Abstract: The image sentiment analysis task aims to use machine learning models to automatically predict the observer's emotional response to images.At present,the sentiment analysis method based on the deep network has attracted wide attention,mainly through the automatic learning of the deep features of the image through the convolutional neural network.However,image emotion is a comprehensive reflection of the global contextual features of the image.Due to the limitation of the receptive field size of the convolution kernel,it is impossible to effectively capture the dependencies between long-distance emotional features.At the same time,the emotional features of different levels in the network cannot be effectively fused and utilized.It affects the accuracy of image sentiment analysis.In order to solve the above problems,this paper proposes a hierarchical graph convolutional network model,and constructs spatial context graph convolution(SCGCN) and dynamic fusion graph convolution(DFGCN).The spatial and channel dimensions are mapped respectively to learn the global context association within different levels of emotional features and the relationship dependence between different levels of features,which could improve the sentiment classification accuracy.The network is composed of four hierarchical prediction branches and one fusion prediction branch.The hierarchical prediction branch uses SCGCN to learn the emotion context expression of single-level features,and the fusion prediction branch uses DFGCN to self-adaptively aggregate the context emotion features of different semantic levels to realize fusion reasoning and classification.Experiment results on four emotion datasets show that the proposed method outperforms existing image emotion classification models in both emotion polarity classification and fine-grained emotion classification.

Key words: Image sentiment analysis, Graph convolution, Global context association, Hierarchical feature association, Fusion classification