Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus

Hendrik Schuff, Jeremy Barnes, Julian Mohme, Sebastian Padó, Roman Klinger


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.
Anthology ID:
W17-5203
Volume:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13–23
Language:
URL:
https://aclanthology.org/W17-5203
DOI:
10.18653/v1/W17-5203
Bibkey:
Cite (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.
Cite (Informal):
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus (Schuff et al., WASSA 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5203.pdf
Presentation:
 W17-5203.Presentation.pdf