Understanding Emotional Responses to Mobile Video Advertisements via Physiological Signal Sensing and Facial Expression Analysis

Abstract

Understanding a target audience's emotional responses to video advertisements is crucial to stakeholders. However, traditional methods for collecting such information are slow, expensive, and coarse-grained. We propose AttentiveVideo, an intelligent mobile interface with corresponding inference algorithms to monitor and quantify the effects of mobile video advertising. AttentiveVideo employs a combination of implicit photoplethysmography (PPG) sensing and facial expression analysis (FEA) to predict viewers' attention, engagement, and sentiment when watching video advertisements on unmodified smartphones. In a 24-participant study, we found that AttentiveVideo achieved good accuracies on a wide range of emotional measures (the best average accuracy = 73.59%, kappa = 0.46 across 9 metrics). We also found that the PPG sensing channel and the FEA technique are complimentary. While FEA works better for strong emotions (e.g., joy and anger), the PPG channel is more informative for subtle responses or emotions. These findings show the potential for both low-cost collection and deep understanding of emotional responses to mobile video advertisements.