@article{Chen_SalahEldeen_He_Kan_Lu_2015, title={VELDA: Relating an Image Tweet’s Text and Images}, volume={29}, url={https://ojs.aaai.org/index.php/AAAI/article/view/9168}, DOI={10.1609/aaai.v29i1.9168}, abstractNote={ <p> 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. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chen, Tao and SalahEldeen, Hany and He, Xiangnan and Kan, Min-Yen and Lu, Dongyuan}, year={2015}, month={Feb.} }