## 4 Experiments
### Results and Analysis  
Table 2 shows the experimental results of our proposed model and other comparison models. According to the data in the table, we can get the following conclusions. The performance of our proposed model is stronger than all comparison models on most datasets, and the improvement is particularly obvious when using pre-trained BERT as embedding (using pre-trained BERT as embedding has a huge improvement in the performance of the model). The experimental results prove the effectiveness of our model. Models based on graph convolutional networks and dependency trees are significantly better than models based on attention in capturing long-distance dependent information. This result reflects the superiority of graph convolutional network in ABSC task. Compared with the attention-based model, our model can capture long-distance dependence information through the attention-enhanced graph convolutional network and dependency tree, obtaining better feature representation; compared with the model based on the graph convolutional network, our model can effectively integrate semantic \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Twitter**} & \multicolumn{2}{c}{**Lap14**} & \multicolumn{2}{c}{**Rest14**} & \multicolumn{2}{c}{**Rest15**} & \multicolumn{2}{c}{**Rest16**} \\ \cline{3-11}  & & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline \multirow{6}{*}{Att} & ATAE-LSTM & - & - & 68.70 & - & 77.20 & - & - & - & - & - \\  & MenNet & 71.48 & 69.90 & 70.64 & 65.17 & 79.61 & 69.64 & 77.31 & **58.28** & 85.44 & 65.99 \\  & IAN & 72.50 & 70.81 & 72.05 & 67.38 & 79.26 & 70.09 & **78.54** & 52.65 & 84.74 & 55.21 \\  & AOA & 72.30 & 70.20 & 72.62 & 67.52 & 79.97 & 70.42 & 78.17 & 57.02 & **87.50** & **66.21** \\  & T-MGAN & 71.23 & 70.63 & 76.38 & 73.02 & 82.06 & 72.65 & - & - & - & - \\  & AEN & 72.83 & 69.81 & 73.51 & 69.04 & 80.98 & 72.14 & - & - & - & - \\  & AEN-BERT & 74.71 & 73.13 & 79.93 & 76.31 & 83.12 & 73.76 & - & - & - & - \\  & IMAN & **75.72** & **74.50** & **80.53** & **76.91** & **83.95** & **75.63** & - & - & - & - \\ \hline \multirow{6}{*}{Syn} & LSTM + SynATT & - & - & 72.57 & 69.13 & 80.45 & 71.26 & 80.28 & **65.46** & 83.39 & 66.83 \\  & CDT & **74.66** & **73.66** & **77.19** & **72.99** & **82.30** & **74.02** & - & - & 85.58 & 69.93 \\ \cline{1-1}  & ASGCN & 72.15 & 70.40 & 75.55 & 71.05 & 80.77 & 72.02 & 79.89 & 61.89 & **88.99** & 67.48 \\ \cline{1-1}  & BiGCN & 74.16 & 73.35 & 74.59 & 71.84 & 81.97 & 73.48 & **81.16** & 64.79 & 88.96 & **70.84** \\ \hline \multirow{2}{*}{Ours} & AEGCN & 73.86 & 71.59 & 75.91 & 71.88 & 81.43 & 73.66 & 80.85 & 63.96 & 88.76 & 68.73 \\ \cline{1-1}  & AEGCN-BERT & **75.99** & **75.01** & **80.37** & **76.68** & **84.46** & **76.33** & **83.92** & **67.08** & **89.61** & **70.71** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model comparison results of accuracy and macro-F1(%) on five datasets. The best results of different categories in each dataset are shown in bold. The best results in all models are bolded and underlined. “-” means not reported. information and syntactic information through the multi-head attention mechanism, and alleviate the impact of the limitations of the dependency tree. Compared with the AEN model, our model has a significant improvement effect. The AEN model models the context and aspect words separately, extracts semantic features through a multi-head attention mechanism, and interacts context information and aspect information to obtain feature representations. The effect of the model highly depends on whether the multi-head attention mechanism can accurately establish the connection between the aspect words and the context.