%A DING Feng, SUN Xiao %T Negative-emotion Opinion Target Extraction Based on Attention and BiLSTM-CRF %0 Journal Article %D 2022 %J Computer Science %R 10.11896/jsjkx.210100046 %P 223-230 %V 49 %N 2 %U {https://www.jsjkx.com/CN/abstract/article_20552.shtml} %8 2022-02-15 %X Aspect-based sentiment analysis (ABSA) is a popular topic for natural language processing,in which opinion target extraction and sentiment polarity classification of opinion target are one of the basic subtasks of ABSA.However,few studies directly extract the opinion targets of specific emotional polarity,especially the negative emotion opinion targets with more potential value.A new ABSA subtask--negative emotion opinion target extraction (NE-OTE) is proposed,and a BiLSTM-CRF model based on attention mechanism and character and word mixture embedding (AB-CE) is proposed.On the basis of bi-directional long short-term memory (BiLSTM) learning textual semantic information and capturing long distance bi-directional semantic dependency,through the attention mechanism,the model can better pay attention to the key parts in the input sequence and capture the implied characteristics related to the opinion target and its emotional tendency.Finally,the CRF layer can be used to predict the optimal tag sequence at the sentence level,so as to extract the negative emotional opinion target.This paper builds three NE-OTE task datasets based on the mainstream ABSA task baseline datasets and conducts extensive experiments on these datasets.Experimental results show that the model proposed in this paper can effectively identify the target of negative emotional opinions,and is significantly better than other baseline models,which verifies the effectiveness of the method proposed in this paper.