## 1 Introduction  
With the vast popularity of social networks, people tend to express their emotions and share their experiences online through posting images [1], which promotes the study of the principles of human emotion and the analysis and estimation of human behavior. Recently, with the wide application of convolution neural networks (CNNs) in emotion prediction, numerous studies [2, 3, 4] have proved the excellent ability of CNN to recognize the emotional features of images. Based on the theory that the emotional cognition of a stimulus attracts more human attention [5], some researchers enriched emotional prediction with saliency detection or instance segmentation to extract more concrete emotional features [6, 7, 8]. [PERSON] et al. [9] put forward the \"Affective Regions\" which are objects that convey significant sentiments, and proposed three fusion strategies for image features from the original image and \"Affective Regions\". Alternatively, [PERSON] et al. [8] utilized saliency detection to enhance the local features, improving the classification performance to a large margin. \"Affective Regions\" or Local features in images play a crucial role in image emotion, and the above methods can effectively improve classification accuracy. However, although these methods have achieved great success, there are still some drawbacks. They focused on improving visual representations and ignored emotional effectiveness of objects, which leads to a non-tendential feature enhancement. For example, in an image expressing a positive sentiment, positivity is generated by interaction among objects. Separating objects and directly merging the features will lose much of the critical information of image. Besides, they also introduce a certain degree of noise, which leads to the limited performance improvement obtained through visual feature enhancement. For example, in human common sense, \"cat\" tends to be a positive categorical word. As shown in Figure 1a,b, when \"cat\" forms the image with other neutral or positive objects, the image tends to be more positive, consistent with the conclusion that local features can improve accuracy. In the real world, however, there are complex images, as shown in Figure 1c,d, \"cat\" can be combined with other objects to express opposite emotional polarity, reflecting the effect of objects on emotional interactions. Specifically, in Figure 1d, the negative sentiment is not directly generated by the \"cat\" and \"injector\", but the result of the interaction between the two in the emotional space. Indiscriminate feature fusion of such images will affect the performance of the classifier. To address the abovementioned problems, we design a framework with two branches, one of which uses a deep network to extract visual emotional features in images. The other branch uses GCN to extract emotional interaction features of objects. Specially, we utilize Detectron2 to obtain the object category, location, and additional information in images. And then, SentiWordNet [10] is selected as an emotional dictionary to mark each category word with emotional intensity value. Based on the above information, we use the sentimental value of objects and visual characteristics in each image to build the corresponding graph model. Finally, we employ GCN to update and transmit node features, generate features after object interaction, which, together with visual components, serve as the basis for sentiment classification. The contributions of this paper can be summarized as follows:  
1. We propose an end-to-end image sentiment analysis framework that employs GCN to extract sentimental interaction characteristics among objects. The proposed model makes extensive use of the interaction between objects in the emotional space rather than directly integrating the visual features. 2. We design a method to construct graphs over images by utilizing Detectron2 and SentiWordNet.