# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 6 Conclusions and Future Work  
In this study, we investigated the performance differences of a wide range of 35 different deep learning methods in ALSC through a statistical comparison framework [9] utilizing eight ASBA datasets. The studied deep learning methods include RNNs, CNNs, \begin{table}
\begin{tabular}{c c} \hline \hline
**ASGCN vs. GAT-BERT** & \\ \hline
**Evaluation Criteria** & \(p\)**-Value** \\ \hline Macro F1 score & 0.082 (-) \\ \hline Accuracy & 0.32 (-) \\ \hline Time & 0.0015 (\(\uparrow\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Wilcoxon Test results for pair-wise comparison of ASGCN and GAT-BERT. Figure 9: Pareto approach for selecting deep learning methods. Memory Networks, and Hybrid networks. The methods utilizing BERT pre-trained models, known as BERT-based methods are also evaluated in this study. Although the average numerical Accuracy and Macro F1 scores of BERT-based methods are higher than the non-BERT-based methods, no statistically significant differences could be observed between top-ranking GAT-BERT method and several non-BERT methods based on post-hoc tests. However, the experimental results with respect to Time reveal that BERT-based methods require a lot of time for training. GAT-BERT method is second-worst performer in terms of Time. Thus, the results of post hoc tests could not lead to any concrete conclusion regarding the selection of deep learning methods with respect to multiple performance metrics. To deal with this problem, we applied the Pareto dominance approach to select methods that perform optimally with respect to various performance metrics. Pareto dominance approach revealed that GAT-BERT and ASGCN are the only two non-dominated methods amongst the top 10 accurate methods. The reason behind the good performance of both methods is the usage of syntactical information. Both methods utilize the dependency graph of the input sentence, but their underlying architectures are different. GAT-BERT uses Graph Attention Network, whereas ASGCN uses Graph Convolution Network. Furthermore, GAT-BERT also leverages dependency labels along with the dependency graph. Moreover, GAT-BERT generates contextual embeddings with the help of the BERT pre-trained model. This utilization of BERT for generating contextual embeddings penalizes the GAT-BERT model in terms of Time. To select from these two non-dominated methods, we applied the Wilcoxon test for pair-wise comparison of GAT-BERT and ASGCN on eight datasets. On basis of the Wilcoxon test GAT-BERT could not outperform ASGCN on Accuracy and F1 score metrics, whereas ASGCN outperformed GAT-BERT on Time Metric. This enabled the selection of ASGCN as the most optimal method with respect to multiple performance metrics. ABSA is very important for e-commerce business organizations. Deep Learning technology for ALSC is evolving at a very high pace. The selection of the right technology is very important to retain customers. The results of our study will aid business managers to select superior methods from a wide range of Deep Learning methods. If the performance difference of the two methods is not significantly different in terms of Accuracy and Macro-F1 score, then excessive Training time for any model is undesirable. To this end, this study evaluates the performance of 35 deep learning methods in terms of training time as well. As per this study, ASGCN is an optimal method with better performance in terms of Accuracy and Macro F1 score without compromising in time. ASGCN leverages the syntactical information of the input sentence which ensures its better performance across datasets of multiple domains. The contributions of this study are:  
After an in-depth review, it is been concluded that this is the first study to perform an extensive statistical comparison on the performance of various deep learning methods on eight datasets for ALSC. For the cost-effective research in deep learning-based ALSC, it is essential to choose a method with good performance and taking less time.