Department of Administration of Justice, Pennsylvania State University, Schuylkill Haven, PA 17972, USA.
Department of Health Management, Sahmyook University, Seoul 01795, Korea.
Int J Environ Res Public Health. 2019 Sep 26;16(19):3607. doi: 10.3390/ijerph16193607.
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.
该研究在韩国收集了与颗粒物相关的文献，并使用文本和观点挖掘对与颗粒物、健康和社会问题相关的主要关键词进行了分类。该研究试图通过社会大数据分析，提出与颗粒物有关的重要原因的预测模型。从 2015 年 1 月 1 日至 2016 年 5 月 31 日，从在线（在线新闻网站、博客、咖啡馆、社交网络服务和公告板）收集了与颗粒物有关的主题，并对 226977 个文本文件进行了分析。本研究应用机器学习分析技术来预测颗粒物的风险。发现与颗粒物有关的情绪有 65.4%为负面，7.7%为中性，27.0%为正面。如果通过机器学习预测模型的关联预测出颗粒物的风险因素，那么有可能开发出能够检测早期并预防颗粒物未知危机情况的智能服务。
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