Abstract
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 an approach to find out the reasons that are causing sadness and fear, and study the emotion trend in both keyword and topic level.
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Notes
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https://github.com/huggingface/transformers: bert-base-multilingual-cased model.
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Visualization tool: https://wordart.com/. Invalid for a few languages.
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Li, I., Li, Y., Li, T., Alvarez-Napagao, S., Garcia-Gasulla, D., Suzumura, T. (2020). What Are We Depressed About When We Talk About COVID-19: Mental Health Analysis on Tweets Using Natural Language Processing. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVII. SGAI 2020. Lecture Notes in Computer Science(), vol 12498. Springer, Cham. https://doi.org/10.1007/978-3-030-63799-6_27
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