BGAT: Aspect-based sentiment analysis based on bidirectional GRU and graph attention network

In today's social media and various frequently used lifestyle applications, the phenomenon that people express their sentiment via comments or instant barrage is common. People not only show their joys and sorrows in the process of expression but also present their opinions to one thing in many aspects which include. Nowadays, aspect-based sentiment analysis has become a mature and wildly-used technology. There are many public datasets considered as a benchmark to test model performance, such as Laptop2014, Restaurant2014, Twitter, etc. In our work, we also use these public datasets as the test criteria. Current mainstream models generally use the methods of stacking multi-RNNs layers or combining neural networks and BERT or other pre-trained models. On account of the importance displayed by the dependence between aspect words and sentiment words, we investigate a novel model (BGAT) blending bidirectional gated recurrent unit (BiGRU) and relational graph attention network (RGAT) to learn dependencies information. Extensive experiments have been conducted on five datasets, the results demonstrate the great capability of our model.

Automated summary

In today's social media and lifestyle applications, expressing sentiments through comments or instant barrage is common. Aspect-based sentiment analysis has become a widely-used technology, using public datasets as benchmarks. Current models stack multi-RNNs layers or combine neural networks with pre-trained models. Considering the importance of dependencies between aspect words and sentiment words, a novel model (BGAT) blending BiGRU and RGAT is investigated to learn dependencies information. Extensive experiments on five datasets demonstrate the great capability of the model.