论文标题
发现飓风灾难中感知的情绪
Detecting Perceived Emotions in Hurricane Disasters
论文作者
论文摘要
自然灾害（例如飓风）每年会影响数百万人，从而造成广泛的破坏。人们最近将社交媒体网站（例如Twitter）带到了与更大的社区分享他们的情感和感受。因此，这些平台已经在大规模理解和感知情绪方面起到了作用。在本文中，我们介绍了飓风，这是一个跨越三个飓风的15,000条英语推文的情感数据集：哈维，艾尔玛和玛丽亚。我们介绍了一项关于细粒情绪的全面研究，并提出了分类任务，以区分粗粒度的情绪群体。我们最好的BERT模型，即使在任务指导的预培训中利用未标记的Twitter数据也只能达到68％的精度（所有组的平均）。飓风不仅是模型的具有挑战性的基准，而且还可以作为分析以灾难为中心领域的情绪的宝贵资源。
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.
