Relational Graph Attention Network for Aspect-based Sentiment Analysis
Table 2: Overall performance of different methods on the three datasets.
['Category', 'Method', 'Restaurant Accuracy', 'Restaurant Macro-F1', 'Laptop Accuracy', 'Laptop Macro-F1', 'Twitter Accuracy', 'Twitter Macro-F1']
5.3.1 Overall Performance First, the R-GAT model outperforms most of the baseline models. Second, the performance of GAT can be significantly improved when incorporated with relational heads in our aspect-oriented dependency tree structure. It also outperforms the baseline models of ASGCN, and CDT, which also involve syntactic information in different ways. This proves that our R-GAT is better at encoding the syntactic information. Third, the basic BERT can already outperform all the existing ABSA models by significant margins, demonstrating the power of this large pre-trained model in this task. Nevertheless, after incorporating our R-GAT (R-GAT+BERT), this strong model sees further improvement and has achieved a new state of the art. These results have demonstrated the effectiveness of our R-GAT in capturing important syntactic structures for sentiment analysis.