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A Study on Learners' Emotion Classification Based on Improved CNN Algorithm in Online Teaching and Learning

algorithms-and-analysis-of-algorithms
artificial-intelligence
data-mining-and-machine-learning
social-computing
neural-networks

Abstract

With the rapid advancement of online education, students' sentiment feedback serves as a pivotal factor in enhancing course quality and refining pedagogical strategies. However, conventional sentiment analysis approaches often struggle with unstructured textual data, limiting their capacity to precisely discern the emotional inclinations embedded in student comments. To address this challenge, this study introduces RBTCN-Net, a novel framework integrating RoBERTa, CNN, Bi-LSTM, and an attention mechanism to classify sentiment within an online learning environment. Specifically, RoBERTa is employed to extract deep semantic representations, CNN captures localized sentiment features, Bi-LSTM models temporal dependencies, and the attention mechanism amplifies critical sentiment-related information, thereby improving classification accuracy and robustness. Experimental evaluations demonstrate that RBTCN-Net surpasses standalone deep learning models in positive and negative sentiment classification across publicly available datasets. The results underscore the framework’s capability to effectively analyze sentiment tendencies in online educational discourse, offering valuable data-driven insights for personalized instruction and course refinement. Beyond enhancing sentiment analysis in digital learning contexts, this study also provides innovative technical solutions and pragmatic pathways for the development of intelligent teaching evaluation systems
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