Sentiment analytics of social media data in the construction industry : preliminary study on Weibo

The increasing use of the Internet for many purposes is creating big data, many of which are generated from social media. These big data potentially could assist in obtaining valuable administrative information and even explore new social phenomena. Traditional ways of collecting data, such as questionnaire surveys, are time-consuming and costly. Therefore, the use of social media affords the opportunity to extract information that might be of benefit to the construction industry in a responsive and inexpensive manner. To this end, this paper explores whether information and knowledge that would be valuable in the construction domain can be generated by analyzing social media data. Construction safety is relative to the mental health of construction labors. This paper used social media data in the construction industry to analysis the sentiments of construction labors. Weibo was selected for an initial trial analysis because of its wide usage in China. Five user clusters were selected and analyzed: construction workers, construction companies, construction unions, construction media, and construction college. For each user identified in the five clusters, the 500 most recent Weibo messages were collected. Stanford Natural Language Processing (StanfordNLP) as a data-analysis method was used for sentiment analysis. The detailed findings, benefits, and barriers to incorporating social media data analytics in the construction industry, as well as future research directions, are discussed in this paper. For example, the sentiment analysis results indicated that construction workers tend to have a higher proportion of negative messages compared to the other clusters, which may prompt more attention to emotional guidance and understanding by construction companies and the public. This paper benefits academia by testing an alternative way of studying the construction population, which could help decision makers gain a better understanding of real-world situations in the construction industry.