Following the “Cognitive Building” concept, in a few years, building automation systems were drastically improved to collect a large amount of user data. However, despite this evolution and the research efforts in the field, human-building interaction remained one of the least mature fields of building science due to the occupants’ complexity and diversity. Collecting data became simple and cheap, but transforming collected “data” into valuable “information” able to create an effective interaction between buildings and occupants remains complex.
This work contributes by proposing a method to translate unstructured data, coming from Computerized Maintenance Management System (CMMS), into information useful to improve the interactions between occupants and buildings in the management of the maintenance process. End-users’ maintenance requests, collected through a CMMS, were used to create a technical sentiment lexicon able to predict the priority of an intervention based on an inverted naïve Bayes approach. Sentiment lexicons are part of sentiment analysis, an interesting research field introduced to study people’s opinions, sentiments, emotions, and attitudes through Natural Language Processing (NLP). The technical lexicon is useful to immediately perform the priority assessment of contemporary end-users’ maintenance requests, thus being more rapid than traditional Machine Learning methods.
