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

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2021-09-21
Authors/Contributors
Author: Javadi, Roya
Abstract
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.
Document
Extent
35 pages.
Identifier
etd21669
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Lim, Angelica
Language
English
Member of collection
Download file Size
etd21669.pdf 5.48 MB

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