Application of Real-time Sentiment Analysis Based on Mini-Xception in Classroom Teaching

The in-depth understanding and application of teacher and student data by learning analytics technology provide a new development perspective for the education field, in which emotional data play a vital role in evaluating teaching quality and learning effects. Currently, sentiment analysis technology is developing rapidly, but its application in the educational field is lagging. Most of the research is based on sentiment analysis of published texts on social media or videos recorded by students, which may lead to problems such as incomplete feedback content and delayed feedback analysis. Based on the mini-Xception framework, this paper implements the real-time identification and analysis of student sentiment in classroom teaching. Through the feedback results, teachers can fully understand the degree of student engagement and provide reasonable suggestions for subsequent teaching progress. The experimental results show that this method has high recognition accuracy for the real-time detection of seven student sentiments, and the average accuracy is 76.71 %. Compared with after-class student feedback or real-time text sentiment analysis, it can better reflect the real time and high efficiency of information feedback. It provides a basis for teachers to control the teaching rhythm and evaluate the teaching effect and is an effective method for realizing personalized teaching.