Abstract
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.
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