Oh, Heung Jin (2024) Exploratory analysis of construction job opening advertisements for investigating actual labor needs using web scraping and text analytics. PhD thesis, Georgia Institute of Technology, USA.
Abstract
The research focuses on exploratory data analysis of construction job opening advertisements, aiming to extract extensive information, including wages, locations, and job requirements. It proposes a novel approach for collecting nationwide data on these advertisements and analyzing them to understand the actual job market. Using web scraping and text mining techniques, the study collected over 1.4 million job openings across the U.S. to identify market patterns and trends.To analyze the data, the study employs various techniques involving natural language processing (NLP), machine learning (ML), statistical methods, application programming interfaces (APIs), and chatbots across three main chapters. The first main chapter investigates skill sets for multiskilled laborers in the construction industry. The second chapter identifies wage gaps to address workforce equity issues. The third chapter aims to expand research approaches by employing chatbots to detect further issues in the construction job market.This research contributes to the body of knowledge by creating a novel approach for capturing large data from construction job advertisements using web scraping and text analytics. The research will transform the paradigm of analyzing the construction job market. The anticipated long-term benefits include enhancing workforce usability, promoting workforce equity, and facilitating adaptation to detect dynamic changes in the construction job market.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Ashuri, Baabak |
| Index terms: | usability, wages, data analysis, machine learning, body of knowledge, programming, adaptation, construction industry, paradigm, mining, statistical method |
| Subjects: | data analysis and analytics, artificial intelligence, user-centered design, geotechnical engineering, statistical analysis, industry analysis, education and knowledge transfer, knowledge management, user focus, programming, business economics |
| Topics: | Research Practice, Information Management, Human Resources, Design Practice, Digital Applications, Engineering Principles |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here