DynAMoS: The Dynamic Affective Movie Clip Database for Subjectivity Analysis

In this paper, we describe the design, collection, and validation of a new video database that includes holistic and dynamic emotion ratings from 83 participants watching 22 affective movie clips. In contrast to previous work in Affective Computing, which pursued a single “ground truth” label for the affective content of each moment of each video (e.g., by averaging the ratings of 2 to 7 trained participants), we embrace the subjectivity inherent to emotional experiences and provide the full distribution of all participants’ ratings (with an average of 76.7 raters per video).

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