# Graph Convolutional Networks with Bidirectional Attention for Aspect-Based Sentiment Classification
## 4 Experiments
### Results and Analysis  
#### 4.4.1 Effectiveness of ATGCN  
In this section we report our experimental results and analyze them extensively. In order to evaluate the performance of ATGCN, we use Accuracy and Macro-F1 metrics, as shown in Table 2, our experiments almost always outperform all the comparison models on the three datasets. In order to evaluate the performance of ATGCN, we use Accuracy and Macro-F1 metrics, as shown in Table 2, our experiment consistently outperforms all comparison models on the three datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Twitter**} & \multicolumn{2}{c}{**Lap14**} & \multicolumn{2}{c}{**Rest14**} \\ \cline{2-7}  & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline
**AddRNN** & 66.30 & 65.90 & - & - & 60.42 & 45.73 \\
**PhraseRNN** & - & - & - & - & 66.20 & 59.32 \\
**LSTM+SynATT+TarRep** & 70.46 & 67.55 & 70.87 & 66.53 & 79.33 & 69.25 \\
**TD-LSTM** & 70.80 & 69.00 & 68.13 & - & 75.63 & - \\
**ATAE-LSTM** & - & - & 68.70 & - & 77.20 & - \\
**MemNet** & 71.48 & 69.90 & 70.64 & 65.17 & 79.61 & 69.64 \\
**IAN** & 72.50 & 70.81 & 72.05 & 67.38 & 79.26 & 70.09 \\
**AEN** & 72.83 & 69.81 & 73.51 & 69.04 & 80.98 & 72.14 \\
**BERT-pair-QA-M** & **74.28** & 72.38 & 78.21 & 73.56 & 81.96 & 73.29 \\
**ASGCN** & 72.15 & 70.40 & 75.55 & 71.05 & 80.77 & 72.02 \\
**ATGCN(Glove)** & 73.02 & 70.98 & 75.92 & 71.70 & 81.85 & 73.31 \\
**ATGCN(BERT)** & 74.26 & **73.81** & **80.06** & **75.61** & **82.79** & **75.10** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall performance of accuracy and F1 on three datasets. We can know intuitively that Table 2 indicates the comparison of ATGCN with other methods. The proposed ATGCN model achieved a significant improvement over the Glove based model in all datasets, such as TD-LSTM, MemNet and IAN. In addition, the performance of ATGCN (BERT) is better than the BERT-based models, such as Bert-pair-QA-M, indicating that ATGCN is effective even if the pre-trained BERT [42] model is used to aspect-based sentiment classification. Specifically, our model performs better than other model, and in most cases our model significantly outperformed the strongest baseline model. According to the experimental results, in terms of accuracy and macro F1, ATGCN performs better than the previously proposed syntactic-based models (such as AdaRNN, LSTM+SynATT+TarRep, and PhraseRNN), which proves that our model can capture syntactic information more effectively. In the model based on neural network, we can observe that the performance of TD-LSTM model is poor, because it considers the role of aspect information roughly, and it does not make full use of the correlation information between the aspect word and its corresponding context, so that the overall effect is poor. The performance of ATAE-LSTM is superior to TD-LSTM. However, its performance is poor because LSTM is inefficient in capturing message. It does not make a comprehensive two-way analysis of the influence between aspect and context, but it only considers the use of attention mechanism to get important parts of sentences. Our approach not only enables multiple interactions between contexts and aspects, but also uses MHA to analyze the impact of words simultaneously. One of the advantages of MemNet is the introduction of location embedding, which performs marginally better than the two models above, because the attention mechanism does not have the ability to model location information. In general, we believe that adjacent words have a significant influence on aspect terms. Therefore, the introduction of position coding can improve the classification effect. However, the hidden semantics of embedding were not modeled, and the outcome of the last focus was substantially a linear combination of word embedding, resulting in poor performance. IAN achieved better performance on the three datasets because context and aspect interactions were accomplished. IAN uses two kinds of attention to perform ABSC tasks.