## 4 Experiment
### Experimental Results and Analysis  
Our proposed model is compared with three types of baseline model: the attention-based method, the syntax-based model, and the pre-training-based model. The attention-based model includes IAN, AOA, RAM, MGAN, and TNet. The syntax-based model includes ASGCN, CDT, BiGCN, kumaGCN, R-GAT, DGEDT, DualGCN, SSEGCN, and RAG-TCGCN. The pre-training-based model includes BERT, BERT-PT, AEN-BERT, and BERT4 GCN. The main experimental results are reported in Table 4.  
Based on the experimental results in Table 4, we offer the following analysis:  
1. Our proposed model achieves better results compared with other last and baseline models. We believe that the primary reason is that the designed SSEMGAT model captures syntactic and semantic feature information more efficiently than other models, which also proves the effectiveness of our work.
2. The model that considers syntactic structure and semantic information at the same time is better than the model that considers only semantic information or syntactic structure, which shows that syntax and semantics do not exist in isolation, and learning the interaction information between them is also very necessary.
3. Compared with attention-based models, our proposed model has obvious advantages. From the analysis of this phenomenon, we believe that the attention mechanism is easily affected by the noise factor in the sentence when facing complex sentences and obscure structures and cannot accurately align the contextual and sentiment information. This reduces the performance of the model.
4. Compared with syntax-based models, our model also has good results. This may be because we made up for the inherent defects in dependency trees in sentence parsing, thus enhancing their ability to capture aspect words and their corresponding opinion words and improving the model's ability to resist interference to noise elements introduced in the dependency tree.
5. Compared with the model based on pre-training, our model also has better performance. BERT has strong representational learning ability and a rich hierarchical structure, while the dependency tree also has an obvious hierarchical structure, which  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & \multicolumn{2}{c}{**Restaurant**} & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Twitter**} \\  & **Accuracy** & **Macro-F1** & **Accuracy** & **Macro-F1** & **Accuracy** & **Macro-F1** \\ \hline IAN & 78.60 & - & 72.10 & - & - & - \\ AOA & 80.53 & 69.84 & 72.88 & 67.48 & 72.25 & 69.96 \\ RAM & 80.23 & 70.80 & 74.49 & 71.35 & 69.36 & 67.30 \\ MGAN & 81.25 & 71.94 & 75.39 & 72.47 & 72.54 & 70.81 \\ TNet & 80.69 & 71.27 & 76.54 & 71.75 & 74.90 & 73.60 \\ ASGCN-DG & 80.77 & 72.02 & 75.55 & 71.05 & 72.15 & 70.40 \\ ASGCN-DT & 80.86 & 72.19 & 74.14 & 69.24 & 71.53 & 69.68 \\ CDT & 82.30 & 74.02 & 77.19 & 72.99 & 74.66 & 73.66 \\ BiGCN & 81.97 & 73.48 & 74.59 & 71.84 & 74.16 & 73.35 \\ kumaGCN & 81.43 & 73.64 & 76.12 & 72.42 & 72.45 & 70.77 \\ R-GAT & 83.30 & 76.08 & 77.42 & 73.76 & 75.57 & 73.82 \\ DGEDT & 83.90 & 75.10 & 76.80 & 72.30 & 74.80 & 73.40 \\ DualGCN & 84.27 & 78.08 & 78.48 & 74.74 & 75.92 & 74.29 \\ SSEGCN & 84.72 & 77.51 & 79.43 & 76.49 & 76.51 & 75.32 \\ RAG-TCGCN & 84.09 & 77.02 & 78.80 & 75.04 & 76.66 & 75.41 \\ BERT-PT & 84.95 & 76.96 & 78.07 & 75.08 & - & - \\ BERT-SPC & 84.46 & 76.98 & 78.99 & 75.03 & 73.55 & 72.14 \\ AEN-BERT & 83.12 & 73.76 & 79.93 & 76.31 & 74.71 & 73.13 \\ BERT4 GCN & 84.75 & 77.11 & 77.49 & 73.01 & 74.73 & 73.76 \\ Ours & **86.42** & **79.70** & **80.06** & **76.78** & **76.81** & **76.10** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sentiment classification results. We directly introduce the result data from the original author’s paper as the data for comparison, where “-” means that this part of the work is not revealed, and the best experimental results are shown in bold.  
may be related in some way. When we use the enhanced feature extractor for extraction, we can better capture the correlation between syntax and semantics.