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。
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.
作者
潘芳
张会兵
董俊超
首照宇
PAN Fang;ZHANG Hui-bing;DONG Jun-chao;SHOU Zhao-yu(Teachers College for Vocational and Technical Education,Guangxi Normal University,Guilin,Guangxi 541004,China;Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China;School of Information and Communication,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)
出处
《计算机科学》
CSCD
北大核心
2021年第S01期264-269,共6页
Computer Science
基金
国家自然科学基金(61662013,61967005,U1811264)。
关键词
在线课程
课程评论
方面情感分析
预训练语言模型
注意力机制
Online course
Course review
Aspect-based sentiment analysis
Pre-training language model
Attention mechanism
