Context Unlocks Emotions: Text-based Emotion Classification Dataset
  Auditing with Large Language Models
Daniel Yang Aditya Kommineni Mohammad Alshehri Nilamadhab Mohanty Vedant Modi Jonathan Gratch Shrikanth Narayanan
The lack of contextual information in text data can make the annotation
process of text-based emotion classification datasets challenging. As a result,
such datasets often contain labels that fail to consider all the relevant
emotions in the vocabulary. This misalignment between text inputs and labels
can degrade the performance of machine learning models trained on top of them.
As re-annotating entire datasets is a costly and time-consuming task that
cannot be done at scale, we propose to use the expressive capabilities of large
language models to synthesize additional context for input text to increase its
alignment with the annotated emotional labels. In this work, we propose a
formal definition of textual context to motivate a prompting strategy to
enhance such contextual information. We provide both human and empirical
evaluation to demonstrate the efficacy of the enhanced context. Our method
improves alignment between inputs and their human-annotated labels from both an
empirical and human-evaluated standpoint.