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Most approaches to emotion analysis of social media, literature, news, andother domains focus exclusively on basic emotion categories as defined by Ekmanor Plutchik. However, art (such as literature) enables engagement in a broaderrange of more complex and subtle emotions. These have been shown to alsoinclude mixed emotional responses. We consider emotions in poetry as they areelicited in the reader, rather than what is expressed in the text or intendedby the author. Thus, we conceptualize a set of aesthetic emotions that arepredictive of aesthetic appreciation in the reader, and allow the annotation ofmultiple labels per line to capture mixed emotions within their context. Weevaluate this novel setting in an annotation experiment both with carefullytrained experts and via crowdsourcing. Our annotation with experts leads to anacceptable agreement of kappa = .70, resulting in a consistent dataset forfuture large scale analysis. Finally, we conduct first emotion classificationexperiments based on BERT, showing that identifying aesthetic emotions ischallenging in our data, with up to .52 F1-micro on the German subset. Data andresources are available at https://github.com/tnhaider/poetry-emotion

PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry