Evaluating Content-centric vs User-centric Ad Affect Recognition

TL;DR

This study compares content-centric and user-centric affect recognition methods for advertisements, demonstrating that EEG signals better encode emotions than content features and exploring their impact on real-world advertising experiences.

Contribution

It is the first to explicitly compare user-centered and content-centered ad affect recognition and analyze their effects on advertising effectiveness.

Findings

CNN features outperform low-level emotion descriptors

EEG signals encode emotional information better than content features

Objective affect recognition correlates with subjective viewer experience

Abstract

Despite the fact that advertisements (ads) often include strongly emotional content, very little work has been devoted to affect recognition (AR) from ads. This work explicitly compares content-centric and user-centric ad AR methodologies, and evaluates the impact of enhanced AR on computational advertising via a user study. Specifically, we (1) compile an affective ad dataset capable of evoking coherent emotions across users; (2) explore the efficacy of content-centric convolutional neural network (CNN) features for encoding emotions, and show that CNN features outperform low-level emotion descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram (EEG) responses acquired from eleven viewers, and find that EEG signals encode emotional information better than content descriptors; (4) investigate the relationship between objective AR and subjective viewer experience while watching an ad-embedded online video stream based on a study involving 12 users. To our knowledge, this is the first work to (a) expressly compare user vs content-centered AR for ads, and (b) study the relationship between modeling of ad emotions and its impact on a real-life advertising application.