见，所提模型能有效增强语义-句法协同，提升全局特征捕捉效能与情感极性区分度，在ABSA任务中表现优异。

关键词: 方面级情感分析, 互信息最大化, 双注意力动态路由, 对比学习, 图卷积网络