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        <title>Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus</title>
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        <namePart type="given">Hendrik</namePart>
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    <name type="personal">
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            <title>Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</title>
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            <namePart type="given">Alexandra</namePart>
            <namePart type="family">Balahur</namePart>
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        <name type="personal">
            <namePart type="given">Saif</namePart>
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            <namePart type="family">Mohammad</namePart>
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        <name type="personal">
            <namePart type="given">Erik</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus
%A Schuff, Hendrik
%A Barnes, Jeremy
%A Mohme, Julian
%A Padó, Sebastian
%A Klinger, Roman
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y van der Goot, Erik
%S Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F schuff-etal-2017-annotation
%X 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.
%R 10.18653/v1/W17-5203
%U https://aclanthology.org/W17-5203
%U https://doi.org/10.18653/v1/W17-5203
%P 13-23

Markdown (Informal)

[Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus](https://aclanthology.org/W17-5203) (Schuff et al., WASSA 2017)

• Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus (Schuff et al., WASSA 2017)

ACL

• Hendrik Schuff, Jeremy Barnes, Julian Mohme, Sebastian Padó, and Roman Klinger. 2017. Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 13–23, Copenhagen, Denmark. Association for Computational Linguistics.