An unsupervised text-mining approach and a hybrid methodology to improve early warnings in construction project management

Abstract

Extracting critical sections from project management documents is a challenging process and an active area of research. Project management documents contain certain earlywarnings that, ifmodelled properly, may inform the project planners and managers in advance of any impending risks via early warnings. Extraction of such indicators from documents is termed as text mining, which is an active area of research. In the context of construction project management, extraction of semantically crucial information from documents is a challenging task that can in turn be used to provide decision support by optimising the entire project lifecycle. This research presents a two-step modelling and clustering methodology. It exploits the capability of a Naïve Bayes classifier to extract early warnings from management text data. In the first step, a database corpus is prepared via a qualitative analysis of expertly fed questionnaire responses. In the latter stage a Naïve Bayes classifier is proposed which evaluates real-world construction management documents to identify potential risks based on certain keyword usages. The classifier outcome was compared against labelled test documents and gave an accuracy of 68.02 %, which is better than the majority of text mining algorithms reported in the literature.