Emotion Tagging

To address the statistical similarity between the predicted emotion labels and ground-truth emotion labels without any assumptions, we propose a novel emotion tagging approach through adversarial learning. Specifically, the proposed emotion tagging approach consists of an emotional tag classifier C and a discriminator D.

To address the shortcomings in emotion tagging, we consider applying emotion relationship patterns at both feature and label levels. We propose a novel emotion tagging framework, that makes full use of the emotion relationship patterns in local and global distribution.

To the best of our knowledge, this paper is the first work to assign multiple emotions to multimedia data by exploring the emotional relationships at both feature and label levels. By learning the shared features with a multi-task RBM classifier and modeling the dependencies among emotion labels with a hierarchy RBM model, the proposed approaches can exploit both top-down and bottom-up relations among emotions independently and dependently to improve multiple emotions tagging for multimedia.