## 4 Experiment
### Case Study  
To better understand our model, we compared different models in several test examples. The experimental results are shown in Table 7. The Attention Visualization column in the table shows the different attention scores for each model, using darker to lighter colors depending on how high or low they scored. In the first example, \"Great food but terrible service\", the sentence contains two aspects, \"food\" and \"service\", as well as two sentiment words, \"great\" and \"terrible\". The attention-based IAN model does not capture the connection between the two well, resulting in inaccurate predictions. In the second and third examples, IAN models still fail to correctly establish the relationship between aspect words and sentiment words due to the complexity of the sentences. Although it is possible to directly establish a connection between an aspect word and sentiment words through syntactic structure, this connection does not exist in a sentence with no obvious syntactic structure. In the third example, the SynGCN model does not capture the feature representation of the keyword \"not\", resulting in incorrect prediction results, while the SemGCN, and Comom models can focus on the semantic correlation between words and capture this \"not\" feature representation, thereby obtaining correct prediction results. In the second example, when using the syntax or semantics, public information convolution modules incorrectly allocate the highest attention to \"wonderful\". Treating \"wonderful\" as a sentiment word for \"dinner\" eventually leads to errors in sentence judgment. However, our proposed RAG-TCGCN model adaptively reduces the attention to the irrelevant word \"wonderful\" and adaptively increases the score of \"dinner\".  
\begin{table}
\begin{tabular}{l l|l l|l l|l l|l l} \hline \hline
**Model** & **Aspect** & \multicolumn{6}{c}{**Attention Visualization**} & **Prediction** & **Label** \\ \hline \multirow{4}{*}{IAN} & food & **Great** & **food** & **but** & **herrible** & **service** & Neutral & Positive \\  & dinner & My & wife & and I & recently & visited & the bistro & for dinner & and \\  & & have & a & **wonderfull** & experience & & & \\  & Windows11 & Did & not & enjoy & the new & Windows11 & and & touchscreen functions & Neutral & Negative \\ \hline \multirow{4}{*}{SynGCN} & food & **Great** & **food** & **but** & **herrible** & **service** & & Positive & Positive \\  & dinner & My & wife & and I & recently & visited & the bistro & for dinner & and \\  & & have & a & **wonderfull** & experience & & & \\  & Windows11 & Did & not & enjoy & the new & Windows11 & and & touchscreen functions & Positive & Negative \\ \hline \multirow{4}{*}{SemGCN} & food & **Great** & **food** & **but** & **herrible** & **service** & & Positive & Positive \\  & dinner & My & wife & and I & recently & visited & the bistro & for dinner & and \\  & Windows11 & Did & not & enjoy & the new & Windows11 & and & touchscreen functions & Negative & Negative \\ \hline \multirow{4}{*}{Comom} & food & **Great** & **food** & **but** & **herrible** & **service** & & Positive & Positive \\  & dinner & My & wife & and I & recently & visited & the bistro & for dinner & and \\  & Windows11 & Did & not & enjoy & the new & Windows11 & and & touchscreen functions & Negative & Negative \\ \hline \multirow{4}{*}{RAG-TCGCN} & food & **Great** & **food** & **but** & **herrible** & **service** & & Positive & Positive \\  & dinner & My & wife & and I & recently & visited & the bistro & for dinner & and \\ \cline{1-1} \cline{2-7}  & **dinner** & **dinner** & **dinner** & **dinner** & **dinner** & **dinner** & & Neutral & Neutral \\ \cline{1-1}  & Windows11 & Did & not & enjoy & the new & Windows11 & and & touchscreen functions & Negative & Negative \\ \hline \hline \end{tabular}
\end{table}
Table 7: Visual analysis cases of IAN, SynGCN, SemGCN, Comom, and RAG-TCGCN.