  The most prominent tasks in emotion analysis are to assign emotions to texts
and to understand how emotions manifest in language. An observation for NLP is
that emotions can be communicated implicitly by referring to events, appealing
to an empathetic, intersubjective understanding of events, even without
explicitly mentioning an emotion name. In psychology, the class of emotion
theories known as appraisal theories aims at explaining the link between events
and emotions. Appraisals can be formalized as variables that measure a
cognitive evaluation by people living through an event that they consider
relevant. They include the assessment if an event is novel, if the person
considers themselves to be responsible, if it is in line with the own goals,
and many others. Such appraisals explain which emotions are developed based on
an event, e.g., that a novel situation can induce surprise or one with
uncertain consequences could evoke fear. We analyze the suitability of
appraisal theories for emotion analysis in text with the goal of understanding
if appraisal concepts can reliably be reconstructed by annotators, if they can
be predicted by text classifiers, and if appraisal concepts help to identify
emotion categories. To achieve that, we compile a corpus by asking people to
textually describe events that triggered particular emotions and to disclose
their appraisals. Then, we ask readers to reconstruct emotions and appraisals
from the text. This setup allows us to measure if emotions and appraisals can
be recovered purely from text and provides a human baseline. Our comparison of
text classification methods to human annotators shows that both can reliably
detect emotions and appraisals with similar performance. Therefore, appraisals
constitute an alternative computational emotion analysis paradigm and further
improve the categorization of emotions in text with joint models.
