论文标题
当我们谈论covid19：使用自然语言处理的推文时，我们感到沮丧
What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
论文作者
论文摘要
2019年冠状病毒病（Covid-19）的爆发最近在很大程度上影响了人类的生活。除了直接的身体和经济威胁外，大流行还间接影响人们的心理健康状况，这可能是压倒性但难以衡量的。问题可能来自各种原因，例如失业状况，全职政策，对病毒的恐惧等。在这项工作中，我们专注于应用自然语言处理（NLP）技术来分析心理健康的推文。我们训练了将每条推文分类为以下情绪的深层模型：愤怒，期待，厌恶，恐惧，喜悦，悲伤，惊喜和信任。我们通过手动标记1,000个英语推文来构建EMOCT（Emotion-covid19-Tweet）数据集，以供培训目的。此外，我们提出并比较两种方法来找出引起悲伤和恐惧的原因。
The outbreak of coronavirus disease 2019 (COVID-19) recently has affected human life to a great extent. Besides direct physical and economic threats, the pandemic also indirectly impact people's mental health conditions, which can be overwhelming but difficult to measure. The problem may come from various reasons such as unemployment status, stay-at-home policy, fear for the virus, and so forth. In this work, we focus on applying natural language processing (NLP) techniques to analyze tweets in terms of mental health. We trained deep models that classify each tweet into the following emotions: anger, anticipation, disgust, fear, joy, sadness, surprise and trust. We build the EmoCT (Emotion-Covid19-Tweet) dataset for the training purpose by manually labeling 1,000 English tweets. Furthermore, we propose and compare two methods to find out the reasons that are causing sadness and fear.
