  Aspect-based sentiment Analysis (ABSA) identifies and evaluates sentiments
toward specific aspects of entities within text, providing detailed insights
beyond overall sentiment. However, Attention mechanisms and neural network
models struggle with syntactic constraints, and the quadratic complexity of
attention mechanisms hinders their adoption for capturing long-range
dependencies between aspect and opinion words in ABSA. This complexity can lead
to the misinterpretation of irrelevant con-textual words, restricting their
effectiveness to short-range dependencies. Some studies have investigated
merging semantic and syntactic approaches but face challenges in effectively
integrating these methods. To address the above problems, we present
MambaForGCN, a novel approach to enhance short and long-range dependencies
between aspect and opinion words in ABSA. This innovative approach incorporates
syntax-based Graph Convolutional Network (SynGCN) and MambaFormer
(Mamba-Transformer) modules to encode input with dependency relations and
semantic information. The Multihead Attention (MHA) and Mamba blocks in the
MambaFormer module serve as channels to enhance the model with short and
long-range dependencies between aspect and opinion words. We also introduce the
Kolmogorov-Arnold Networks (KANs) gated fusion, an adaptively integrated
feature representation system combining SynGCN and MambaFormer representations.
Experimental results on three benchmark datasets demonstrate MambaForGCN's
effectiveness, outperforming state-of-the-art (SOTA) baseline models.
