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Emotion lexica are commonly used resources to combat data poverty inautomatic emotion detection. However, vocabulary coverage issues, differencesin construction method and discrepancies in emotion framework andrepresentation result in a heterogeneous landscape of emotion detectionresources, calling for a unified approach to utilising them. To combat this, wepresent an extended emotion lexicon of 30,273 unique entries, which is a resultof merging eight existing emotion lexica by means of a multi-view variationalautoencoder (VAE). We showed that a VAE is a valid approach for combininglexica with different label spaces into a joint emotion label space with achosen number of dimensions, and that these dimensions are still interpretable.We tested the utility of the unified VAE lexicon by employing the lexiconvalues as features in an emotion detection model. We found that the VAE lexiconoutperformed individual lexica, but contrary to our expectations, it did notoutperform a naive concatenation of lexica, although it did contribute to thenaive concatenation when added as an extra lexicon. Furthermore, using lexiconinformation as additional features on top of state-of-the-art language modelsusually resulted in a better performance than when no lexicon information wasused.

Joint Emotion Label Space Modelling for Affect Lexica