Automatic detection of maintenance requests: Comparison of Human Manual Annotation and Sentiment Analysis techniques

In the building management process, the collection of end-users' maintenance requests is a rich source of information to evaluate occupants' satisfaction and building systems. Computerized Maintenance Management Systems typically collect non-standardized data, difficult to be analyzed. Text mining methodologies can help to extract information from end-users' requests and support priority assignment of decisions. Sentiment Analysis can be applied at this aim, but complexities due to words/sentences orientations/polarities and domains/contexts can reduce its effectiveness. This study compares the ability of different Sentiment Analysis techniques and Human Manual Annotation, considered the gold standard, to automatically define a maintenance severity ranking. About 12,000 requests were collected for 34 months in 23 University buildings. Results show that current Sentiment Analysis techniques seem to limitedly recognize the role of technical words for severity assessment of requests, thus remarking the necessity of novel lexicons in the field of building facility management for automatic maintenance management procedures.