Text mining and natural language processing in construction research: a scientometric analysis and qualitative review

Text mining (TM) and natural language processing (NLP) have emerged as powerful analytical approaches for extracting value from the large volumes of unstructured textual data generated throughout the construction industry, leading to a growing body of research focused on addressing sector-specific challenges using these technologies. This paper presents a systematic review integrating scientometric analysis and qualitative synthesis to map the research landscape of TM/NLP applications in construction. The objectives are to summarize existing research achievements, identify prevailing research trends, evaluate academic influence patterns, and propose future research directions. A dataset of 153 peer-reviewed journal articles was compiled from 2015 to 2025 and retrieved from the Web of Science (WoS), Scopus, and Google Scholar databases following duplicate removal and screening procedures. The analysis investigates publication growth trends, collaboration networks among researchers, countries, and institutions, and thematic structures through keyword co-occurrence and co-citation analysis to identify emerging research topics. Results reveal that the field has entered a high-growth stage since 2022, with annual publications surging sharply; the most dominant research theme is construction safety risk identification and accident prevention, followed by technical applications of machine learning and NLP. The findings highlight leading contributors, influential institutions, and major publication outlets, providing guidance for academic collaboration and research dissemination. Furthermore, the study offers a reference framework for researchers and practitioners to select appropriate TM- and NLP-based solutions for specific construction challenges, while also supporting policymakers and journal editors in prioritizing future research and development directions. Overall, this review enhances understanding of the application landscape of TM and NLP in construction and contributes to advancing the digital transformation of the industry.