## 4 Experiment
### Comparative Results and Analysis  
The results of comparison with all baseline models are shown in Table 5. We drew the following conclusions:  
1. On the three datasets of Restaurant, Laptop and Twitter, our model performance is better than the attention-based and syntax-based models, which shows that the RAG-TCGCN model performs better in encoding syntactic and semantic information through the adaptive fusion of syntactic and semantic information. 2. On the three datasets, the performance of the syntax-based model and the attention-based model is very different. The syntax-based model (ASGCN, CDT, BiGCN) is superior to the attention-based model (IAN, AOA, RAM). The main reason is that syntactic structure can more effectively capture the relationship between aspects and corresponding sentiment words, and extract more useful information. 3. Compared with attention-based models (MGAN, IAN), our model demonstrates significant improvement. The IAN model mainly obtains the initial feature representation through an LSTM pre-training model, and then obtains relatively rich semantic feature information through an attention mechanism. The effect of the model highly \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Restaurant**} & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Twitter**} \\ \cline{2-7}  & **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 \\ 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 \\ 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 \\ RAG-TCGCN & 84.09 & 77.02 & **78.80** & **75.04** & **76.66** & **75.41** \\ \hline \hline \end{tabular} The symbol -\(\cdot\) indicates this result is not available in their work. The best are in **bold**.  
\end{table} 
Table 5: Experimental results comparison on three publicly available datasets. depends on whether the attention mechanism can accurately establish the connection between aspects and context. However, due to the complexity of a sentence and the inadequacy of the attention mechanism in capturing long-distance dependent information, this introduces some irrelevant information and generates noise, resulting in a poor performance of the model. However, our model constructs the relationship between aspects and opinion words by using syntactic information. Therefore, the noise introduced by the attention mechanism is avoided. 4. Compared with syntax-based models (R-GAT, DGEDT), our model's performance demonstrates significant improvement. Syntax-based models mainly obtain local features through syntactic structures and establish word-word relationships, but they ignore the global information and the semantic information between words. However, when there are sentences with obscure grammatical structure or complex sentences, extracting features only by syntactic knowledge leads to poor results. 5. Compared with the DualGCN model, our RAG-TCGCN model shows an improvement of 0.32% and 0.74% on Laptop and Twitter accuracy, respectively, and 0.3% and 1.12% on F1. Although a DualGCN model extracts information features syntactically and semantically, due to the complexity of sentences, each sentence has a different sensitivity to syntax and semantics. Therefore, important information cannot be obtained by self-learning according to the characteristics of sentences.