## 1 Introduction  
With the integration of internet technology into education and the expansion of the demand for online teaching in schools during the epidemic [1], online learning has been widely promoted to school education, professional education, party member education, and other educational learning classes. Thus, diversified online courses, such as MOOC, SPOC, and NetEase Cloud Classes, have emerged. These online courses focus on providing personalized learning programs for different learners, and their convenient communication learning methods and advanced education concepts have triggered educational and teaching reforms at all levels of learning [2]. Thanks to the development of new technologies, such as artificial intelligence, online interaction, and remote control, the online teaching experience has been greatly enriched. However, users also put forward higher requirements for online courses in terms of rich content, depth of theory, and quality of service. Some long-standing problems in online education, such as uneven quality of online courses and difficulty in guaranteeing learning effectiveness, also need to be solved [3]. The large scale of users has produced a large amount of online education data [4]. How to extract effective information for curriculum improvement and provide personalized learning suggestions for different users has attracted extensive attention from scholars and online education enterprises at home and abroad. Users in online learning commonly expound their views in the comments and evaluation. Through comments, users can realize the exchange of knowledge to understand or course evaluation. Potential users can learn about the course content and other user experience. Besides, the platform can also improve and enrich course teaching content by students' and teachers' comments. It can be found that online education is different from other commodities or products. Its user comments include understanding of knowledge, opinions on arguments, and evaluation of courses, among which the evaluation of course content and online teaching methods is of great significance to the improvement of online education [5]. As an important basis to reflect students' acceptance of courses and online teaching quality, the text data of online course evaluation has a strong emotional color. The analysis of students' evaluation of online courses can provide guidance for further optimization of online education quality. The text sentiment analysis is conducive to mining effective suggestions on online education and is an important way to improve online course satisfaction and online education quality. Text sentiment analysis is an important branch of comment text mining. Its main task is to recognize the emotional tendency and emotional degree of subjective text, and then complete the induction and classification of the text content, so it is also called opinion mining [6]. At present, the mainstream sentiment analysis methods can be divided into the sentiment dictionary method [7], traditional machine learning method, deep learning, and other methods. As the most classical text sentiment analysis method, the sentiment dictionary method has the characteristics of simple operation, but the method is limited in the establishment of sentiment dictionary, thesaurus scope, accuracy, and other aspects [8]. Traditional machine learning methods, such as the K-nearest neighbor algorithm (KNN), decision tree (DT), and support vector machine (SVM), can ensure the accuracy of sentiment classification to a certain extent [9], but most of the above methods rely on manually labeled training sets and features extracted by established methods. The accuracy of training set labeling and the effectiveness of features largely determine the accuracy of traditional machine learning methods. In recent years, the application of deep learning methods in text emotion analysis has become a research hotspot, such as convolutional neural network (CNN), recurrent neural network (RNN), and other deep networks [10].