Predicting audience responses to movie content from electro-dermal activity signals

Abstract

The ability to assess fine-scale user responses has applications in advertising, content creation, recommendation, and psychology research. Unfortunately, current approaches, such as focus groups and audience surveys, are limited in size and scope. In this paper, we propose a combined biometric sensing and analysis methodology to leverage audience-scale electro-dermal activity (EDA) data for the purpose of evaluating user responses to video. We provide detailed characterization of how temporal physiological responses to video stimulus can be modeled, along with first-of-its-kind audience-scale EDA group experiments in uncontrolled real-world environments. Our study provides insights into the techniques used to analyze EDA, the effectiveness of the different temporal features, and group dynamics of audiences. Our experiments demonstrate the ability to classify movie ratings with accuracy of over 70% on specific films. Results of this study suggest the ability to assess emotional reactions of groups using minimally invasive sensing modalities in uncontrolled environments.

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