基于双重多视角表示的目标级隐性情感分类OACSTPCD

Target-level implicit sentiment classification is a critical sentiment analysis task in natural language processing.Many existing studies mainly focused on modeling context-aware targets,and their modeling information source were relatively single,making it difficult to adequately capture the implicit sentiment of the target in the text.This study proposes a target-level sentiment classification method based on dual multiview representation learning that models the target and text from three information views.Specifically,this study designs a representation learning model from the text,the view of the graph,and the view of external knowledge and exploits a convolutional neural network to deeply integrate the representations of the three views.Moreover,the proposed method learns target-dependent representations from these views.Finally,the semantic representations of the text and the target are combined and fed into the sentiment classifier.The results of experiments conducted on five public datasets and comparative experiments with eight baseline models show that the solution achieves state-of-the-art performance.In particular,the F1m of the proposed model is 1.0%and 2.6%higher than those of previous best models on NewsMTSC-mt and NewsMTSC-rw implicit sentiment analysis datasets,respectively.In addition,the F1m of the proposed model is 3.6%,1.4%,and 1.6%higher than those of the previous best models on Laptop14,Restaurant14,and Twitter explicit emotion analysis datasets,respectively.