Journal of Construction Engineering and Management

Abstract

Learning from online comments is essential for enhancing understanding and improving online housing reputation (OHR). However, two significant issues require attention. First, analyzing online comments for reputation information extraction is a labor-intensive and time-consuming task. Second, most existing online housing information platforms lack effective visual aids, merely presenting the average comment ratings or listing comment texts without secondary interpretation. To address these challenges, this study proposes an OHR assessment framework based on text mining and visualization technologies. This study first evaluates the performance of eight sentiment analysis models for analyzing housing comments, and the attention-based BiLSTM model achieved the highest accuracy (83.57%). Additionally, a housing attribute ontology is constructed to reveal eight critical attributes influencing OHR. Finally, a reputation visualization scheme is designed to comprehensively present OHR. A case study for analyzing online comments from three construction enterprises reveals the advantages and feasibility of the proposed framework for assessing OHR. This study contributes to the body of knowledge by establishing the connection between housing comments and OHR, greatly advancing the research in the construction domain’s reputation management. Furthermore, OHR analysis can facilitate decision making optimization for both consumers and managers, which has theoretical and practical significance for the healthy and sustainable development of the online housing market.

Practical Applications

Developing a sound housing reputation assessment method is crucial for the development of the online housing market. Emerging information technologies like natural language processing and deep learning appear promising in the comment-based reputation analysis. This study proposes an online housing reputation assessment framework that combines text mining and visualization technologies to automatically analyze housing comments and generate online housing reputations. The findings of this study establish the connection between housing comments and reputation and reveal eight key attributes influencing online housing reputation, contributing to research on reputation management in the construction field. The potential practical applications of this study are elucidated from the perspectives of consumers, construction enterprises, governments, and the construction industry. For consumers, reputation assessment can optimize the decision making process and promote public supervision to force construction enterprises to improve the quality of their housing products. Construction enterprises can timely improve their product strategies based on reputation information to increase revenue. Governments can strengthen the supervision of low-reputation enterprises and low quality housing products, thereby enhancing government credibility. The proposed framework can encourage technological innovation and digital transformation in the construction industry and even can be extended to other industries for online reputation assessment.