| Abstract | Art is imaginative human creation meant to be appreciated, make people think, and evoke an emotional response. Here for the first time, we create a dataset of more than 4,000 pieces of art (mostly paintings) that has annotations for emotions evoked in the observer. The pieces of art are selected from WikiArt.org's collection for four western styles (Renaissance Art, Post-Renaissance Art, Modern Art, and Contemporary Art). The art is annotated via crowdsourcing for one or more of twenty emotion categories (including neutral). In addition to emotions, the art is also annotated for whether it includes the depiction of a face and how much the observers like the art. The dataset, which we refer to as the {\it WikiArt Emotions Dataset}, can help answer several compelling questions, such as: what makes art evocative, how does art convey different emotions, what attributes of a painting make it well liked, what combinations of categories and emotions evoke strong emotional response, how much does the title of an art impact its emotional response, and what is the extent to which different categories of art evoke consistent emotions in people. We found that fear, happiness, love, and sadness were the dominant emotions that also obtained consistent annotations among the different annotators. We found that the title often impacts the affectual response to art. We show that pieces of art that depict faces draw more consistent emotional responses than those that do not. We also show, for each art category and emotion combination, the average agreements on the emotions evoked and the average art ratings. The WikiArt Emotions dataset also has applications in automatic image processing, as it can be used to develop systems that detect emotions evoked by art, and systems that can transform existing art (or even generate new art) that evokes the desired affectual response. |
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