Labor sentiment analysis for the construction industry : a case study of Twitter in the USA

Construction industry is a labor-intensive industry. Sentiment or mood of worker is a key issue in this business. To analyse this issue, using traditional ways such as questionnaire survey to collect data is both time- and cost-consuming. Recently, with the rapid development of social media services, data can be collected and extracted for sentiment analysis to provide officials and managers with fresh perspectives on labor in the construction management. In this paper, a labor sentiment analysis systematic framework is proposed. This system collected user messages from social media sites, establishes and compare different clusters emotion dictionaries by location, time, etc. This paper generated valuable information and knowledge in the construction domain. As an initial trial, this study selected social media of Twitter because of its wide usage in the United States. Four clusters which include construction workers, construction companies, construction unions, and construction media were analysed. For each user identified in the four clusters, the 3,200 most recent twitter messages were collected. This research then analysed these data in the following aspects to dig out sentiments behind data: hourly, daily, monthly, and locations. Detailed findings, benefits and barriers to incorporating social media data analytics in the construction industry, along with future research, were discussed. This paper benefits the academia by testing an alternative way of studying the construction population, which further will help decision makers better understand the real situations of the construction industry.