Beyond emotion archetypes: Databases for emotion modelling using neural networks

There has been rapid development in conceptions of the kind of database that is needed for emotion research. Familiar archetypes are still influential, but the state of the art has moved beyond them. There is concern to capture emotion as it occurs in action and interaction ('pervasive emotion') as well as in short episodes dominated by emotion, and therefore in a range of contexts, which shape the way it is expressed. Context links to modality-different contexts favour different modalities. The strategy of using acted data is not suited to those aims, and has been supplemented by work on both fully natural emotion and emotion induced by various technique that allow more controlled records. Applications for that kind of work go far beyond the 'trouble shooting' that has been the focus for application: 'really natural language processing' is a key goal. The descriptions included in such a database ideally cover quality, emotional content, emotion-related signals and signs, and context. Several schemes are emerging as candidates for describing pervasive emotion. The major contemporary databases are listed, emphasising those which are naturalistic or induced, multimodal, and influential.