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 agriculture with various functions is getting attention as a countermeasure. The purpose of this study is to provide basic data for revitalizing related research and setting effective policy directions by analyzing online articles on urban agriculture from 2012 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 morphological analysis, and the main keywords were confirmed by simple frequency analysis and TF-IDF weighting analysis. Following that, LDA topic modeling was conducted to generate topics and words for each period. Python 3.9.5 was applied to run the above analysis.Results: As a result of the analysis, there were four topics in the 1st period and six topics in the 2nd period. Summarizing the topics over time, it was found that urban agriculture is expanding not only to social and cultural areas but also to diverse areas such as welfare and the environment. In addition, as the role of urban agriculture and the demand for services expanded, various education courses and programs were activated, including the implementation of a national professional qualification 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 take multidisciplinary values into account and create various convergence services that follow the trend of the times.