  Natural disasters (e.g., hurricanes) affect millions of people each year,
causing widespread destruction in their wake. People have recently taken to
social media websites (e.g., Twitter) to share their sentiments and feelings
with the larger community. Consequently, these platforms have become
instrumental in understanding and perceiving emotions at scale. In this paper,
we introduce HurricaneEmo, an emotion dataset of 15,000 English tweets spanning
three hurricanes: Harvey, Irma, and Maria. We present a comprehensive study of
fine-grained emotions and propose classification tasks to discriminate between
coarse-grained emotion groups. Our best BERT model, even after task-guided
pre-training which leverages unlabeled Twitter data, achieves only 68% accuracy
(averaged across all groups). HurricaneEmo serves not only as a challenging
benchmark for models but also as a valuable resource for analyzing emotions in
disaster-centric domains.
