Affect Recognition in Ads with Application to Computational Advertising
This work addresses computational advertising by improving emotion recognition in ads, though it is incremental as it applies existing CNN methods to a new dataset.
The paper tackled the problem of recognizing emotions in advertisements by compiling a dataset with expert and annotator opinions, showing that CNN features outperform traditional audio-visual descriptors, and demonstrated that improved affect prediction enhances computational advertising and viewer experience in a study with 17 users.
Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 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.