Design and Implementation of Learner Sentiment Analysis System Based on LSTM in Smart Education

This paper proposes and implements a real-time sentiment analysis system based on LSTM, focusing on learner emotion recognition in a smart education environment. The system can effectively classify positive, negative, and neutral emotions by processing students' text data in online classes. In order to improve the accuracy of sentiment recognition, the system adopts a two-layer LSTM architecture and vectorizes the text through word embedding technology. Experimental results show that the LSTM model performs well in the sentiment classification task of processing long sequence texts, especially in the classification of positive and negative emotions. The system also provides teachers with instant feedback through sentiment visualization tools to help optimize classroom teaching strategies and improve student engagement. This study provides new technical support for personalized teaching in smart education, which helps to comprehensively improve teaching quality and students' learning experience.