Developing a Sentiment Lexicon-Based Quality Performance Evaluation Model on Construction Projects in Korea

Abstract

The increasing frequency of structural failures on construction sites emphasizes the critical role of rigorous supervision in ensuring the quality of both construction processes and materials. Current regulatory frameworks mandate the production of detailed supervision reports to provide comprehensive evaluations of construction quality, material compliance, and site records. This study proposes a novel approach to harnessing unstructured reports for automated quality assessment. Employing text mining techniques, a sentiment lexicon specifically tailored for quality performance evaluation was developed. A corpus-based manual classification was conducted on 291 relevant words and 432 sentences extracted from the supervision reports, assigning sentiment labels of negative, neutral, and positive. This sentiment lexicon was then utilized as fundamental information for the Quality Performance Evaluation Model (QPEM). To validate the efficacy of the QPEM, it was applied to supervision reports from 30 construction sites adhering to legal standards. Furthermore, a Pearson correlation analysis was performed with the actual outcomes based on the legal requirements, including quality test failure rate, material inspection failure rate, and inspection management performance. By leveraging the wealth of unstructured data continuously generated throughout a project’s lifecycle, this model can enhance the timeliness of inspection and management processes, ultimately contributing to improved construction performance.