Text mining and natural language processing in construction

Text mining (TM) and natural language processing (NLP) have stirred interest within the construction field, as they offer enhanced capabilities for managing and analyzing text-based information. This highlights the need for a systematic review to identify the status quo, gaps, and future directions from the perspective of construction management. A review was conducted by aligning the objectives of 205 publications with the specific domains, areas, tasks, and processes outlined in construction management practices. This review reveals multiple facets of the construction sector empowered by TM/NLP approaches and highlights essential voids demanding consideration for automation possibilities and minimizing manual tasks. Ultimately, following identified obstacles, the review results indicate potential research opportunities: (1) strengthening overlooked construction aspects, (2) coupling diverse data formats, and (3) leveraging pre-trained language models and reinforcement learning. The findings will provide vital insights, fostering further progress in TM/NLP research and its applications in academia and industry.

Automated summary

This paper reviews the application of text mining and natural language processing in the construction field, highlighting the need for automation and minimizing manual tasks. The study identifies potential research opportunities in strengthening overlooked construction aspects, coupling diverse data formats, and leveraging pre-trained language models and reinforcement learning.