## 4 Experiment
### Baseline Methods  
In this paper, the following mainstream models were selected for comparison and run in the same experimental environment. The results are shown in Table 5.  
1. IAN [12]: Simultaneous modeling of aspect words and context information to make aspect words and context information interactively fuse with attention.
2. AOA [45]: Simultaneous modeling of aspects and text using long short-term memory neural networks to focus on what is important in sentences.
3. RAM [34]: Mem-Net is enhanced with deep Bi-LSTM and position weighting. The gated recurrent unit network is nonlinearly combined with multiple attention results using a recurrent network to obtain the final feature representation.
4. MGAN [46]: Fine-grained attention is proposed to solve the loss caused by coarse-grained attention, and then combined with coarse-grained attention to predict the sentiment polarity of sentences.
5. ASGCN [15]: GCNs on syntactic dependency trees are built and generate aspect-oriented sentence representations by applying masking and an attention mechanism.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Settings** & **Value** \\ \hline embed\_dim & 300 \\ batch\_size & 16 \\ rnn\_hidden & 50 \\ input\_dropout & 0.7 \\ gcn\_dropout & 0.1 \\ num\_epoch & 50 \\ learning\_rate & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Division** & **Positive** & **Negative** & **Neutral** \\ \hline \multirow{3}{*}{Rest14} & Training & 2164 & 807 & 637 \\  & Testing & 728 & 196 & 196 \\ \multirow{3}{*}{\
\begin{tabular}{c} Lap14 \\ \end{tabular} } & Training & 994 & 851 & 455 \\  & Testing & 341 & 128 & 167 \\ \multirow{3}{*}{
\begin{tabular}{c} Twitter \\ \end{tabular} } & Training & 1507 & 1528 & 3016 \\  & Testing & 173 & 169 & 336 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of datasets.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**System** & Windows 10 \\
**CPU** & Intel(R) Core(TM) i7-10510u \\
**GPU** & NVIDIA GeForce MX250 \\
**Language** & Python 3.8 \\
**Tool** & Pycharm 2021 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental environment.  
Finally, two variants of ASGCN are proposed, namely, ASGCN-DG based on an undirected dependency graph and ASGCN-DT based on a directed dependency tree. (6) CDT [16]: BiLSTM is used to obtain the feature representation of the sentence and further enhance the embedding by a direct convolution operation on the dependency tree. (7) BiGCN [47]: A conceptual hierarchy is built on the syntactic and lexical graphs to distinguish various types of dependencies or lexical word pairs, and a two-layer interactive graph convolutional network is designed to take full advantage of these two graphs. (8) R-GAT [37]: An aspect correlation tree rooted in aspect terms by reshaping the dependency parse tree is constructed, which uses relational graph attention network coding. (9) DGEDT [6]: A dual-transformer structure is designed to enable interaction enhancement between planar representations learned from the transformer and graph-based representations. (10) DualGCN [39]: The dual-graph convolutional network considers both the complementarity of the syntactic structure and semantic relevance.  
The comparison models selected in this paper can be divided into two categories: attention mechanism and graph neural network. IAN, AOA, RAM, and MGAN mainly use an attention mechanism to complete classification tasks. ASGCN, CDT, BiGCN, R-GAT, DGEDT, and DualGCN all use dependency trees to obtain syntactic information and utilize graph neural networks for classification tasks.