Analyzing Public Opinions on Particulate Matter Contents using Comments of News Article
DOI:
https://doi.org/10.61841/rpgnwg04Keywords:
Particulate Matter, News Article,, Text Mining, Sentiment Analysis, Linear RegressionAbstract
PM (Particulate Matter) is a mixture of solid particles and liquid droplets found in the air. PM10 and PM2.5 mean PM whose diameter is smaller than 10 micrometers and 2.5 micrometers or less respectively. PM contains microscopic solids or liquid droplets that can be inhaled and can cause lung disease or invade the blood or brain. Since 2013, South Korea has published official PM statistics and informed the public about how to act. With the development of artificial intelligence, there is an increasing number of studies that analyze texts' emotions and public opinions embedded in texts. Many people comment on news related to fine dust, and the comments contain words that can be used to understand what news readers think about fine dust using opinion mining. This study aims to analyze people's perception by analyzing comments expressed on PM news. After reviewing related researches, we will present three research questions and provide answers to them through an empirical analysis using web crawling, basic sentiment analysis, and multiple linear regression. We will also provide a concluding remarks with research limitation and future research directions.
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