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

Juyoung Song and Tae Min Song
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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

IJERPH, 2019, vol. 16, issue 19, 1-17

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.

Keywords: Social Big-Data Analysis; Particulate Matter; health; South Korea (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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