GCN-LSTM: Multi-label educational emotion prediction based on graph Convolutional network and long and short term memory network fusion label correlation in online social networks

Zhiguang Liu 1∗ , Fengshuai Li 2 , Guoyin Hao 3 , Xiaoqing He 1 , and Yuanheng Zhang 1

Computer Science and Information Systems, Tome 21 (2024) no. 4.

Résumé

Although there are a lot of methods for multi-label classification in the past research, there are still many problems. For example, in the real world, labels are not necessarily independent of each other, and there may be some connection between labels. Therefore, exploring and utilizing the interdependence between labels is a key issue in current research. For example, in the photo category, a picture that contains blue sky often also contains white clouds, and in the text category, a political story is less likely to be entertainment news. Therefore, the key to improve the accuracy of multi-label classification is to effectively learn the possible correlations between each label. Therefore, we propose a novel multi-label educational emotion prediction based on graph convolutional network and long and short term memory network fusion label correlation in online social networks. This model uses Word2Vec method to train word vectors and combines graph convolutional neural network (GCN) with long and short term memory network (LSTM). The GCN is used to dig deeper word features of text, the LSTM layer is used to learn the longterm dependence relationship between words, and the multi-granularity attention mechanism is used to assign higher weight to the affective word features. At the same time, label correlation matrix is used to complete the label feature vector and text features as the input of the classifier, and the correlation between labels is investigated. The experimental results on the open data set show that the proposed model has a good classification effect compared with other advanced methods. The research results promote the combination of deep learning and affective computing, and can promote the research of network user behavior analysis and prediction, which can be used in personalized recommendation, targeted advertising and other fields, and has wide academic significance and application prospects.

Détail

Keywords: multi-label educational emotion prediction, GCN, LSTM, multi-granularity attention mechanism