A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Benchmarking operational performance of buildings by text mining tenant surveys
Facility managers of large commercial and institutional buildings periodically collect text-based survey data from their tenants. While these large and amorphous datasets contain valuable information to benchmark operational performance and identify anomalies, it is time and resource-intensive to hire employees to read and analyze the datasets and extract insightful information from them. This paper presents a natural language processing-based methodology to extract operational insights from tenant survey databases. It also incorporates the verification of extracted complaint patterns using computerized maintenance management systems (CMMS) on a smaller scale. Tenant survey databases are comprised of free-text responses from tenants regarding annual/bi-annual survey responses that building managers request as a source of solicited feedback. CMMS databases consist of unsolicited complaints that are logged by tenants who are under discomfort/dissatisfaction with no additional prompt from a building operator/manager. The effectiveness of this methodology is demonstrated by gaining operational insights from tenant feedback gathered using survey data from a large office building in Ottawa, Canada. Different algorithms for sentiment analysis, association rule mining, and topic modeling are employed in the analysis to consolidate the textual data into common thermal and maintenance complaint categories. The accuracy of different text analytics algorithms is compared, and their effectiveness in analyzing tenant survey responses is discussed. Patterns of unsolicited tenant work order requests are contrasted to those observed in the survey responses. The results indicate that the floors that frequently submit work order requests are also likely to submit a large number of negative survey responses.
Benchmarking operational performance of buildings by text mining tenant surveys
Facility managers of large commercial and institutional buildings periodically collect text-based survey data from their tenants. While these large and amorphous datasets contain valuable information to benchmark operational performance and identify anomalies, it is time and resource-intensive to hire employees to read and analyze the datasets and extract insightful information from them. This paper presents a natural language processing-based methodology to extract operational insights from tenant survey databases. It also incorporates the verification of extracted complaint patterns using computerized maintenance management systems (CMMS) on a smaller scale. Tenant survey databases are comprised of free-text responses from tenants regarding annual/bi-annual survey responses that building managers request as a source of solicited feedback. CMMS databases consist of unsolicited complaints that are logged by tenants who are under discomfort/dissatisfaction with no additional prompt from a building operator/manager. The effectiveness of this methodology is demonstrated by gaining operational insights from tenant feedback gathered using survey data from a large office building in Ottawa, Canada. Different algorithms for sentiment analysis, association rule mining, and topic modeling are employed in the analysis to consolidate the textual data into common thermal and maintenance complaint categories. The accuracy of different text analytics algorithms is compared, and their effectiveness in analyzing tenant survey responses is discussed. Patterns of unsolicited tenant work order requests are contrasted to those observed in the survey responses. The results indicate that the floors that frequently submit work order requests are also likely to submit a large number of negative survey responses.
Benchmarking operational performance of buildings by text mining tenant surveys
Dutta, Saptak (author) / Gunay, H. Burak (author) / Bucking, Scott (author)
Science and Technology for the Built Environment ; 27 ; 741-755
2021-07-03
15 pages
Article (Journal)
Electronic Resource
Unknown
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