  Emotion classification in text is typically performed with neural network
models which learn to associate linguistic units with emotions. While this
often leads to good predictive performance, it does only help to a limited
degree to understand how emotions are communicated in various domains. The
emotion component process model (CPM) by Scherer (2005) is an interesting
approach to explain emotion communication. It states that emotions are a
coordinated process of various subcomponents, in reaction to an event, namely
the subjective feeling, the cognitive appraisal, the expression, a
physiological bodily reaction, and a motivational action tendency. We
hypothesize that these components are associated with linguistic realizations:
an emotion can be expressed by describing a physiological bodily reaction ("he
was trembling"), or the expression ("she smiled"), etc. We annotate existing
literature and Twitter emotion corpora with emotion component classes and find
that emotions on Twitter are predominantly expressed by event descriptions or
subjective reports of the feeling, while in literature, authors prefer to
describe what characters do, and leave the interpretation to the reader. We
further include the CPM in a multitask learning model and find that this
supports the emotion categorization. The annotated corpora are available at
https://www.ims.uni-stuttgart.de/data/emotion.
