Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 264-269.doi: 10.11896/jsjkx.200800116

• Intelligent Computing • Previous Articles     Next Articles

Aspect Sentiment Analysis of Chinese Online Course Review Based on Efficient Transformer

PAN Fang1, ZHANG Hui-bing2, DONG Jun-chao2, SHOU Zhao-yu3   

  1. 1 Teachers College for Vocational and Technical Education,Guangxi Normal University,Guilin,Guangxi 541004,China
    2 Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
    3 School of Information and Communication,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:PAN Fang,born in 1982,master,asso-ciate professor.Her main research interests include educational big data and online learning behavior analysis.
    ZHANG Hui-bing,born in 1976,Ph.D,is a member of China Computer Federation.His main research interests include educational big data and social computing.
  • Supported by:
    National Natural Science Foundation of China(61662013,61967005,U1811264).

Abstract: It is of great value for the healthy development of online courses that accurately mine the emotional information contained in online course reviews.Most of the existing research on sentiment analysis of Chinese online course reviews is a coarse-grained model,which cannot accurately express the fine-grained sentiment for all aspects of the review sentence.The paper puts forward an efficient Transformer based sentiment analysis model for Chinese online course review.Firstly,the dynamic word vector coding of the review's aspect and context is obtained by the Albert pre-training model.Then,the semantic representation of the review's aspect and context is carried out by the efficient Transformer which can input the word vector in parallel.Finally,it uses the interactive attention mechanism to learn the important parts of the context and aspects in the course review,and puts its final representation into the sentiment classification layer to predict the sentiment polarity.Experimental results on real datasets of MOOC in China show that the accuracy of the proposed model achieves more than 80% at lower time cost compared with the baseline model.

Key words: Aspect-based sentiment analysis, Attention mechanism, Course review, Online course, Pre-training language model

CLC Number: 

  • TP391
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