## 2 Related Work  
### Visual Sentiment Prediction  
Existing methods can be classified into two groups: dimensional spaces and categorical states. Dimensional spaces methods employ valence-arousal space [11] or activity-weight-heat space [12] to represent emotions. On the contrary, categorical states methods classify emotions into corresponding categories [13; 14], which is easier for people to understand, and our work falls into categorical states group.  
Feature extraction is of vital importance to emotion analysis, various kinds of features may contribute to the emotion of images [15]. Some researchers have been devoting themselves to exploring emotional features and bridging the \"affective gap\", which can be defined as the lack of coincidence between image features and user emotional response to the image [16]. Inspired by art and psychology, [PERSON] and [PERSON] [14] designed low-level features such as color, texture, and composition. [PERSON] et al. [17] proposed extensive use of visual image information, social context related to the corresponding users, the temporal evolution of emotion, and the location information of images to predict personalized emotions of a specified social media user.  
With the availability of large-scale image datasets such as ImageNet and the wide application of deep learning, the ability of convolutional neural networks to learn discriminative features has been recognized. [PERSON] et al. [3] fine-tuned the pre-trained AlexNet on ImageNet to classify emotions into eight categories. [PERSON] et al. [18] integrated deep metric learning with sentiment classification and proposed a multi-task framework for affective image classification and retrieval.  
[PERSON] et al. [19] discovered affective regions based on an object proposal algorithm and extracted corresponding in-depth features for classification. Later, [PERSON] et al. [20] adopted an attention algorithm to utilize localized visual features and got better emotional classification performance than using global visual features. To mine emotional features in images more accurately, [PERSON] et al. [6] combined the saliency detection method with image sentiment analysis. They concluded that images containing prominent artificial objects or faces, or indoor and low depth of field images, often express emotions through their saliency regions. To enhance the work theme, photographers blurred the background to emphasize the main body of the picture [14], which led to the birth of close-up or low-depth photographs. Therefore, the focus area in low-depth images fully expresses the information that the photographer and forwarder want to tell, especially emotional information.  
On the other hand, when natural objects are more prominent than artificial objects or do not contain faces, or open-field images, emotional information is usually not transmitted only through their saliency areas. Based on these studies, [PERSON] et al. [7] established an image dataset labeled with statistical data of eye-trackers on human attention to exploring the relationship between human attention mechanisms and emotional characteristics. [PERSON] et al. [9] synthetically considered image objects and emotional factors and obtained better sentiment analysis results by combining the two pieces of information.  
Such methods make efforts in extracting emotional features accurately to improve classification accuracy. However, as an integral part of an image, objects may carry emotional information. Ignoring the interaction between objects is unreliable and insufficient. This paper selects the graph model and graph convolution network to generate sentimental interaction information and realize the sentiment analysis task.