A comparison of emotion annotation schemes and a new annotated data set

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 more nuanced affect has received less attention, and in particular, there are very few publicly available gold standard annotated resources. To address this lack, we present a series of emotion annotation studies on tweets culminating in a publicly available collection of 2,019 tweets with scores on four emotion dimensions: valence, arousal, dominance and surprise, following the emotion representation model identified by Fontaine et.al. (Fontaine et al., 2007). Further, we make a comparison of relative vs. absolute annotation 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 (Ekman et al., 1987), 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.We 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).non-peer-reviewe