Appraisal Theories for Emotion Classification in Text
Table 4: Classifier performance on predicting appraisal dimensions.
['Appraisal', 'T→A P', 'T→A R', 'T→A F1']
Experiment 1 (Appraisal Prediction, T→A) aims at understanding how well appraisal dimensions can be predicted from text. Emotion classification is an established task, and one might have some intuition on the expected performance with a given data set, but the prediction of appraisal dimensions has never been performed before. The prediction of certainty works best (85%F1) followed by attention (82%F1). The lowest performance is seen for control (55%F1) and circumstance (61%F1). These results are only partially in line with the inter-annotator agreement scores. We obtain a 75 micro average F1 score.