This paper proposes a methodology for using text analytics on computerized maintenance management system (CMMS) databases to benchmark the operational performance of commercial buildings. To this end, we extracted five years’ worth of service request and work-order logs from five large commercial buildings in Ottawa, Canada. We employed the association rule mining method on these datasets to identify building, system, and component-level recurring work-order taxonomies and common failure modes. The potential of Sankey diagrams, survival curves, and stacked line plots to effectively visualize the temporal, spatial, and categorical anomalies in the service request patterns was examined. It was identified that often only a few floors and service request types account for most of the service requests in a building. By applying the association rule mining algorithm on the work-order logs, it was identified that the lighting-related complaints were resolved by replacing ballasts and lights, and the thermal complaints were addressed by adjusting the temperature setpoints, airflow rates, and fan operation schedules.