author = {Enrica Troiano and Laura Oberl\"ander and Roman
  title = {Dimensional Modeling of Emotions in Text with
                  Appraisal Theories: Corpus Creation, Annotation
                  Reliability, and Prediction},
  journal = {Computational Linguistics},
  number = 1,
  volume = 49,
  month = mar,
  year = 2023,
  address = {Cambridge, MA},
  publisher = {MIT Press},
  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.},
  doi = {10.1162/coli_a_00461},
  url = {https://doi.org/10.1162/coli_a_00461},
  internaltype = {journal}