基于BERT及双向GRU模型的慕课用户评论情感倾向性分析

计算机与现代化 ›› 2021, Vol. 0 ›› Issue (04): 20-26.

基于BERT及双向GRU模型的慕课用户评论情感倾向性分析

(新疆师范大学计算机科学技术学院，新疆乌鲁木齐830054)

出版日期:2021-04-22 发布日期:2021-04-25
作者简介:尼格拉木·买斯木江（1993—），女，新疆乌鲁木齐人，硕士研究生，研究方向：自然语言处理，E-mail: 1248929491@qq.com; 通信作者：艾孜尔古丽·玉素甫（1987—），女，新疆乌鲁木齐人，副教授，硕士生导师，博士，研究方向：计算语言学，自然语言处理，E-mail: Azragul2010@126.com。
基金资助:
新疆维吾尔自治区自然科学基金资助项目(2017D01A58)

Analysis of Emotional Tendency of MOOC User Comments Based on BERT and Bidirectional GRU Model#br#

(College of Computer Science and Technology, Xinjiang Normal University, Urumqi 830054, China)

Online:2021-04-22 Published:2021-04-25

摘要/Abstract

摘要： 以实现慕课网用户评论的情感倾向性分析为目的，本文提出一种基于BERT和双向GRU模型的用户评论情感倾向性分类方法。首先使用BERT模型提取课程评论文本的特征表示，其次将获取的词语特征输入BiGRU网络实现用户评论的情感特征的提取，最后用Softmax逻辑回归的方式进行情感倾向性分类。实验结果表明基于BERT和双向GRU模型的评论情感倾向性分类模型的F1值达到92.5%，提高了用户情感倾向性分析的准确率，从而验证了方法的有效性。

关键词: 课程评论, 文本情感分析, 中文文本分类, BERT-BiGRU

Abstract: For the purpose of realizing the sentimental analysis of the user reviews of MOOC, this article proposes a method for the sentimental classification of user reviews based on the BERT and BiGRU model. The article uses the BERT model to extract the feature representation of the course review text, and the acquired words features are input to the BiGRU network to extract the emotional features of user reviews, finally the emotional tendency classification is performed by Softmax logistic regression. Experimental results show that the F1 value of the review sentiment orientation classification model based on BERT and BiGRU model reaches 92.45%, which improves the accuracy of user sentiment orientation analysis and is better than other mainstream orientation analysis models, proving the effectiveness of the method.

Key words: course reviews, text sentiment analysis, Chinese text classification, BERT-BiGRU

尼格拉木·买斯木江, 艾孜尔古丽·玉素甫. 基于BERT及双向GRU模型的慕课用户评论情感倾向性分析[J]. 计算机与现代化, 2021, 0(04): 20-26.

Nigara Masimjan, Azragul Yusuf. Analysis of Emotional Tendency of MOOC User Comments Based on BERT and Bidirectional GRU Model#br#[J]. Computer and Modernization, 2021, 0(04): 20-26.

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