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The most prominent tasks in emotion analysis are to assign emotions to textsand to understand how emotions manifest in language. An observation for NLP isthat emotions can be communicated implicitly by referring to events, appealingto an empathetic, intersubjective understanding of events, even withoutexplicitly mentioning an emotion name. In psychology, the class of emotiontheories known as appraisal theories aims at explaining the link between eventsand emotions. Appraisals can be formalized as variables that measure acognitive evaluation by people living through an event that they considerrelevant. They include the assessment if an event is novel, if the personconsiders themselves to be responsible, if it is in line with the own goals,and many others. Such appraisals explain which emotions are developed based onan event, e.g., that a novel situation can induce surprise or one withuncertain consequences could evoke fear. We analyze the suitability ofappraisal theories for emotion analysis in text with the goal of understandingif appraisal concepts can reliably be reconstructed by annotators, if they canbe predicted by text classifiers, and if appraisal concepts help to identifyemotion categories. To achieve that, we compile a corpus by asking people totextually describe events that triggered particular emotions and to disclosetheir appraisals. Then, we ask readers to reconstruct emotions and appraisalsfrom the text. This setup allows us to measure if emotions and appraisals canbe recovered purely from text and provides a human baseline. Our comparison oftext classification methods to human annotators shows that both can reliablydetect emotions and appraisals with similar performance. Therefore, appraisalsconstitute an alternative computational emotion analysis paradigm and furtherimprove the categorization of emotions in text with joint models.

Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction