novel dataset of image-caption pairs where the caption both describes an image, and also convey the desired sentiment. [We summarize the new dataset, and the crowd-sourcing task to collect image-sentiment caption data. More details of the data collection process are included in the suplementary.]{}

There are many ways a photo could evoke emotions. In this work, we focus on creating a collection and learning sentiments [*from an objective viewer*]{} who does not know the back story outside of the photo – a setting also used by recent collections of objectively descriptive image captions [@chen2015microsoft; @hodosh2013framing].

[[**[Dataset construction.]{}**]{}]{} We design a crowd-sourcing task to collect such objectively described emotional image captions. This is done in a caption re-writing task based upon objective captions from MSCOCO [@chen2015microsoft] by asking Amazon Mechanical Turk (AMT) workers to choose among ANPs of the desired sentiment, and incorporate one or more of them into any one of the five existing captions. Detailed design of the AMT task is in the appendix.

The set of candidate ANPs required for this task is collected from the captions for a large sets of online images. We expand the Visual SentiBank [@borth2013sentibank] vocabulary with a set of ANPs from the 