论文标题
测量Covid-19现实世界中的情绪担忧数据集
Measuring Emotions in the COVID-19 Real World Worry Dataset
论文作者
论文摘要
Covid-19-大流行对世界各地的社会和经济产生了巨大影响。通过各种封锁和社会疏远的衡量标准，大规模了解情感反应变得很重要。在本文中，我们介绍了对Covid-19的情感响应的第一个基础真理数据集。我们要求参与者表明他们的情绪并在文本中表达这些情绪。这导致了现实世界中的忧虑数据集，该数据集的5,000条文本（2,500个短 + 2,500条文本）。我们的分析表明，情感反应与语言措施相关。主题建模进一步表明，英国的人们担心自己的家庭和经济状况。推文大小的文字起着团结的呼吁，而较长的文本则阐明了忧虑和疑虑。使用预测建模方法，我们能够在其实际价值的14％以内参与者的情感响应近似。我们鼓励其他人使用数据集并改善如何使用自动化方法来了解情绪反应并担心紧急问题。
The COVID-19 pandemic is having a dramatic impact on societies and economies around the world. With various measures of lockdowns and social distancing in place, it becomes important to understand emotional responses on a large scale. In this paper, we present the first ground truth dataset of emotional responses to COVID-19. We asked participants to indicate their emotions and express these in text. This resulted in the Real World Worry Dataset of 5,000 texts (2,500 short + 2,500 long texts). Our analyses suggest that emotional responses correlated with linguistic measures. Topic modeling further revealed that people in the UK worry about their family and the economic situation. Tweet-sized texts functioned as a call for solidarity, while longer texts shed light on worries and concerns. Using predictive modeling approaches, we were able to approximate the emotional responses of participants from text within 14% of their actual value. We encourage others to use the dataset and improve how we can use automated methods to learn about emotional responses and worries about an urgent problem.
