three baselines. For positive [*SentiCap*]{}, 88.4% are judged to have a more positive sentiment than the [*CNN+RNN*]{} baseline. These gains are made with only a small reduction in the descriptiveness [– yet this decrease is due to a minority of failure cases, since 84.6% of captions ranked favorably in the pair-wise descriptiveness comparison.]{} [*SentiCap*]{} negative sentences are judged to have more negative sentiment 72.5% of the time. On the automatic metrics [*SentiCap*]{} generating negative captions outperforms all three baselines by a margin.]{} This improvement is likely due to negative [*SentiCap*]{} being able to learn more reliable statistics for the new words that only appear in negative ANPs.

[*SentiCap*]{} sentences with positive sentiment were judged by AMT workers as [*more interesting*]{} than those without sentiment in 66.4% of cases, which shows that our method improves the expressiveness of the image captions. On the other hand, negative sentences were judged to be [*less interesting*]{} than those without sentiment in 63.2% of cases. This is mostly due to that negativity in the sentence naturally contradicts with being [*interesting*]{}, a positive sentiment.

It has been noted by [@vinyals2015show] that RNN captioning methods tend to exactly reproduce sentences from the training set. Our [SentiCap]{} method 