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…
eess.AS2025
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…
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…