Description:
We present a quantitative study of textually encoded emotions in a core set of the Grimms’ Children’s and Household Tales. As a contribution to Computational Literary Studies, we publish (a) a fairy tale corpus (ChildTale-A) with more than 5,000 manually annotated sentences and introduce (b) four aggregated measures for the analysis of textually encoded emotions (Average Valence, Emotional Potential, Emotional Arc, and Emotion Profile), with which we (c) analyze the corpus with regard to the purported cruelty vs. optimism of fairy tales. On average, the fairy tales contain more than 50 % emotional sentences without clear negative sentiment, while emotion trajectory patterns vary. Together, these findings underscore the role of emotions as plot-driving elements in fairy tales as a highly schematized historical genre.