BERT IAN Model for Aspect based Sentiment Analysis
Aspect based sentiment analysis is different from document level and sentence level sentiment analysis, which aims to predict the sentiment polarity of a certain aspect in a sentence. The accuracy of the existing aspect based sentiment analysis model still needs to be improved. A BERT IAN sentiment analysis model that improves the Interactive Attention Networks (IAN) model is proposed to further improve the accuracy of the aspect based sentiment analysis. First use the BERT pre training model to encode aspects and context respectively. Then use a transformer encoder with interactive attention to interactively learn the attention of the aspect and context, and generate a final representation. Finally, through the sentiment classification layer, the aspect corresponding sentiment are analyzed. The experimental results on Restaurant and Laptop datasets show the effectiveness and superiority of the BERT IAN model.
2020 International Conference on Communications, Information System and Computer Engineering (CISCE)