Sentiment analysis of ideological and political education feedback in College English Courses Based on a bidirectional LSTM and attention mechanism

Abstract

As universities promote the deep integration of ideological and political education with the Production-Oriented Approach (POA), accurately identifying the emotional tendencies in student feedback has become a key challenge in improving teaching quality. Ideological and political emotions differ from general emotional expressions in that they involve deeper psychological activities such as value guidance and cultural identity, and are characterized by implicitness, complexity, and contextual dependence. Existing research has largely focused on teaching model design, but lacks sentiment analysis of ideological and political education feedback text, particularly in the areas of semantic understanding and weighting of key sentiment terms. To address this, this paper proposes a sentiment analysis model that integrates Bidirectional Long Short-Term Memory (BiLSTM) with an attention mechanism to accurately discriminate sentiment in ideological and political education feedback for POA college English courses. Through text preprocessing, BiLSTM bidirectionally captures contextual semantic dependencies, and an additive attention mechanism dynamically weights key sentiment terms, the model ultimately achieves positive, neutral, and negative sentiment discrimination using Softmax. Experimental results show that the model achieves an accuracy of 93.7% and an F1 score of 0.93, both outperforming baseline models and demonstrating good robustness and interpretability. This research provides an efficient, lightweight, and interpretable technical solution for sentiment analysis of ideological and political education feedback.