challenges in artificial intelligence – vision and language. Despite being a difficult problem, the research community has recently made headway into this area, thanks to large labeled datasets, and progresses in learning expressive neural network models. In addition to composing a factual description about the objects, scene, and their interactions in an image, there are richer variations in language, often referred to as styles [@crystal1969investigating]. Take emotion, for example, it is such a common phenomena in our day-to-day communications that over half of text accompanying online pictures contains an emoji (a graphical alphabet for emotions) [@instagram2015emoji]. How well emotions are expressed and understood [influences]{} decision-making [@lerner2015emotion] – from the mundane (e.g., making a restaurant menu appealing) to major (e.g., choosing a political leader in elections). Recognizing [sentiment and opinions]{} from written communications has been an active research topic for the past decade [@pang2008opinion; @socherrecursive], the synthesis of text with sentiment that is relevant to a given image is still an open problem. In Figure \[fig:intro\], [each]{} image is described with a factual caption, and with positive or negative emotion[, respectively]{}. One may argue that the descriptions with sentiments are more likely to pique interest about the subject being pictured (the 