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x-enVENT : A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations
Troiano, Enrica; Oberlaender, Laura Ana Maria; Wegge, Maximilian; u. a. (2022): x-enVENT : A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations, in: Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, u. a. (Hrsg.), Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille: European Language Resources Association, S. 1365–1375.
Faculty/Chair:
Title of the compilation:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Editors:
Calzolari, Nicoletta
Béchet, Frédéric
Blache, Philippe
Choukri, Khalid
Cieri, Christopher
Declerck, Thierry
Goggi, Sara
Isahara, Hitoshi
Maegaard, Bente
Mariani, Joseph
Mazo, Hélène
Odijk, Jan
Piperidis, Stelios
Conference:
Thirteenth Language Resources and Evaluation Conference (LREC), Juni 2022 ; Marseille
Publisher Information:
Year of publication:
2022
Pages:
Language:
English
Abstract:
Emotion classification is often formulated as the task to categorize texts into a predefined set of emotion classes. So far, this task has been the recognition of the emotion of writers and readers, as well as that of entities mentioned in the text. We argue that a classification setup for emotion analysis should be performed in an integrated manner, including the different semantic roles that participate in an emotion episode. Based on appraisal theories in psychology, which treat emotions as reactions to events, we compile an English corpus of written event descriptions. The descriptions depict emotion-eliciting circumstances, and they contain mentions of people who responded emotionally. We annotate all experiencers, including the original author, with the emotions they likely felt. In addition, we link them to the event they found salient (which can be different for different experiencers in a text) by annotating event properties, or appraisals (e.g., the perceived event undesirability, the uncertainty of its outcome). Our analysis reveals patterns in the co-occurrence of people’s emotions in interaction. Hence, this richly-annotated resource provides useful data to study emotions and event evaluations from the perspective of different roles, and it enables the development of experiencer-specific emotion and appraisal classification systems.
GND Keywords: ; ; ;
Emotionales Verhalten
Korpus <Linguistik>
Affective Computing
Bewertungstheorie
Keywords: ; ; ; ; ; ;
emotion analysis
corpus
affective computing
role labeling
emotion experiencer
appraisal theories
events
DDC Classification:
RVK Classification:
Peer Reviewed:
Yes:
International Distribution:
Yes:
Open Access Journal:
Yes:
Type:
Conferenceobject
Activation date:
March 7, 2024
Versioning
Question on publication
Permalink
https://fis.uni-bamberg.de/handle/uniba/93887