Case Study of Text Analytics Applied to Accident Reports of a University

Abstract

Many accidents have occurred in universities and the accident reports are accumulated in most universities. The information described in the accident reports must be used effectively to prevent a recurrence of the accidents. In this study, we applied text analytics to the description written in 373 accident reports in a university as a case study. Information mining method was adopted for the contents analysis, and 9 factors based on m-SHEL and human error, that is “software”, “hardware”, “environment”, “liveware2”, “management” “slip”, “lapse”, “mistake”, and “violation” were used for morphological analysis for description in report. The factors in each category of accident situation were extracted, and it is suggested that text analytics is one of the most effective methods to analyse the accident reports in universities.