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

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ID: 55602
2019
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Ranked #233 of 862 articles by views in International journal of environmental research and public health

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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.
Reference Key
song2019socialinternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Song, Juyoung;Song, Tae Min;
Journal International journal of environmental research and public health
Year 2019
DOI
E3607
URL
Keywords
particulate matter health south korea social big-data analysis

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