A Method of Extracting and Classifying Local Community Problems from Citizen-Report Data using Text Mining

Abstract

Local governments are required to appropriately prioritize and respond to regional issues that are becoming diversified and complicated under the constraints of manpower and budget. In addition, it is required to understand regional issues based on objective data analysis from the viewpoint of “evidence-based policymaking.” Under these circumstances, the “Citizen-Report” mechanism, which has recently been introduced in local governments, may contribute not only to prompt resolution of individual field problems but also to the clarification of the tendencies of problem occurrence. However, the classification of problems is not necessarily set for reflecting the actual occurrence tendencies. Therefore, this study proposes a method to extract and classify problems in an objective and reproducible manner that reflects the tendencies of actual problem occurrences by analyzing the content of the Citizen-Report using text mining. We verify this method using the data of Chiba City as an example, and the result shows that the tendencies of real problem occurrence, which were not able to be understood by classifying based on the category of the department in charge in local government such as “roads” or “parks,” became clear. This method is also applicable to other Citizen-Report data and can be expected to be used for understanding regional issues in various local governments.