基于BERT及双向GRU模型的慕课用户评论情感倾向性分析

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。

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

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.