NAGNet: A novel framework for real-time students' sentiment analysis in the wisdom classroom

Facial expression is the basis of human emotion recognition, and we can infer the emotional state of human beings by analysing facial expressions. As the main method of human emotional expression, facial expression contains much information about inner emotional changes. In recent years, with the wide application of artificial intelligence in education, related research on student sentiment analysis has become a hot topic in the educational technology field. Analysing affective data is helpful for understanding students' learning status and provides an important basis for the effective implementation of learning interventions, which is of great significance for the evaluation of teaching effects and changes in teaching methods. However, traditional learner emotion recognition methods have some problems, such as low recognition rates, complex algorithms, poor robustness, and easy loss of the key information of facial expression features. Therefore, this article proposes a learner emotion recognition method based on NAGNet. The network model is composed of Res2Net, nonlocal attention and GeM pooling, which can fuse global expression feature information to realize fine-grained sentiment analysis. Additionally, we conducted training and experiments on the large-scale learner emotion dataset FERPlus. The NAGNet model trained by the public emotion dataset FERPlus has a recognition accuracy of 89.3% for eight kinds of student emotions. The experimental results show that the method can quickly and accurately identify learner emotional states. We conduct experiments in a real classroom scenario, and use the NAGNet model proposed in this article to analyse and detect students' real-time sentiment. Then teachers improve teaching methods by understanding the feedback of students' classroom status. Therefore, this method has important reference significance in the construction of wisdom classrooms and wisdom learning environments.