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dc.contributor.authorWood, Ian D.
dc.contributor.authorMcCrae, John P.
dc.contributor.authorAndryushechkin, Vladimir
dc.contributor.authorBuitelaar, Paul
dc.date.accessioned2018-07-27T11:29:44Z
dc.date.available2018-07-27T11:29:44Z
dc.date.issued2018-05-11
dc.identifier.citationWood, Ian D. , McCrae, John P., Andryushechkin, Vladimir , & Buitelaar, Paul. (2018). A Comparison of Emotion Annotation Approaches for Text. Information, 9(5), 117. doi: 10.3390/info9050117en_IE
dc.identifier.issn2078-2489
dc.identifier.urihttp://hdl.handle.net/10379/7432
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 ï¬ nd improved annotator agreement with a relative annotation scheme (comparisons) on a dimensional emotion model over a categorical annotation scheme on Ekmanâ 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 ï¬ nd 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 identiï¬ ed by Fontaine et al. in 2007.en_IE
dc.description.sponsorshipWe would like to thank volunteers from the Insight Centre for Data Analytics for their efforts in pilot study annotations. This work was supported in part by the Science Foundation Ireland under Grant Number 16/IFB/4336 and Grant Number SFI/12/RC/2289 (Insight). The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant agreements No. 644632 (MixedEmotions).en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherMDPIen_IE
dc.relation.ispartofInformationen
dc.subjectEmotionen_IE
dc.subjectAnnotationen_IE
dc.subjectAnnotator-agreementen_IE
dc.subjectSocial-mediaen_IE
dc.subjectAffective-computingen_IE
dc.titleA comparison of emotion annotation approaches for texten_IE
dc.typeArticleen_IE
dc.date.updated2018-06-29T09:50:37Z
dc.identifier.doi10.3390/info9050117
dc.local.publishedsourcehttps://dx.doi.org/10.3390/info9050117en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.contributor.funderHorizon 2020en_IE
dc.internal.rssid14558721
dc.local.contactIan Wood. Email: ian.wood@nuigalway.ie
dc.local.copyrightcheckedNo
dc.local.versionPUBLISHED
dcterms.projectinfo:eu-repo/grantAgreement/EC/H2020::IA/644632/EU/Social Semantic Emotion Analysis for Innovative Multilingual Big Data Analytics Markets/MixedEmotionsen_IE
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/
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