Affect Recognition in Ads with Application to Computational Advertising

TL;DR

This paper presents a new affective ad dataset, evaluates CNN features for emotion recognition, and demonstrates how improved affect prediction enhances computational advertising and user experience.

Contribution

It introduces a novel affective ad dataset, compares CNN features with traditional descriptors, and applies affect modeling to improve computational advertising.

Findings

CNN features outperform low-level descriptors in emotion encoding

Affective ad modeling improves user experience in online video ads

Enhanced affect prediction benefits real-world advertising applications

Abstract

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.