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Experiencers, Stimuli, or Targets : Which Semantic Roles Enable Machine Learning to Infer the Emotions?
Oberländer, Laura; Reich, Kevin; Klinger, Roman (2020): Experiencers, Stimuli, or Targets : Which Semantic Roles Enable Machine Learning to Infer the Emotions?, in: Malvina Nissim, Viviana Patti, Barbara Plank, u. a. (Hrsg.), Proceedings of the Third Workshop on Computational Modeling of People’s Opinions, Personality, and Emotion’s in Social Media, Association for Computational Linguistics, S. 119–128.
Faculty/Chair:
Author:
Title of the compilation:
Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media
Editors:
Nissim, Malvina
Patti, Viviana
Plank, Barbara
Durmus, Esin
Conference:
Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media ; Barcelona, Spain (Online)
Publisher Information:
Year of publication:
2020
Pages:
Language:
English
Abstract:
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.
GND Keywords: ; ;
Computerlinguistik
Maschinelles Lernen
Emotion
Keywords:
Machine Learning
DDC Classification:
RVK Classification:
Peer Reviewed:
Yes:
International Distribution:
Yes:
Open Access Journal:
Yes:
Type:
Conferenceobject
Activation date:
March 14, 2024
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https://fis.uni-bamberg.de/handle/uniba/93900