IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v23y2024i04ns0219622022500870.html
   My bibliography  Save this article

A Text Mining Approach to Covid-19 Literature

Author

Listed:
  • Fangyao Liu

    (Key Laboratory of Electronic and Information Engineering, (Southwest Minzu University), State Ethnic Affairs Commission, P. R. China)

  • Daji Ergu

    (Key Laboratory of Electronic and Information Engineering, (Southwest Minzu University), State Ethnic Affairs Commission, P. R. China)

  • Biao Li

    (��College of Business Administration, Southwest University of Finance and Economics, P. R. China)

  • Wei Deng

    (��Department of Computer Science, University of Nebraska at Omaha, USA)

  • Zhengxin Chen

    (��Department of Computer Science, University of Nebraska at Omaha, USA)

  • Guoqing Lu

    (�Department of Biology and School of Interdisciplinary Informatics, University of Nebraska at Omaha, USA)

  • Yong Shi

    (�Research Center on Fictitious Economy and Data Science, Chinese Academy of Science, P. R. China∥Key Lab of Big Data Mining and Knowledge Management, Chinese Academy of Science, P. R. China)

Abstract

The novel coronavirus disease — COVID-19 is a historic catastrophe that has caused many devastating impacts on human life and wellness. Researchers in academia and industry strive to understand the causes of this pandemic disease and find new therapeutics combating it. Consequently, the number of COVID-19 related publications increases rapidly, and it is too difficult for medical researchers and practitioners to keep up with the latest research and development. Literature filtering and categorization, and knowledge discovery can use text mining as a powerful tool. In this paper, we propose a text mining method to explore the categories of COVID-19 related themes and identify the standard methodologies that have been used. We discuss the potential limitations of this preliminary study and present future perspectives related to COVID-19 research. This paper provides an quantitative and qualitative mixed analysis example of using some research papers by data mining method to dig out several hidden information and set up a foundation for data scientists to develop more effective algorithms to deal with COVID-19 related problems.

Suggested Citation

  • Fangyao Liu & Daji Ergu & Biao Li & Wei Deng & Zhengxin Chen & Guoqing Lu & Yong Shi, 2024. "A Text Mining Approach to Covid-19 Literature," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 23(04), pages 1427-1448, July.
    • Handle: RePEc:wsi:ijitdm:v:23:y:2024:i:04:n:s0219622022500870
    DOI: 10.1142/S0219622022500870
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622022500870
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219622022500870?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:ijitdm:v:23:y:2024:i:04:n:s0219622022500870. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.