Human emotions are increasingly understood to be a crucial aspect in human-machine interactive systems. Especially for non-expert end users, reactions to complex intelligent systems resemble social interactions, involving feelings such as frustration, impatience, or helplessness if things go wrong. Furthermore, technology is increasingly used to observe human-to-human interactions, such as customer frustration monitoring in call center applications. Dealing with these kinds of states in technological systems requires a suitable representation, which should make the concepts and descriptions developed in the affective sciences available for use in technological contexts.
This adds dimensions to such functions as Google’s Knowledge Graph and other data maps, as well as interpreting facial expressions in video and voice inflections in audio. Turning emotions into data points is an incredible step forward.