dc.contributor.author | Wood, Ian | |
dc.contributor.author | McCrae, John | |
dc.contributor.author | Andryushechkin, Vladimir | |
dc.contributor.author | Buitelaar, Paul | |
dc.date.accessioned | 2018-09-20T16:28:35Z | |
dc.date.available | 2018-09-20T16:28:35Z | |
dc.date.issued | 2018-05-11 | |
dc.identifier.citation | Wood, Ian; McCrae, John; Andryushechkin, Vladimir; Buitelaar, Paul (2018). A comparison of emotion annotation approaches for text. Information 9 (5), | |
dc.identifier.issn | 2078-2489 | |
dc.identifier.uri | http://hdl.handle.net/10379/14449 | |
dc.description.abstract | While 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.publisher | MDPI AG | |
dc.relation.ispartof | Information | |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Ireland | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/ie/ | |
dc.subject | emotion | |
dc.subject | annotation | |
dc.subject | annotator-agreement | |
dc.subject | social-media | |
dc.subject | affective-computing | |
dc.title | A comparison of emotion annotation approaches for text | |
dc.type | Article | |
dc.identifier.doi | 10.3390/info9050117 | |
dc.local.publishedsource | http://www.mdpi.com/2078-2489/9/5/117/pdf | |
nui.item.downloads | 0 | |