Affective Ad Dataset

Advertisements(ads) often include strongly emotional content toleave a lasting impression on the viewer. This work (i) compiles anaffective ad dataset capable of evoking coherent emotions acrossusers, as determined from the affective opinions of five expertsand 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising,and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.


The annotations can be downloaded here