  Understanding emotion expressed in language has a wide range of applications,
from building empathetic chatbots to detecting harmful online behavior.
Advancement in this area can be improved using large-scale datasets with a
fine-grained typology, adaptable to multiple downstream tasks. We introduce
GoEmotions, the largest manually annotated dataset of 58k English Reddit
comments, labeled for 27 emotion categories or Neutral. We demonstrate the high
quality of the annotations via Principal Preserved Component Analysis. We
conduct transfer learning experiments with existing emotion benchmarks to show
that our dataset generalizes well to other domains and different emotion
taxonomies. Our BERT-based model achieves an average F1-score of .46 across our
proposed taxonomy, leaving much room for improvement.
