Research on Feedback System of Ideological and Political Teaching Resource Base Based on Emotion Analysis and Deep Learning

This paper proposes and implements a feedback system of ideological and political teaching resource base based on emotion analysis and deep learning, aiming to capture students' emotional responses to teaching resources through technical means, and then optimize teaching content and teaching strategy. The system uses the deep learning model of Markov logic network to analyze the emotional tendency of the text feedback generated by students on the interactive platform. By quantifying the positive or negative emotions of student reviews, the system was able to identify which teaching resources were more popular and which might need improvement. In addition, the system integrates natural language processing (NLP) technology to extract key information from feedback, such as student needs, confusion points, and suggestions, so that teachers can tailor lesson plans. The experimental results show that the system can accurately analyze students' emotions and provide valuable feedback data for teachers. Compared with the traditional subjective assessment, this system provides a more objective and efficient teaching quality assessment method.