Aspect-level text sentiment analysis method combining Bi-GRU and AlBERT
Zhigang Xu, Bo Jin, Honglei Zhu · 2022
As more and more industries and enterprises begin to develop the online/offline business model, the demand for efficient sentiment analysis of online service user comments is increasing day by day. In view of the problem that the aspect emotion analysis model is difficult to combine the semantic information of the text effectively, and the accuracy of aspect emotion classification of the texts need to be improved, this paper uses GRU model to improve the ATAE-LSTM model and improves AlBERT model through the attention mechanism based on aspect level. The ATAE-AlBERT-BiGRU model is proposed. In this paper, comparative experiments are carried out on the SemEval 2014 dataset, and the results show that the accuracy of the proposed model is significantly improved compared with the comparison model in the aspect level emotion analysis task, and it performs well in reducing the computational load of the model.