A text sentiment multi-classification method based on dual graph convolutional network

At present, text sentiment multi-classification model has problems of insufficient semantic feature fusion and ignore the syntactic structure of sentences. Therefore, this paper proposes a dual graph convolutional network model, which extract semantic and syntactic information of text by semantic graph convolutional network and syntactic graph convolutional network. Also, this paper proposes a label graph embedding method to fuse richer semantic features. Finally, experiments on two public datasets show that our method achieves better results.