计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 264-269.doi: 10.11896/jsjkx.200800116

• 智能计算 • 上一篇    下一篇

基于高效Transformer的中文在线课程评论方面情感分析

潘芳1, 张会兵2, 董俊超2, 首照宇3   

  1. 1 广西师范大学职业技术师范学院 广西 桂林541004
    2 桂林电子科技大学广西可信软件重点实验室 广西 桂林541004
    3 桂林电子科技大学信息与通信学院 广西 桂林541004
  • 出版日期:2021-06-10 发布日期:2021-06-17
  • 通讯作者: 张会兵(zhanghuibing@guet.edu.cn)
  • 作者简介:panfang@mailbox.gxnu.edu.cn
  • 基金资助:
    国家自然科学基金(61662013,61967005,U1811264)

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).

摘要: 准确挖掘在线课程评论中蕴涵的情感信息对在线课程的健康发展极具价值。现有中文在线课程评论情感分析研究大多为分析整条评论句子情感极性的粗粒度模型,无法准确表达课程评论句子中各个方面的细粒度情感。为此,提出一种基于高效Transformer的中文在线课程评论方面情感分析模型。首先,通过ALBERT预训练模型获得评论文本方面和上下文的动态字向量编码;然后,采用可以并行输入字向量的高效Transformer分别对课程评论文本的方面和上下文进行语义表征;最后,使用交互注意机制交互地学习课程评论文本中方面和上下文的重要部分,并输入方面和上下文的最终表示到情感分类层进行在线课程评论情感极性预测。在中国MOOC网真实数据集上的实验结果表明,高效Transformer中文在线课程评论方面情感分析模型与基线模型相比,在更低的时间开销下准确率达到了80%以上。

关键词: 方面情感分析, 课程评论, 预训练语言模型, 在线课程, 注意力机制

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

中图分类号: 

  • TP391
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