Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach.

Clicks: 376
ID: 109587
2020
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Abstract
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.
Reference Key
jelodar2020deepieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jelodar, Hamed;Wang, Yongli;Orji, Rita;Huang, Hucheng;
Journal ieee journal of biomedical and health informatics
Year 2020
DOI
10.1109/JBHI.2020.3001216
URL
Keywords
electronic health records public health natural language processing informatics response covid-19 emergent disease medication information

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