Sentiment Analysis in Political Education Reviews Using Aspect-Level Coupled Reflective Network

As ideological and political education strengthens in institutions of higher education, there is a growing need for automated methods that identify emotions and ideological stances in student comments. Existing approaches have focused on sentiment polarity and have struggled with multidimensional emotions, sentiment–ideology interactions, and indirect or metaphorical expressions, resulting in limited granularity and interpretability. To address these challenges, this study proposes an Aspect-level Coupled Reflective Network (ACR-Net) for the joint modeling of sentiment and ideology. ACR-Net extracts aspect semantics using large language models, builds fused representations through a module coupling ideology with emotion, and incorporates a sentiment weighted network and reflective calibration mechanism to capture local cues and handle implicit expressions. Experiments on real ideological and political student comments demonstrated that ACR-Net surpasses existing methods in accuracy, interpretability, and generalization. Our code is available at https://github.com/jialekang/Aspect-level-Coupled-Reflective-Network.git.