{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-many-faces-of-anger-a-multicultural-video","title":"The Many Faces of Anger: A Multicultural Video Dataset of Negative Emotions in the Wild (MFA-Wild)","arxiv_id":"2112.05267","date":"2021-12-10","proceeding":null,"authors":["Roya Javadi","Angelica Lim"],"abstract":"The portrayal of negative emotions such as anger can vary widely between cultures and contexts, 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, hateful, and more. In this work, 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.","url_abs":"https://arxiv.org/abs/2112.05267v1","url_pdf":"https://arxiv.org/pdf/2112.05267v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-many-faces-of-anger-a-multicultural-video","repo_url":"https://github.com/rjavadi/social-signal-project","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"culture","task_name":"Cultural Vocal Bursts Intensity Prediction"},{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"}],"methods":[],"datasets_introduced":[{"slug":"mfa","name":"MFA","full_name":"Many Faces of Anger"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-classification-on-mfa","task":"Emotion Classification","dataset":"MFA","model":"MLKNN","rank_in_archive_order":1,"of":2,"metrics":{"F-F1 score (Comb.)":"0.34","F-F1 score (NA)":"0.42","F-F1 score (Persian)":"0.4","V-F1 score (Comb.)":"0.39","V-F1 score (NA)":"0.42","V-F1 score (Persian)":"0.40"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-classification-on-mfa","task":"Emotion Classification","dataset":"MFA","model":"CC - XGB","rank_in_archive_order":2,"of":2,"metrics":{"F-F1 score (Comb.)":"0.33","F-F1 score (NA)":"0.42","F-F1 score (Persian)":"0.28","V-F1 score (Comb.)":"0.36","V-F1 score (NA)":"0.4","V-F1 score (Persian)":"0.33"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}