## 4 Experiment
### Case Study  
To better understand the work of the SSEMGAT model, we selected two samples to review for visual case studies. In Table 6, we visualize the attention weights, predicted labels, aspect terms, and corresponding true labels for sentences.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Aspect** & **Attention Visualization** & **Prediction** & **Label** \\ \hline \multirow{4}{*}{AOA} & environment & Its environment is elegant but price is expensive & Negative & Positive \\ \cline{2-5}  & price & Its environment is elegant but price is expensive & Positive & Negative \\ \cline{2-5}  & room & The look of the room is novel & Positive & Positive \\ \hline \multirow{4}{*}{ASGCN} & environment & Its environment is elegant but price is expensive & Negative & Positive \\ \cline{2-5}  & price & Its environment is elegant but price is expensive & Negative & Negative \\ \cline{2-5}  & room & The look of the room is novel & Positive & Positive \\ \hline \multirow{4}{*}{BERT-BASE} & environment & Its environment is elegant but price is expensive & Positive & Negative \\ \cline{2-5}  & price & Its environment is elegant but price is expensive & Positive & Negative \\ \cline{2-5}  & room & The look of the room is novel & Positive & Positive \\ \hline \multirow{4}{*}{SSEMGAT} & environment & Its environment is elegant but price is expensive & Positive & Positive \\ \cline{2-5}  & price & Its environment is elegant but price is expensive & Negative & Negative \\ \cline{1-1} \cline{2-5}  & room & The look of the room is novel & Positive & Positive \\ \hline \hline \end{tabular}
\end{table}
Table 6: Visual analysis of attention in review sample.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Restaurant**} & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Twitter**} \\  & **Accuracy** & **Macro-F1** & **Accuracy** & **Macro-F1** & **Accuracy** & **Macro-F1** \\ \hline SSEMGAT & **86.42** & **79.70** & **80.06** & **76.78** & **76.81** & **76.10** \\ w/o dep & 85.69 & 78.69 & 79.15 & 75.97 & 74.26 & 73.56 \\ w/o con & 85.52 & 78.16 & 78.80 & 74.67 & 75.62 & 74.43 \\ w/o aaa & 86.05 & 79.66 & 79.75 & 76.00 & 76.22 & 75.70 \\ w/o mha & 85.25 & 78.02 & 78.31 & 75.73 & 75.31 & 75.22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of the ablation study.  
The first sample contains two aspect terms where the corresponding sentiment polarity is opposite, and the second sample contains only one aspect term.  
In the first example, the AOA model focuses on \"elegant\" and \"but\" at the same time, misjudges \"environment\" as negative sentiment polarity, while \"price\" focuses on \"elegant\" and \"expensive\" and allocates positive sentiment polarity. This shows that there is interference between different aspect terms. In the second example, with only one aspect term, the correct sentiment polarity was identified. The ASGCN model may misjudge sentiment by taking the relationship between \"but\" and \"environment\" as clues. The BERT model does not correctly align the sentiment information corresponding to \"price\". We speculate that the possible reason is that the corresponding sentiment words are randomly replaced with other irrelevant information when masking. The SSEMGAT model effectively combined syntactic structure and semantic correlation of the feature information and correctly predicted all aspects of terms related to sentiment tendency.