Sehwan Chung , Jungyeon Kim , Joonwoo Baik , Seokho Chi , Du Yon Kim
{"title":"Identifying issues in international construction projects from news text using pre-trained models and clustering","authors":"Sehwan Chung , Jungyeon Kim , Joonwoo Baik , Seokho Chi , Du Yon Kim","doi":"10.1016/j.autcon.2024.105875","DOIUrl":null,"url":null,"abstract":"<div><div>The uncontrollable nature of international construction projects requires continuous monitoring of issues in the host country. News articles can provide relevant information to monitor the issues, but the manual investigation of substantial news text is impractical. This paper proposes a framework to automatically collect information related to the host country's business environments from news text, consisting of three modules: (1) web scraping for collecting news text; (2) text embedding using a pre-trained language model; and (3) text clustering for extracting essential issues. Applying this framework to real-world news demonstrated its proficiency in identifying significant issues, outperforming the existing similar methods in terms of: (1) the accuracy of issue identification; (2) the quality of identified issues; and (3) the degree of human intervention. This paper contributes to the body of knowledge by showcasing the utility of news text in gathering information and identifying issues about the host country during international projects.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"168 ","pages":"Article 105875"},"PeriodicalIF":9.6000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580524006113","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The uncontrollable nature of international construction projects requires continuous monitoring of issues in the host country. News articles can provide relevant information to monitor the issues, but the manual investigation of substantial news text is impractical. This paper proposes a framework to automatically collect information related to the host country's business environments from news text, consisting of three modules: (1) web scraping for collecting news text; (2) text embedding using a pre-trained language model; and (3) text clustering for extracting essential issues. Applying this framework to real-world news demonstrated its proficiency in identifying significant issues, outperforming the existing similar methods in terms of: (1) the accuracy of issue identification; (2) the quality of identified issues; and (3) the degree of human intervention. This paper contributes to the body of knowledge by showcasing the utility of news text in gathering information and identifying issues about the host country during international projects.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.