English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The portrayal of negative emotions such as anger can vary widely betweencultures and contexts, depending on the acceptability of expressing full-blownemotions rather than suppression to maintain harmony. The majority of emotionaldatasets collect data under the broad label ``anger", but social signals canrange from annoyed, contemptuous, angry, furious, hateful, and more. In thiswork, we curated the first in-the-wild multicultural video dataset of emotions,and deeply explored anger-related emotional expressions by askingculture-fluent annotators to label the videos with 6 labels and 13 emojis in amulti-label framework. We provide a baseline multi-label classifier on ourdataset, and show how emojis can be effectively used as a language-agnostictool for annotation.

The Many Faces of Anger: A Multicultural Video Dataset of Negative Emotions in the Wild (MFA-Wild)