Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 24, Issue 10, Pages 2733-2742Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2020.3001216
Keywords
Semantics; Natural language processing; Social networking (online); Viruses (medical); Computational modeling; COVID-19; Coronavirus; COVID-19; Natural Language Processing; Topic modeling; Deep Learning
Categories
Funding
- National Natural Science Foundation of China [61941113, 81674099, 61502233]
- Fundamental Research Fund for the Central Universities [30918015103, 30918012204]
- Nanjing Science and Technology Development Plan [201805036]
- 13th Five-Year equipment field fund [61403120501]
- China Academy of Engineering Consulting Research [2019-ZD-1-02-02]
- National Social Science Foundation [18BTQ073]
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Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% - a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.
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