## 4 Experiment
### Baseline Methods  
We selected some mainstream baseline and lasted models to compare with the proposed models.  
1. IAN [8]: The aspect words and contextual representations generated by LSTM are used to learn interactively through attention.
2. AOA [10]: The aspect words and context representations generated by LSTM are modeled by attention-over-attention neural networks to capture the interaction between aspect and context.
3. RAM [9]: This proposes a recurrent attention network on memory to capture sentiment features between long distances.
4. MGAN [11]: The alignment matrix is used to complete the coarse-grained interaction between the aspect word and the context, and the aspect alignment loss function is designed to complete the fine-grained interaction at the word level.
5. TNet [32]: Use CNN to extract significant features from the transformed word representations from the bidirectional RNN layer.
6. ASGCN [12]: The dependency tree is used to extract syntactic information and perform graph convolution operations on the dependency tree to learn the representation of nodes.
7. CDT [13]: The feature representation of a sentence is learned by using bidirectional LSTM, and the embedded representation is enhanced by graph convolutional networks.
8. BiGCN [14]: The hierarchical syntactic graph and lexical graph are constructed by convolution on GNN embedding and BiLSTM embedding, respectively, and a bi-level interactive network is designed to learn information interaction.
9. kumaGCN [15]: It combines information from the latent graph and the dependency graph through a gated attention mechanism.
10. R-GAT [16]: The dependency tree is rooted to the target aspect by reconstructing, and pruning is performed to preserve the edges that are directly dependent on the aspect term.
11. DGEDT [15]: Considering the dependency tree as a special form of transformer, representations from the dependency tree and transformer are learned in an iterative interaction manner.
12. DualGCN [26]: Syntactic graph and semantic graph are constructed at the same time, and a double affine mechanism is used to complete the information exchange between syntactic and semantic, and finally, all the information is fused for classification.
13. SSEGCN [2]: The attention matrix constructed by the attention mechanism and syntactic mask matrix are combined to accomplish the interaction of syntactic structure and semantic information.
14. RAG-TCGCN [33]: Multiple attention is used to combine syntactic and semantic features and their related features with word-level features parsed using residual attention gates.
15. BERT [21]: MLM is used for pre-training bidirectional transformers to generate deep language representation, and good results can be achieved only with fine-tuning in downstream tasks.
16. BERT-PT [22]: The pre-training language model is trained through a large number of general domain data and a small amount of downstream data. It provides a solution for small sample data research.
17. AEN-BERT [23]: This uses an attention encoding network to interact aspect words with context and designs a processing form based on BERT word embedding.
18. BERT4 GCN [25]: Based on BERT's rich hierarchical structure information, the feature information in the middle layer is fused with the knowledge of the dependency tree, the enhanced dependency graph is constructed, and the convolution operation is performed in it.