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dc.contributor.authorWood, Ian
dc.contributor.authorMcCrae, John
dc.contributor.authorAndryushechkin, Vladimir
dc.contributor.authorBuitelaar, Paul
dc.date.accessioned2018-09-20T16:28:35Z
dc.date.available2018-09-20T16:28:35Z
dc.date.issued2018-05-11
dc.identifier.citationWood, Ian; McCrae, John; Andryushechkin, Vladimir; Buitelaar, Paul (2018). A comparison of emotion annotation approaches for text. Information 9 (5),
dc.identifier.issn2078-2489
dc.identifier.urihttp://hdl.handle.net/10379/14449
dc.description.abstractWhile the recognition of positive/negative sentiment in text is an established task with many standard data sets and well developed methodologies, the recognition of a more nuanced affect has received less attention: there are few publicly available annotated resources and there are a number of competing emotion representation schemes with as yet no clear approach to choose between them. To address this lack, we present a series of emotion annotation studies on tweets, providing methods for comparisons between annotation methods (relative vs. absolute) and between different representation schemes. We find improved annotator agreement with a relative annotation scheme (comparisons) on a dimensional emotion model over a categorical annotation scheme on Ekman's six basic emotions; however, when we compare inter-annotator agreement for comparisons with agreement for a rating scale annotation scheme (both with the same dimensional emotion model), we find improved inter-annotator agreement with rating scales, challenging a common belief that relative judgements are more reliable. To support these studies and as a contribution in itself, we further present a publicly available collection of 2019 tweets annotated with scores on each of four emotion dimensions: valence, arousal, dominance and surprise, following the emotion representation model identified by Fontaine et al. in 2007.
dc.publisherMDPI AG
dc.relation.ispartofInformation
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectemotion
dc.subjectannotation
dc.subjectannotator-agreement
dc.subjectsocial-media
dc.subjectaffective-computing
dc.titleA comparison of emotion annotation approaches for text
dc.typeArticle
dc.identifier.doi10.3390/info9050117
dc.local.publishedsourcehttp://www.mdpi.com/2078-2489/9/5/117/pdf
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