Object aroused emotion analysis network for image sentiment analysis

Abstract

From cognitive psychology, objects are closely related to emotions, and inherently possess the ability to arouse human emotions. Hence, fully utilizing the relationships between objects and emotions can help achieve more accurate visual emotion recognition. In this paper, we propose a novel object aroused emotion analysis network to realize image sentiment classification by investigating the interactions between objects and emotions. To quantify the various emotion potencies of each object, a novel object emotion distribution module is proposed to explore the mapping among objects and emotions, and quantitatively demonstrate how various objects arouse different emotions. An object emotion-modeling mapping module is proposed to analyze the effect of objects and object combinations with emotion information on image sentiment; this module maps common objects to emotion dimensions and improves image sentiment classification with abundant object combination information. Then, we fuse the object-emotion mapping relation and multi-model features using BiGRU, thus realizing more accurate emotion recognition. Extensive experiments on widely used emotion datasets prove that our proposed method achieves excellent performance and outperforms most state-of-the-art image sentiment classification methods.