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Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus
Schuff, Hendrik; Barnes, Jeremy; Mohme, Julian; u. a. (2017): Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus, in: Alexandra Balahur, Saif M. Mohammad, Erik van der Goot, u. a. (Hrsg.), Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Association for Computational Linguistics, S. 13–23, doi: 10.18653/v1/W17-5203.
Faculty/Chair:
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Title of the compilation:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
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Conference:
8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis ; Copenhagen, Denmark
Publisher Information:
Year of publication:
2017
Pages:
Language:
English
DOI:
Abstract:
There is a rich variety of data sets for sentiment analysis (viz., polarity and subjectivity classification). For the more challenging task of detecting discrete emotions following the definitions of Ekman and Plutchik, however, there are much fewer data sets, and notably no resources for the social media domain. This paper contributes to closing this gap by extending the SemEval 2016 stance and sentiment datasetwith emotion annotation. We (a) analyse annotation reliability and annotation merging; (b) investigate the relation between emotion annotation and the other annotation layers (stance, sentiment); (c) report modelling results as a baseline for future work.
GND Keywords: ;
Computerlinguistik
Emotion
Keywords:
Fine-Grained Emotions
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Peer Reviewed:
Yes:
International Distribution:
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Open Access Journal:
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Type:
Conferenceobject
Activation date:
March 13, 2024
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https://fis.uni-bamberg.de/handle/uniba/93976