3 papers
cs.AI2026
What You Feel Is Not What They See: On Predicting Self-Reported Emotion from Third-Party Observer Labels
Yara El-Tawil, Aneesha Sampath, Emily Mower Provost
Self-reported emotion labels capture internal experience, while third-party labels reflect external perception. These perspectives often diverge, limiting the applicability of thir…
cs.CL2024
Rethinking Emotion Annotations in the Era of Large Language Models
Minxue Niu, Yara El-Tawil, Amrit Romana +1
Modern affective computing systems rely heavily on datasets with human-annotated emotion labels, for training and evaluation. However, human annotations are expensive to obtain, se…
eess.AS2024
The Whole Is Bigger Than the Sum of Its Parts: Modeling Individual Annotators to Capture Emotional Variability
James Tavernor, Yara El-Tawil, Emily Mower Provost
Emotion expression and perception are nuanced, complex, and highly subjective processes. When multiple annotators label emotional data, the resulting labels contain high variabilit…