VELDA: Relating an Image Tweet’s Text and Images

Abstract

Image tweets are becoming a prevalent form of socialmedia, but little is known about their content — textualand visual — and the relationship between the two mediums.Our analysis of image tweets shows that while visualelements certainly play a large role in image-text relationships, other factors such as emotional elements, also factor into the relationship. We develop Visual-Emotional LDA (VELDA), a novel topic model to capturethe image-text correlation from multiple perspectives (namely, visual and emotional). Experiments on real-world image tweets in both Englishand Chinese and other user generated content, show that VELDA significantly outperforms existingmethods on cross-modality image retrieval. Even in other domains where emotion does not factor in imagechoice directly, our VELDA model demonstrates good generalization ability, achieving higher fidelity modeling of such multimedia documents.