Emotion Analysis of Ideological and Political Education Using a GRU Deep Neural Network

Abstract

Theoretical research into the emotional attributes of ideological and political education can improve our ability to understand human emotion and solve socio-emotional problems. To that end, this study undertook an analysis of emotion in ideological and political education by integrating a gate recurrent unit (GRU) with an attention mechanism. Based on the good results achieved by BERT in the downstream network, we use the long focusing attention mechanism assisted by two-way GRU to extract relevant information and global information of ideological and political education and emotion analysis, respectively. The two kinds of information complement each other, and the accuracy of emotion information can be further improved by combining neural network model. Finally, the validity and domain adaptability of the model were verified using several publicly available, fine-grained emotion datasets.