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Text Mining Applications in the Construction Industry: Current Status, Research Gaps, and Prospects

Author

Listed:
  • Na Xu

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221008, China)

  • Xueqing Zhou

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221008, China)

  • Chaoran Guo

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221008, China)

  • Bai Xiao

    (Tianjin Jingang Construction Co., Ltd., Tianjin 300456, China)

  • Fei Wei

    (Tianjin Jingang Construction Co., Ltd., Tianjin 300456, China)

  • Yuting Hu

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221008, China)

Abstract

With the advent of the Industry 4.0 era, information technology has been widely developed and applied in the construction engineering field. Text mining techniques can extract interesting and important data hidden in plain text, potentially allowing problems in the construction field to be addressed. Although text mining techniques have been used in the construction field for many years, there is a lack of recent reviews focused on their development and application from a literature analysis perspective; therefore, we conducted a review with the aim of filling this gap. We use a combination of bibliometric and manual literature analyses to systematically review the text mining-based literature related to the construction field from 1997 to 2022. Specifically, publication analysis, collaboration analysis, co-citation analysis, and keyword analysis were conducted on 185 articles collected from the SCOPUS database. Based on a read-through of the 185 papers, the current research topics in text mining were manually determined and sorted, including tasks and methods, application areas, and core methods and algorithms. The presented results provide a comprehensive understanding of the current state of TM techniques, thereby contributing to the further development of TM techniques in the construction industry.

Suggested Citation

  • Na Xu & Xueqing Zhou & Chaoran Guo & Bai Xiao & Fei Wei & Yuting Hu, 2022. "Text Mining Applications in the Construction Industry: Current Status, Research Gaps, and Prospects," Sustainability, MDPI, vol. 14(24), pages 1-29, December.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16846-:d:1004857
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    References listed on IDEAS

    as
    1. Huchang Liao & Ming Tang & Li Luo & Chunyang Li & Francisco Chiclana & Xiao-Jun Zeng, 2018. "A Bibliometric Analysis and Visualization of Medical Big Data Research," Sustainability, MDPI, vol. 10(1), pages 1-18, January.
    2. S Nefti & M Oussalah & Y Rezgui, 2009. "A modified fuzzy clustering for documents retrieval: application to document categorization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 384-394, March.
    3. Su Jin Choi & So Won Choi & Jong Hyun Kim & Eul-Bum Lee, 2021. "AI and Text-Mining Applications for Analyzing Contractor’s Risk in Invitation to Bid (ITB) and Contracts for Engineering Procurement and Construction (EPC) Projects," Energies, MDPI, vol. 14(15), pages 1-28, July.
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