Real-time personalized content catering via viewer sentiment feedback: a QoE perspective

Multimedia content service and delivery have long been plagued by the difficulty in obtaining feedback on users´ true quality of experience. Existing estimation methods do not adequately cover all relevant factors, whereas questionnaires are costly, time-consuming, and impossible to scale. In this work, we present a framework for estimating a viewer´s reactions toward on-screen content in real time by capturing and analyzing his/her facial video, thus allowing up-to-date learning of the viewer´s preferences to occur, enabling the content provider to serve the most desirable and relevant contents and advertisements. Experiments have shown that the proposed sentiment analysis method can predict the viewer´s preferences with good accuracy.