IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i19p3607-d270921.html
   My bibliography  Save this article

Social Big-Data Analysis of Particulate Matter, Health, and Society

Author

Listed:
  • Juyoung Song

    (Department of Administration of Justice, Pennsylvania State University, Schuylkill Haven, PA 17972, USA)

  • Tae Min Song

    (Department of Health Management, Sahmyook University, Seoul 01795, Korea)

Abstract

The study collected particulate matter (PM)-related documents in Korea and classified main keywords related to particulate matter, health, and social problems using text and opinion mining. The study attempted to present a prediction model for important causes related to particulate matter by using social big-data analysis. Topics related to particulate matter were collected from online (online news sites, blogs, cafés, social network services, and bulletin boards) from 1 January 2015, to 31 May 2016, and 226,977 text documents were included in the analysis. The present study applied machine-learning analysis technique to forecast the risk of particulate matter. Emotions related to particulate matter were found to be 65.4% negative, 7.7% neutral, and 27.0% positive. Intelligent services that can detect early and prevent unknown crisis situations of particulate matter may be possible if risk factors of particulate matter are predicted through the linkage of the machine-learning prediction model.

Suggested Citation

  • Juyoung Song & Tae Min Song, 2019. "Social Big-Data Analysis of Particulate Matter, Health, and Society," IJERPH, MDPI, vol. 16(19), pages 1-17, September.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:19:p:3607-:d:270921
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/19/3607/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/19/3607/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wei Chen & Yijun Shi & Liwen Fan & Lijun Huang & Jingyi Gao, 2021. "Influencing Factors of Public Satisfaction with COVID-19 Prevention Services Based on Structural Equation Modeling (SEM): A Study of Nanjing, China," IJERPH, MDPI, vol. 18(24), pages 1-18, December.
    2. Bomi Kim & Eun Joo Yoon & Songyi Kim & Dong Kun Lee, 2020. "The Effects of Risk Perceptions Related to Particulate Matter on Outdoor Activity Satisfaction in South Korea," IJERPH, MDPI, vol. 17(5), pages 1-14, March.
    3. Sang-Hyeok Lee & Jung Eun Kang, 2022. "Spatial Disparity of Visitors Changes during Particulate Matter Warning Using Big Data Focused on Seoul, Korea," IJERPH, MDPI, vol. 19(11), pages 1-16, May.

    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:gam:jijerp:v:16:y:2019:i:19:p:3607-:d:270921. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.