Enhanced text clustering and sentiment analysis framework for online education: a BIF-DCN approach in computer education

Understanding students' emotional responses to course content and assignments is crucial for developing effective teaching strategies and improving online learning resources. To address this need, we propose a novel deep learning-based framework called BERT and BTF-IDF Integrated Framework with Deep Clustering Network (BIF-DCN), designed to accurately analyze student sentiment on educational platforms. The framework combines three key components: Bidirectional Encoder Representations from Transformers (BERT) for initial text feature extraction, Bi-level Term Frequency-Inverse Document Frequency (BTF-IDF) for enhanced feature representation, and an Improved Deep Embedded Clustering (IDEC) model for sentiment classification. BERT captures rich semantic features from student comments, which are further refined using BTF-IDF to highlight informative terms. These features are then clustered using the IDEC model to identify underlying sentiment-based topics. Experimental results show that BIF-DCN achieves higher clustering accuracy than existing IDEC-based and traditional single-model approaches on both public and self-constructed datasets. In addition to performance improvements, our method enables in-depth sentiment analysis of clustered topics, offering practical insights for optimizing teaching materials. This framework provides educators with valuable tools to better understand student needs and deliver more personalized and effective instruction, ultimately enhancing teaching quality and learner satisfaction.