<p>Aspect-Based Sentiment Analysis (ABSA) focuses on understanding fine-grained sentiment information by analyzing the sentiment polarity corresponding to particular aspects in sentences. At present, graph neural networks are widely utilized to model the explicit relationships between aspects and opinions derived from the syntactic structures of dependency trees. However, these methods struggle to handle sentences with complex structures and multiple aspect–sentiment pairs. To solve this problem, we propose a Bilateral Synergistic Aggregation Network (BSAN) that integrates semantic and syntactic information to capture sentiment features that are specific to particular aspects. Specifically, within the Syntactic Distillation Module (SDM), we employ a Syntax View Graph Convolution (SynVGC) layer to encode the dependency-tree graph and extract syntactic features, while a Transformer layer is incorporated to capture sequential dependencies and refine the representations of aspect terms. Furthermore, the Semantic Optimization Module (SOM) utilizes Abstract Meaning Representation (AMR) as structured input and integrates attention mechanisms with graph convolutional networks to effectively model the semantic relations represented in the AMR. In addition, the Graph Cognitive Fusion Module (GCFM) is designed to facilitate the integration and interaction of syntactic and semantic representations. Finally, extensive experiments on four publicly available benchmark datasets demonstrate that our proposed BSAN model achieves competitive performance.</p>