A hierarchical static-dynamic framework for emotion classification
The goal of emotion classification is to estimate an emotion label, given representative data and discriminative features. Humans are very good at deriving high-level representations of emotion state and integrating this information over time to arrive at a final judgment. However, currently, most emotion classification algorithms do not use this technique. This paper presents a hierarchical static-dynamic emotion classification framework that estimates high-level emotional judgments and locally integrates this information over time to arrive at a final estimate of the affective label. The results suggest that this framework for emotion classification leads to more accurate results than either purely static or purely dynamic strategies.