YFCC100M image captions [@thomee2015yfcc100m] [as]{} the overlap between the original SentiBank ANPs and the MSCOCO images is insuffcient. We keep ANPs with non-trival frequency and a clear positive or negative sentiment, when rated in the same way as SentiBank. This gives us 1,027 ANPs with a positive emotion, 436 with negative emotions. We collect at least 3 positive and 3 negative captions per image. Figure \[fig:mturk\_eval\](a) contains one example image and its respective positive and negative caption written by AMT workers. We release the list of ANPs and the captions in the online appendix.

[[**[Quality validation.]{}**]{}]{} We validate the quality of the resulting captions with another [two-question]{} AMT task as detailed in the suppliment. This validation is done on [124 images with 3 neutral captions from MSCOCO]{}, and images with 3 positive and 3 negative captions from our dataset. We first ask AMT workers to rate the descriptiveness of a caption for a given image on a four-point scale [@hodosh2013framing; @vinyals2015show]. The [*descriptiveness*]{} column in Figure \[fig:mturk\_eval\](b), shows that the measure for objective descriptiveness tend to decrease when the caption contains additional sentiment. Ratings for the positive captions ([Pos]{}) have a small decrease (by 0.08, or one-tenth of the standard 