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Human emotion is expressed in many communication modalities and media formatsand so their computational study is equally diversified into natural languageprocessing, audio signal analysis, computer vision, etc. Similarly, the largevariety of representation formats used in previous research to describeemotions (polarity scales, basic emotion categories, dimensional approaches,appraisal theory, etc.) have led to an ever proliferating diversity ofdatasets, predictive models, and software tools for emotion analysis. Becauseof these two distinct types of heterogeneity, at the expressional andrepresentational level, there is a dire need to unify previous work onincreasingly diverging data and label types. This article presents such aunifying computational model. We propose a training procedure that learns ashared latent representation for emotions, so-called emotion embeddings,independent of different natural languages, communication modalities, media orrepresentation label formats, and even disparate model architectures.Experiments on a wide range of heterogeneous affective datasets indicate thatthis approach yields the desired interoperability for the sake of reusability,interpretability and flexibility, without penalizing prediction quality. Codeand data are archived under https://doi.org/10.5281/zenodo.7405327 .

Emotion Embeddings $\unicode{x2014}$ Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets