| Title |
Student Emotion Analysis by Integrating Attention Mechanism Algorithm and Neural Network Algorithm |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.6.642 |
| Keywords |
Sentiment analysis; Attention mechanism; Neural networks; Emotional resource attention; Door control unit; Course teaching evaluation |
| Abstract |
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. |