Construction and Application of a Time‐Series Sentiment Analysis Model for Online Learning: A Case of Online Teacher Training

Background Online teacher training has emerged as a crucial pathway for professional development. However, the spatiotemporal separation inherent in online learning often gives rise to emotional challenges, including loneliness, fatigue, and reduced engagement, which can compromise learning outcomes. While prior studies recognize the value of emotion analysis and feedback, they typically rely on static or snapshot measures, limiting insights into the temporal evolution of learners' affective states and constraining timely interventions. Objectives This study aims to develop a time‐series sentiment analysis model that enables real‐time, automated monitoring and feedback of teachers' emotional states in online learning environments, thereby enhancing engagement, self‐regulation, and overall learning effectiveness. Methods A BERT‐based deep learning model was employed to classify textual sentiment, and an orthogonal polynomial regression approach captured temporal dynamics of affective states. The integrated model was embedded within an online Project‐Based Learning platform under a blended education framework. A sequential within‐subject quasi‐experimental design was adopted, combining questionnaire surveys and semi‐structured interviews, with Friedman tests and Wilcoxon signed‐rank post hoc comparisons used to assess the impact of time‐series emotional feedback on learners' online learning outcomes. Results and Conclusions The proposed model significantly improves online teacher training by sustaining engagement, reducing attrition risk, and supporting adaptive cognitive‐emotional regulation. Real‐time visualization and automated feedback facilitate timely interventions, enabling teachers to adjust strategies and maintain reflective learning practices. These findings demonstrate the potential of time‐series sentiment analysis to inform data‐driven, emotion‐aware design of online professional development, advancing both methodological innovation and practical applications in teacher training.