PO-EMO: Conceptualization, Annotation, and Modeling ofAesthetic Emotions in German and English Poetry
Table 7: Recall and precision scores of the best model (dbmdz) for each emotion on the test set. ‘Support’: number of instances with this label.
['Label', 'Precision', 'Recall', 'F1', 'Support']
We find that the multilingual model cannot handle infrequent categories, i.e., Awe/Sublime, Suspense and Humor. However, increasing the dataset with English data improves the results, suggesting that the classification would largely benefit from more annotated data. The best model overall is DBMDZ (.520), showing a balanced response on both validation and test set. Precision is mostly higher than recall. The labels Awe/Sublime, Suspense and Humor are harder to predict than the other labels.