Abstract
Location-specific occupant feedback is crucial information for facility management since it reflects occupant satisfaction on the facility and informs corrective maintenance. However, the current facility maintenance management (FMM) system cannot effectively collect location-specific occupant feedback, resulting in the inefficiency and ineffectiveness of FMM. This research explores a mobile social network approach for location-specific feedback collection in FMM. Specifically, this research develops a natural language processing (NLP)-based machine learning algorithm to classify the occupant feedback collected from the social network application automatically. In turn, the proposed approach could facilitate occupants’ participation and occupant feedback solicitation and assist facility management (FM) personnel in locating and navigating the reporting issues. The Waikato Environment for Knowledge Analysis (Weka) is utilized for training and testing machine learning algorithms based on historical records. The trained model can retrieve the essential information to facilitate FM decision-making based on occupants’ input data (e.g., location and textual information). The technical feasibility of mobile social network applications to report FMM feedback and concerns was demonstrated using a case study. This research contributes to the body of knowledge by an NLP-based model that automatically processes the location-specific occupant feedback for FMM. Future work will focus on developing a prototype application for FMM based on our findings.
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References
Aldwairi M, Alwahedi A (2018) Detecting Fake News in Social Media Networks. Procedia Comput Sci. https://doi.org/10.1016/j.procs.2018.10.171
Barbier G, Liu H (2011) Data mining in social media. In: Social network data analytics.https://doi.org/10.1007/978-1-4419-8462-3_12
Barros C, Moya-Gómez B, Gutiérrez J (2020) Using geotagged photographs and GPS tracks from social networks to analyse visitor behaviour in national parks. Curr Issue Tour. https://doi.org/10.1080/13683500.2019.1619674
Binalhaj M, Liu H, Sulaiman M, Abudayyeh OO (2021) Mobile crowdsourcing-based data collection for user-centered facility maintenance management. Can J Civ Eng. https://doi.org/10.1139/cjce-2020-0439
Bouabdallaoui Y, Lafhaj Z, Yim P, Ducoulombier L, Bennadji B (2020) Natural language processing model for managing maintenance requests in buildings. Buildings 10(9):1–12. https://doi.org/10.3390/BUILDINGS10090160
Bouckaert RR, Frank E, Hall M, Kirkby R, Reutemann P, Seewald A, Scuse D (2014) WEKA manual for version 3-7-12. In: WEKA Manual for Version 3-6-0
Chen C, Tang L (2019) BIM-based integrated management workflow design for schedule and cost planning of building fabric maintenance. Autom Constr 107(August):102944. https://doi.org/10.1016/j.autcon.2019.102944
Daw S, Basak R (2020) Machine learning applications using Waikato environment for knowledge analysis. In: Proceedings of the 4th international conference on computing methodologies and communication, ICCMC. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00065
Harrison S, Johnson P (2019) Challenges in the adoption of crisis crowdsourcing and social media in Canadian emergency management. Gov Inf Q. https://doi.org/10.1016/j.giq.2019.04.002
Hu X, Chu THS, Leung VCM, Ngai ECH, Kruchten P, Chan HCB (2015) A survey on mobile social networks: applications, platforms, system architectures, and future research directions. IEEE Commun Surv Tutorials. https://doi.org/10.1109/COMST.2014.2371813
Injadat MN, Salo F, Nassif AB (2016) Data mining techniques in social media: a survey. Neurocomputing. https://doi.org/10.1016/j.neucom.2016.06.045
Kang Y, Cai Z, Tan CW, Huang Q, Liu H (2020) Natural language processing (NLP) in management research: a literature review. J Manage Anal. https://doi.org/10.1080/23270012.2020.1756939
Lin YC, Su YC, Chen YP (2014) Developing mobile BIM/2D barcode-based automated facility management system. Sci World J. https://doi.org/10.1155/2014/374735
Mo Y, Zhao D, Jing Du, Syal M, Aziz A, Li H (2020) Automated staff assignment for building maintenance using natural language processing. Autom Constr. https://doi.org/10.1016/j.autcon.2020.103150
Motamedi A, Soltani MM, Hammad A (2013) Localization of RFID-equipped assets during the operation phase of facilities. Adv Eng Inform. https://doi.org/10.1016/j.aei.2013.07.001
Motawa I, Almarshad A (2013) A knowledge-based BIM system for building maintenance. Autom Constr. https://doi.org/10.1016/j.autcon.2012.09.008
Nati M, Gluhak A, Abangar H, Headley W (2013) SmartCampus: a user-centric testbed for internet of things experimentation. In: International symposium on wireless personal multimedia communications, WPMC
Naylor S, Gillott M, Lau T (2018) A review of occupant-centric building control strategies to reduce building energy use. Renew Sustain Energy Rev. https://doi.org/10.1016/j.rser.2018.07.019
O’Brien W, Gaetani I, Carlucci S, Hoes PJ, Hensen JLM (2017) On occupant-centric building performance metrics. Build Environ. https://doi.org/10.1016/j.buildenv.2017.06.028
Park JY, Ouf MM, Gunay B, Peng Y, O’Brien W, Kjærgaard MB, Nagy Z (2019) A critical review of field implementations of occupant-centric building controls. Build Environ. https://doi.org/10.1016/j.buildenv.2019.106351
Soiraya M, Thanalerdmongkol S, Chantrapornchai C (2012) Using a data mining approach: spam detection on facebook. Int J Comput Appl. https://doi.org/10.5120/9343-3660
Setting Up Your Facebook App. App Setup—Messenger Platform—Documentation—Facebook for Developers. Accessed 15 Feb 2021. https://developers.facebook.com/docs/messenger-platform/getting-started/app-setup
Terreno S, Akanmu A, Anumba CJ, Olayiwola J (2020) Cyber-physical social systems for facility management. Cyber-Phys Syst Built Environ. https://doi.org/10.1007/978-3-030-41560-0_16
Umair A, Nanda P, He X, Choo KKR (2018) User relationship classification of facebook messenger mobile data using WEKA. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics).https://doi.org/10.1007/978-3-030-02744-5_25
Varudharajulu AK, Ma Y (2019) Feature-based Facebook reviews process model for emanagement using data mining. In: ACM international conference proceeding series.https://doi.org/10.1145/3306500.3306514
Xie Q, Zhou X, Wang J, Gao X, Chen Xi, Chun L (2019) Matching real-world facilities to building information modeling data using natural language processing. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2937219
Xu J, Lu W, Xue F, Chen Ke (2019) ‘Cognitive facility management’: definition, system architecture, and example scenario. Autom Constr. https://doi.org/10.1016/j.autcon.2019.102922
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The authors are grateful for the ongoing support from the facility management center by making the data available to the research team.
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Alhaj, M.B., Liu, H., Sulaiman, M. (2023). Towards Occupant-Centric Facility Maintenance Management: Automated Classification of Occupant Feedback Using NLP. In: Walbridge, S., et al. Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021. CSCE 2021. Lecture Notes in Civil Engineering, vol 247. Springer, Singapore. https://doi.org/10.1007/978-981-19-0968-9_24
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