Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets
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Computer Science > Computation and Language
arXiv:2610.12841 (cs)
[Submitted on 9 Oct 2026]
Title:Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets
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Abstract:Emotion classifiers are routinely applied beyond their training data, on the assumption that same-named labels such as fear or surprise mean the same thing across resources. We test this assumption for five labels shared by GoEmotions, XED, and BRIGHTER, comparing models on identical inputs and under cross-corpus transfer. On the same texts, one fear classifier flags 71% as fearful and another 8%, yet the two largely agree on how the texts rank. Across 30 resource-label comparisons, training-label prevalence explains about half of this kind of scale mismatch, and a fitted intercept removes most of it. What recalibration cannot repair is larger: transferred models lose 55% of native discrimination in aggregate, agreement in ranking between classifiers is only weakly related to that loss, and for surprise the resources disagree about which texts express the emotion. The choice of training resource can therefore change conclusions: on two external tweet collections, BRIGHTER- and XED-trained classifiers give opposite answers about which shows more surprise, even after rank normalization, with intervals excluding zero in both directions. A shared emotion label name does not establish an interchangeable measurement target.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.12841 [cs.CL] |
| (or arXiv:2610.12841v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12841
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets, by Emily \"Ohman and Anna Koufakou
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