  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.
