Advanced NLP techniques for optimizing ideological and political education text analysis

Ideological and Political Education (IPE) aims to shape learners’ political understanding, moral awareness, and social responsibility. Text analysis enhances this process by automating the evaluation of large-scale educational content, enabling more consistent, efficient, and data-driven insights. Advanced Natural Language Processing (NLP) techniques play a crucial role in interpreting complex ideological texts and improving the quality of educational feedback. In this study, we address existing limitations by integrating two advanced transformer-based models—RoBERTa-GRU for deep contextual classification and GPT-4 for higher-order semantic verification. RoBERTa-GRU leverages robust attention mechanisms and sequential modeling to capture long-range dependencies, while GPT-4 provides reasoning-based validation to enhance reliability. Model performance is assessed using accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed RoBERTa-GRU model achieves a 93.5% F1-score, 85% recall, and 76.37% accuracy on Dataset 1, outperforming baselines such as CNN (74.83%) and BiLSTM-BERT (74.66%). On Dataset 2, the model achieves 65.57% accuracy, surpassing traditional RNN and transformer counterparts.