A comparison of emotion annotation approaches for text

Ian D. Wood, John P. McCrae, Vladimir Andryushechkin, Paul Buitelaar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.
Original languageEnglish
Article number117
Pages (from-to)1-13
Number of pages13
JournalInformation (Switzerland)
Volume9
Issue number5
Early online date11 May 2018
DOIs
Publication statusPublished - May 2018
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2018. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • emotion
  • annotation
  • annotator-agreement
  • social-media
  • affective-computing

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