displayed in a random order (example provided in supplement). One sentence is from the [*CNN+RNN*]{} model without sentiment, while the other sentence is from [[*SentiCap*]{} or one of the systems being compared]{}. AMT workers are asked to rate the descriptiveness of each image from 1-4 and select the more positive or more negative image caption. A process for filtering out noisy ratings is described in the supplement. Each pair of sentences is rated by three different AMT workers; at least two must agree that a sentence is more positive/negative for it to be counted as such. The descriptiveness score uses mean aggregation.

[[**[Results.]{}**]{}]{} Table \[tab:senticap\] summarizes the automatic and crowd-sourced evaluations. We can see that [*CNN+RNN*]{} presents almost no sentiment ANPs as it is trained only on MSCOCO. [*SentiCap*]{} contains significantly more sentences with sentiment words [than [any of the three]{} baseline methods,]{} which is expected when the word-level regularization has taken effect. [That [*SentiCap*]{} has more sentiment words than the two insertion baselines [*ANP-Replace*]{} and [*ANP-Scoring*]{} shows that [*SentiCap*]{} actively drives the flow of the sentence towards using sentimental ANPs. Sentences from [*SentiCap*]{} are, on average, judged by crowd sourced workers to have stronger sentiment than any of the 