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Information 2018, 9(5), 117; https://doi.org/10.3390/info9050117

A Comparison of Emotion Annotation Approaches for Text

1
Insight Centre for Data Analytics, National University of Ireland Galway, Galway H91 AEX4, Ireland
2
Aylien Ltd., Dublin D02 RH68, Irenland
*
Author to whom correspondence should be addressed.
Received: 27 March 2018 / Revised: 25 April 2018 / Accepted: 9 May 2018 / Published: 11 May 2018
(This article belongs to the Special Issue Love & Hate in the Time of Social Media and Social Networks)
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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. View Full-Text
Keywords: emotion; annotation; annotator-agreement; social-media; affective-computing emotion; annotation; annotator-agreement; social-media; affective-computing
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Wood, I.D.; McCrae, J.P.; Andryushechkin, V.; Buitelaar, P. A Comparison of Emotion Annotation Approaches for Text. Information 2018, 9, 117.

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