Extracting Environmental Research Trends using LDA - Yeon Kim, Do; Won Kang, Sung (International Journal of Engineering and Technology, 2018)
In this study, we compared topics of two distinct text data on environmental issues: environmental research reports and on-line environmental news using a Latent Dirichlet Allocation (LDA) analysis. For the environmental research reports, we used digitized research reports from the Korea Environment Institute, whereas for the newspaper, we crawled environment news articles on the Naver portal service. Once we extracted the topics, we compared the annual share of each topic in each text medium. From the LDA analysis, ten topics emerged from each medium. Six are common in both media, whereas four of the latest issues, namely, â€œgene variation,â€ â€œnoise,â€ â€œhealth,â€ and â€œdata,â€ only appeared in environmental news. In addition, among the six common topics, the share of â€œwater pollutionâ€ and â€œwasteâ€ topics in environmental news appears to lead the share of the same two topics in the environmental research reports. These two results suggest that research topics tend to fall behind environmental issues in terms of latest interest. This study raises the possibility of using the LDA model to analyze research trends and find new research topics.Â