Classifying scuba diving sites through diver reviews with a web scraping based UTADIS application

Abstract

This research addresses the utilization of user-defined and web-based data for decision-making in recreational activities, focusing on the case of scuba diving, a globally significant recreational pursuit. While a wealth of user-generated information is available on the internet for various leisure activities, harnessing this data involves extensive data collection and organization efforts. Our proposed methodology involves the collection and organization of user reviews, both in verbal and quantitative forms, to create a pertinent dataset for applying multiple criteria decision methodologies to classify diving sites worldwide. An initial dataset containing over 14,000 diving sites worldwide is aggregated into 721 regions and these regions are classified using UTADIS methodology. The research showcases how user-generated review data can be transformed into valuable information, applying classification algorithms of multiple criteria decision analysis within the context of scuba diving. Furthermore, the proposed approach in this research holds the potential to serve as a model for leveraging user-generated data in decision-making processes across various service sectors such as hospitality and leisure that highly rely on customer experience by providing new insights on how more data-driven approaches can be utilized.