PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry
This work addresses the need for more nuanced emotion analysis in literature, specifically for poetry readers and researchers, though it is incremental in applying existing methods to a new domain.
The authors tackled the problem of analyzing complex aesthetic emotions in poetry, focusing on reader-elicited rather than expressed emotions, and achieved an expert annotation agreement of kappa = 0.70 and up to 0.52 F1-micro in classification experiments on German poetry.
Most approaches to emotion analysis of social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik. However, art (such as literature) enables engagement in a broader range of more complex and subtle emotions. These have been shown to also include mixed emotional responses. We consider emotions in poetry as they are elicited in the reader, rather than what is expressed in the text or intended by the author. Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within their context. We evaluate this novel setting in an annotation experiment both with carefully trained experts and via crowdsourcing. Our annotation with experts leads to an acceptable agreement of kappa = .70, resulting in a consistent dataset for future large scale analysis. Finally, we conduct first emotion classification experiments based on BERT, showing that identifying aesthetic emotions is challenging in our data, with up to .52 F1-micro on the German subset. Data and resources are available at https://github.com/tnhaider/poetry-emotion