Student Emotion Analysis by Integrating Attention Mechanism Algorithm and Neural Network Algorithm

This study proposes an emotion analysis model for the education system, aiming to improve teaching quality and effectiveness. The model integrates multiple attention mechanisms and neural network algorithms to construct a sentiment analysis model. By dynamically adjusting the allocation of global and local attention weight and enhancing semantic information through a gating unit, hidden information in the text is fully mined. Model validation indicates that the fused multi-attention mechanism model outperforms the single attention model by increasing the F1 value by 5.76% on average. Compared to other models, the integrated model shows higher accuracy and a 6.91% average increase in F1 value. The proposed model also demonstrates superior classification accuracy compared to a convolutional neural network model. Overall, the study concludes that the integrated student sentiment analysis model effectively considers hidden text information, leading to improved text classification and sentiment analysis results.