that has annotations for various emotions evoked in the observer. Alameda-Pineda et al. [5] introduced an approach to
automatically recognize the emotion elicited by abstract paintings using the MART dataset [128], a collection of 500
abstract paintings labelled as evoking positive or negative sentiment. Most recently, Achlioptas et al. [3] introduced
ArtEmis, a large-scale dataset of emotional reactions to visual artwork joined with explanations of these emotions in
language, and developed machine learning models for dominant emotion prediction from images or text. Developing
computational approaches for quantifying and predicting values of concepts such as aesthetics or sentiment is especially
difﬁcult for art images. The importance and originality, and therefore also our perception, of a particular artwork do not
only emerge from its visual properties, but greatly depend on the art historical context. For that reason, it is obvious
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