The many faces of anger: A multicultural video dataset of negative emotions in the wild

The portrayal of negative emotions such as anger can vary widely between cultures and situations, depending on the acceptability of expressing full-blown emotions rather than suppression to maintain harmony. The majority of emotional datasets collect data under the broad label "anger", but social signals can range from annoyed, contemptuous, angry, furious, hatred, and more. In this thesis, we curated the first in-the-wild multicultural video dataset of emotions, and deeply explored anger-related emotional expressions by asking culture-fluent annotators to label the videos with 6 labels and 13 emojis in a multi-label framework. We provide a baseline multi-label classifier on our dataset, and show how emojis can be effectively used as a language-agnostic tool for annotation.