Computational emotion classification for genre corpora of German tragedies and comedies from 17th to early 19th century

Abstract

Abstract This article presents a method of emotion analysis for German drama from the 17th to the 19th century that significantly goes beyond previous research approaches in computational literary studies. It is based on annotations of 17 dramatic texts resulting in 11,939 annotations which were used as training material to fine-tune a German language BERT model that achieves an average accuracy of 73% for the single-label emotion classification of fourteen emotion types in cross-validation. We apply the emotion classification on a corpus of 141 comedies and 92 tragedies to compare these genres. For tragedies, the mean proportion percentages of ‘suffering’ and ‘abhorrence’ are higher than for comedies. Inversely, mean percentages of ‘anger’ and ‘joy’ are higher for comedies than for tragedies. A new finding is the surprisingly high proportion of ‘anger’ in comedies. Emotion distribution of the last scenes in dramatic texts also proves the quality of the classified data in terms of literary studies. In addition, the emotion distribution for several subgenres of comedy is investigated including non-canonical works of wide circulation which reached the recipients directly through the depicted emotions in the Kasperl Plays. Comedies from 1740 to 1770 are characterized by a pairing of higher amounts of ‘friendship’ and ‘love’. Satirical comedies from the same period stand out due to high rates of ‘anger’ as well as ‘suffering’. The very successful Kasperl plays turn out to be characterized by a comparatively large percentage of ‘schadenfreude’ and ‘joy’.