Abstract: With the deep advancement of smart education,intelligent analysis of teaching evaluation texts has become a crucial research direction for improving educational quality.Teaching evaluation texts typically exhibit features such as multidimensional coexistence,implicit emotional expression,and imbalanced category distribution,which impose higher demands on fine-grained sentiment analysis methods.To address this,a sentiment analysis model named CrossAtt-CapsNet-RoBERTa is proposed,integrating the RoBERTa pretrained language model,cross-attention mechanism,and capsule network.The model first employs RoBERTa to obtain deep semantic representations of texts and aspect categories.Then it strengthens the correlation between aspect categories and context through a cross-semantic cross-attention mechanism.Finally,it introduces learnable category-guided capsules and achieves joint modeling of aspect detection and sentiment classification via dynamic routing.To validate the model's perfor-mance,a real teaching evaluation dataset encompassing nine aspect categories is independently constructed.Experimental results show that the proposed model achieves a sentiment classification accuracy of 91.3% on the public dataset Res14 and 83.68% on the public dataset MAMS-ACSA,both surpassing baseline models;on the independently constructed real teaching evaluation dataset,the aspect detection F1 score reaches 79.95% and sentiment classification accuracy reaches 92.76%,both outperforming comparative models.Ablation experiments further confirm the effectiveness of design elements such as the cross-attention mecha-nism and category-guided capsules.Additionally,the model demonstrates strong adaptability and generalization capabilities in few-shot scenarios.These findings provide effective technical insights for the intelligent processing of teaching evaluations.

Key words: Aspect category sentiment analysis, Capsule network, Cross-attention mechanism, RoBERTa, Teaching evaluation