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

Detecting Perceived Emotions in Hurricane Disasters