Text mining analysis of U.S. Department of Labor's MSHA fatal accident reports for coal mining
© 2018 Society for Mining, Metallurgy and Exploration. All rights reserved. Coal mining is an age-old method of power production in the world. It is also widely known for the associated risks during mining. Although the number of mishaps has dropped dramatically over the years, the number of fatal accidents is still a concern, and any aid in reducing these figures would be important to industry workers. Therefore, data from accident reports during the past seven years were collected and analyzed. Qualitative data from reports and articles provide a deeper understanding of the incidents, thus affording the possibility of identifying patterns and relationships between the accidents. For this study, a total of 119 fatality accidents occurring in U.S. coal mining operations were analyzed using advanced text mining techniques. The fatality data were obtained from U.S. Mine Safety and Health Admininstration fatalgram reports. This analysis is primarily exploratory in nature and uses term frequencyinverse document frequency (TF-IDF) methodologies along with correlation network plots. An interesting word relation pattern was obtained that identified "positioning" as the common cause of 12.6 percent of accidents, and found that 29.4 percent of accidents can be categorized as vehicle-related.
Text mining analysis of U.S. Department of Labor's MSHA fatal accident reports for coal mining.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/13735