The Super Emotion Dataset

Abstract:Despite the wide-scale usage and development of emotion classification datasets in NLP, the field lacks a standardized, large-scale resource that follows a psychologically grounded taxonomy. Existing datasets either use inconsistent emotion categories, suffer from limited sample size, or focus on specific domains. The SuperEmotion dataset addresses this gap by harmonizing diverse text sources into a unified framework based on Shaver's empirically validated emotion taxonomy, enabling more consistent cross-domain emotion recognition research. This report describes version 2 of the dataset. We also report baseline results for a fine-tuned ModernBERT encoder.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2505.15348 [cs.CL]

(or arXiv:2505.15348v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2505.15348 Focus to learn more arXiv-issued DOI via DataCite

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