## 2 Related Work  
ALSC is designed to extract from the provided context aspects for their sentiment polarity. Early sentiment classification methods were dominated by the classical text classification approach [23], which does not do a good job of mining the intrinsic connection of aspects to the context. For the past few years, as neural networks continue to evolve [24; 25], attention-based networks have been extensively used in ALSC. [PERSON] et al. [26] proposed a deep memristive network (MemNet) utilizing attention, which worked on predicting the sentiment polarity by capturing the relevance of contextual words with respect to the aspect. [PERSON] et al. [27] proposed the Interactive Attention Network (IAN) to construct an interactive representation between the target and context to better grasp the complex relationship between the objective aspects and the texts. [PERSON] et al. [28] proposed the Attention-to-Attention Network (AOA), which shows the relevance of opinion words to aspects by learning attention from aspects and text in both directions. [PERSON] et al. [29] trained attention scores through reinforcement learning and attention-based regularization, treating them as syntactic distances to generate aspect-specific discrete opinion trees. The BERT [30] pre-trained model based on Transformer [31] is used extensively in a variety of natural language processing (NLP) domains by relying on the attention mechanism alone. However, these methods may introduce noise while ignoring resources such as dependencies and semantic information, which can help bring the model to an understanding of what it means and the contextual relationships of the text more deeply, thus improving the accuracy and efficiency of NLP tasks. Syntactic structure-based methods can effectively analyze the structure of sentences and inter-word modification relations to obtain detailed syntax information. Early work relied on artificially defining some syntax rules. In recent years, GNNs for coding dependency parse trees have been widely used and exhibit good results. [PERSON] et al. [32] constructed GCN by fusing multiple pieces of external knowledge for SC. [PERSON] et al. [7] combined BiLSTM and GNNs to recognize the sentiment polarity of specific aspects of a sentence through dependency tree-enhanced embedding. [PERSON] et al. [33] integrated GCN into the sentence dependency tree to understand dependency relationships. Nevertheless, these dependency-based methods fail to realize the significance of the aspect words within sentences. To address this issue, [PERSON] et al. [10] proposed an R-GAT, where they encoded, reshaped, and pruned to obtain a novel aspect-oriented dependency parse tree for sentiment classification via GAT. [PERSON] et al. [16] aimed to minimize errors in dependency parsing by designing a module with extensive syntax knowledge. In SC tasks, the understanding of semantic information is as important as the dependencies of words [34]. [PERSON] et al. [35] utilized general linguistic knowledge to promote accuracy in classification. [PERSON] et al. [36] proposed a new goal-directed structured attention network for identifying several semantic fragments in a sentence, which are then fused with the extracted segments to classify them. [PERSON] et al. [37] augmented the LSTM using stratified attention of target and sentence attention and then incorporated the semantic information for sentiment classification in a loop encoder. The syntactic structure and the semantic information can jointly affect the prediction of sentiment polarity, and considering both syntax and semantics at the same time can help us understand sentences better. [PERSON] et al. [38] fused syntactic, semantic, and public information through a three-channel GCN, and optimized the fusion using a multi-head attention mechanism. [PERSON] et al. [17] fused syntactic and semantic features using a multilayer GAT by introducing constituent trees and aspect-aware attention mechanisms. [PERSON] et al.