## 3 Methodology
### Syntax Attention Module (SA)  
In NLP, dependencies are syntactic relationships of words that allow one to gain a better comprehension of the structure and meaning of a sentence. Aspect terms refer to the entities in a sentence that are targeted to express sentiment or point of view. Our model incorporates an aspect terms-oriented dependency representation to catch the association between aspect terms and sentiment polarity. [PERSON] et al. [10] proposed a tree reconstruction algorithm, according to which we reshape the original dependency tree by setting the aspect  
Figure 2: The architecture of the model we propose is illustrated, including the syntactic attention module (SA) and the semantic enhancement module (SE). as the root node and pruning the tree to remove unnecessary relations. With regards to an input sentence, we apply the dependency parser to obtain its tree at first. Then, the aspects are designated as the root nodes. Next, the words that have direct dependencies with aspect terms are set as child nodes, preserving the original dependencies between them. Finally, other dependencies in the sentence that are unrelated to aspect terms are discarded to minimize the effect of the relationships of irrelevant nodes in model performance. For words that do not have direct dependencies, a dummy relationship n: con (n connections) is established between the aspect and each respective node, where n denotes the distance of two nodes from each other. If there are multiple aspects involved in the sentence, we will build a unique tree for every aspect. Figure 3 demonstrates the dependency parse tree after the reconstruction of a sentence containing one aspect word. Figure 4 demonstrates an example of a dependency tree after the reconstruction of a sentence containing two aspect words, constructing a unique tree for each aspect. In GAT, for each node, GAT calculates its attentional weights to its neighboring nodes. Then, these weights are used for weighted summation of the features of the neighboring nodes, and each node collects the feature information of its surrounding nodes, which is aggregated by an aggregation operation to renew the representation of the node. However, this process does not take into account the dependency relationship and may lose some important sentence information. [PERSON] et al. (2020) extended GAT with aspect-oriented dependencies, which inspired us to encode dependency labels using R-GAT. Specifically, GAT aggregates representations of adjacent nodes along the dependencies between them, and in our model, these weights are determined by the dependencies between nodes. This process captures the dependencies among nodes and enhances their representations. For sentence S, the reconstructed aspect-oriented dependency parse tree preserves important  
Figure 4: The sentence contains two aspects, “food” and “surrounding”, each of which has a unique dependency parse tree with the root node of the aspects. Figure 3: Dependency parse tree for sentences containing an aspect word with the aspect word “game” as the root node. dependencies and maps dependencies to vector representations. Ultimate dependency characterization is calculated as below. \[\alpha_{ij}=\sigma\big{(}ReLU\big{(}r_{ij}W_{m1}+b_{m1}\big{)}W_{m2}+b_{m2}\big{)}, \tag{2}\]  
\[\beta_{ij}=\frac{exp\big{(}\alpha_{ij}\big{)}}{\sum_{j=1}^{N_{i}}exp\big{(} \alpha_{ij}\big{)}}, \tag{3}\]  
\[H_{sa}=\Big{\|}\underset{m=1}{M}\sum\
olimits_{j\in N_{i}}\big{(}\beta_{ij}W_{ m}h_{j}\big{)}\,, \tag{4}\]  
In the above equation, \(r_{ij}\) is the relational embedding from node \(i\) to \(j\), and \(W_{m1}\) is the learned weight matrix that maps \(r_{ij}\) to the hidden layer of the GAT. \(W_{m2}\) is the learned weight matrix, \(b_{m1}\) and \(b_{m2}\) are bias vectors, \(h_{j}\) is the feature corresponding to the word \(j\), \(M\) is the number of relational heads in the relational GAT, and \(H_{sa}\) is the feature extracted by the syntactic attention module.