Emotion recognition is predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory (e.g., fear, joy, anger, disgust, sadness, surprise, trust, anticipation). More recently, semantic role labeling approaches have been developed to extract structures from the text to answer questions like: "who is described to feel the emotion?" (experiencer), "what causes this emotion?" (stimulus), and at which entity is it directed?" (target). Though it has been shown that jointly modeling stimulus and emotion category prediction is beneficial for both subtasks, it remains unclear which of these semantic roles enables a classifier to infer the emotion. Is it the experiencer, because the identity of a person is biased towards a particular emotion (X is always happy)? Is it a particular target (everybody loves X) or a stimulus (doing X makes everybody sad)? We answer these questions by training emotion classification models on five available datasets annotated with at least one semantic role by masking the fillers of these roles in the text in a controlled manner and find that across multiple corpora, stimuli and targets carry emotion information, while the experiencer might be considered a confounder. Further, we analyze if informing the model about the position of the role improves the classification decision. Particularly on literature corpora we find that the role information improves the emotion classification.

该研究探讨了情感识别中的文本分类、语义角色标注等问题，结果表明在多种语料库中，刺激物和目标是情感识别的有效信息，而体验者则可能会被视为混淆因素。此外，研究发现在文学语料库中，角色信息可以提高情感分类的准确性。

体验者、刺激物还是目标：哪些语义角色能使机器学习推断情感？