Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (2): 223-230.doi: 10.11896/jsjkx.210100046

Special Issue: Natural Language Processing

• Artificial Intelligence • Previous Articles     Next Articles

Negative-emotion Opinion Target Extraction Based on Attention and BiLSTM-CRF

DING Feng, SUN Xiao   

  1. School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China
    Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine,Hefei University of Technology,Hefei 230601,China
  • Received:2021-01-06 Revised:2021-05-25 Online:2022-02-15 Published:2022-02-23
  • About author:DING Feng,born in 1996,postgraduate.His main research interests include na-tural language processing,machine learning and textual sentiment analysis.
    SUN Xiao,born in 1980,Ph.D,professor,is a member of China Computer Federation.His main research interests include affective computing,natural language processing,machine learning and human-machine interactions.
  • Supported by:
    National Natural Science Foundation of China(61976078).

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Abstract: 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.

Key words: Attention, BiLSTM, CRF, Negative emotion opinion target, Sentiment analysis

CLC Number: 

  • TP391
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