Analysis of News Articles on Urban Agriculture using Text Mining from 2012 to 2021

Abstract

Background and objective: Urbanization and reckless development have caused numerous problems, and urban agriculturewith various functions is getting attention as a countermeasure. The purpose of this study is to provide basic data forrevitalizing related research and setting effective policy directions by analyzing online articles on urban agriculture from2012 to 2021. Methods: A total of 15,336 online news articles on urban agriculture were collected from January 1, 2012 to December 31,2021. For more detailed analysis, the time period was divided by 5 years. Next, nouns were tokenized through morphologicalanalysis, and the main keywords were confirmed by simple frequency analysis and TF-IDF weighting analysis. Followingthat, LDA topic modeling was conducted to generate topics and words for each period. Python 3.9.5 was applied to run theabove analysis. Results: As a result of the analysis, there were four topics in the 1st period and six topics in the 2nd period. Summarizingthe topics over time, it was found that urban agriculture is expanding not only to social and cultural areas but also to diverseareas such as welfare and the environment. In addition, as the role of urban agriculture and the demand for servicesexpanded, various education courses and programs were activated, including the implementation of a national professionalqualification system, expert training courses, and cultivation technology programs. Conclusion: Based on the findings, urban agriculture is expected to be able to expand into various areas that takemultidisciplinary values into account and create various convergence services that follow the trend of the times.