What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing

التفاصيل البيبلوغرافية
العنوان: What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
المؤلفون: Li, Irene, Li, Yixin, Li, Tianxiao, Alvarez-Napagao, Sergio, Garcia-Gasulla, Dario, Suzumura, Toyotaro
سنة النشر: 2020
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning
الوصف: 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.
Comment: 7 pages, 7 figures
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2004.10899
رقم الانضمام: edsarx.2004.10899
قاعدة البيانات: arXiv