Abstract
Dealing with the social and political impacts of large complex projects requires monitoring and responding to concerns from an ever-evolving network of stakeholders. This paper describes the use of text analysis algorithms to identify stakeholders’ concerns across the project life cycle. The social license (SL) concept has been used to monitor the level of social acceptance of a project. That acceptance can be assessed from the texts produced by stakeholders on sources ranging from social media to personal interviews. The same texts also contain information on the substance of stakeholders’ concerns. Until recently, extracting that information necessitated manual coding by humans, which is a method that takes too long to be useful in time-sensitive projects. Using natural language processing algorithms, we designed a program that assesses the SL level and identifies stakeholders’ concerns in a few hours. To validate the program, we compared it to human coding of interview texts from a Bolivian mining project from 2009 to 2018. The program’s estimation of the annual average SL was significantly correlated with rating scale measures. The topics of concern identified by the program matched the most mentioned categories defined by human coders and identified the same temporal trends.
Highlights
Large complex projects generate diverse social and political impacts on a diverse network of stakeholders that itself is constantly evolving across the life cycle of the project [1]
This paper describes text analysis technology that can unlock the access to stakeholder concerns at a pace quick enough to be useful in a dynamic project management environment
As in other areas of machine learning (ML), there are two main types of techniques that can be employed for computationally finding meaning in patterns: supervised learning, in which the user provides examples that are labeled according to their meaning categories, and unsupervised learning, in which the computer establishes its own boundaries between clusters of data, based on proximity of data
Summary
Large complex projects generate diverse social and political impacts on a diverse network of stakeholders that itself is constantly evolving across the life cycle of the project [1]. Managing projects in a sustainable manner requires an awareness of the multiple linkages the project creates and extinguishes in the social, economic, and political ecosystem in which it is embedded [2]. The continuance of the project itself can be called into question by stakeholder coalitions whose concerns have been overlooked or dismissed [3]. Response to, stakeholder discontent can make the difference between costly delays and smooth progress [4]. This paper describes text analysis technology that can unlock the access to stakeholder concerns at a pace quick enough to be useful in a dynamic project management environment.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
