## 4 Experiments
### Ablation Experiment  
In order to further validate the effectiveness of each component in our model MHAKE-GCN, we conducted ablation experiments, and the results are presented in Table 4. The \"w/o\" in the table denotes \"without\".  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Restaurant14 (\%)**} & \multicolumn{3}{c}{**Laptop14 (\%)**} & \multicolumn{3}{c}{**Restaurant15 (\%)**} & \multicolumn{3}{c}{**Restaurant16 (\%)**} \\ \cline{3-8}  & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** \\ \hline \multirow{3}{*}{Syn. + Att} & SVM & 80.16 & - & 70.49 & - & - & - & - \\  & GCAE & 77.28 & - & 69.14 & - & - & - & - \\  & IAN & 79.26 & 70.09 & 72.05 & 67.38 & 78.54 & 52.65 & 84.74 & 55.21 \\  & AOA & 79.97 & 70.42 & 72.62 & 67.52 & 78.17 & 57.02 & 87.50 & 66.21 \\ \hline \multirow{3}{*}{Graph} & ASGCN-DT & 80.86 & 72.19 & 74.14 & 69.24 & 79.34 & 60.78 & 88.69 & 66.64 \\  & ASGCN-DG & 80.77 & 72.02 & 75.55 & 71.05 & 79.89 & 61.89 & 88.99 & 67.48 \\  & MAGCN & 81.25 & 71.94 & 75.39 & 72.47 & - & - & - \\  & R-GAT & 83.39 & **77.33** & 73.74 & 75.56 & 80.30 & 64.17 & 88.92 & **70.89** \\ \hline Ours & MHAKE-GCN & **83.96** & 74.93 & **78.46** & **76.54** & **82.10** & **65.90** & **89.36** & 69.32 \\ \hline \multirow{3}{*}{BERT} & AEN-BERT & 83.12 & 73.76 & 79.93 & 76.31 & - & - & - \\  & BERT-SPC & 84.46 & 76.98 & 78.99 & 75.03 & - & - & - \\ \cline{1-1}  & TD-BERT & 85.10 & 78.35 & 78.87 & 74.38 & - & - & - \\ \cline{1-1}  & R-GAT-BERT & 86.60 & 81.35 & 78.21 & 74.07 & 83.22 & 69.79 & 89.71 & 76.62 \\ \hline Ours & MHAKE-GCN-BERT & **87.65** & **82.55** & **81.30** & **79.19** & **84.00** & **71.00** & **91.90** & **79.00** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results on four datasets. The best results are shown in bold.  
The results of the ablation experiments indicate that all components in our model are important, and their absence leads to decreased performance. The MHSA component extracts rich semantic information and encodes syntactic information. Ablating MHSA resulted in slight decreases in metrics on all four datasets. Of these, the F1 metrics for the Laptop14 and Restaurant15 datasets decreased a bit more, 1.10% and 1.62% respectively, indicating that syntactic information is useful for ABSA. The MHIA component aggregates features and learns interactive associations between semantic and syntactic information. Ablating MHIA resulted in significant decreases in metrics on all four datasets. The Laptop14 and Restaurant15 datasets showed particularly severe performance degradation, with the accuracy and F1 metrics dropping by 4.25% and 4.74%, respectively, for the Laptop14 dataset, and the F1 metric dropping by 4.04% for the Restaurant15 dataset, indicating that the interaction between semantic and syntactic information is crucial for ABSA. Finally, ablation of the affective knowledge component resulted in significant performance reduction on all four datasets. The F1 metric decreased by 3.57% for the Restaurant14 dataset, 4.91% for the Laptop14 dataset, and 5.03% for the Restaurant15 dataset, demonstrating that incorporating external affective knowledge can significantly improve ABSA performance.