Graph convolutional network with multiple weight mechanisms for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims at determining the sentiment polarity of the given aspect term in a sentence. Recently, graph convolution network (GCN) has been used in the ABSA task and obtained promising results. Despite the proliferation of the methods and their success, prevailing models based on GCN lack a powerful constraint mechanism for the message passing to aspect terms, introducing heavy noise during graph convolution. Further, they simply average the subword vectors from BERT to form word-level embeddings, failing to fully exploit the potentials of BERT. To overcome these downsides, a graph convolutional network with multiple weight mechanisms is proposed for aspect-based sentiment analysis in the paper. Specifically, a dynamic weight alignment mechanism is proposed to encourage our model to make full use of BERT. Then an aspect-aware weight mechanism is designed to control message propagation to aspect during graph convolution operation. Afterwards, an aspect oriented loading layer is presented to further reduce adverse effects of words irrelevant with aspect term. Finally, the multi-head self attention is used to fuse high order semantic and syntax information. Hence, the model can obtain the premium aspect-specific representations for prediction. Experiments demonstrate that the proposed model can achieve state-of-the-art results compared to other models.(c) 2022 Elsevier B.V. All rights reserved.