Exploring the Importance of Destination Attributes of Sustainable Urban Waterfronts: Text and Data Mining of Tourists’ Online Reviews

Wei-Ching Wang and Chung-Hsien Lin ()
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Wei-Ching Wang: Business School, Nanfang College, Guangzhou, Guangzhou 510970, China
Chung-Hsien Lin: Department of Leisure and Recreation, National Formosa University, Huwei Township, Yunlin 632, Taiwan

Sustainability, 2024, vol. 16, issue 6, 1-17

Abstract: This study identifies the destination attributes of sustainable urban waterfronts that are frequently mentioned in tourists’ online reviews. We analyzed the influence of these attributes on tourists’ ratings based on stimuli–organism–response theory, and the associations between these destination attributes. The online reviews (both text reviews and star ratings) from TripAdvisor and Google Maps of the sustainable waterfront destinations of the Liuchuan and Luchuan rivers in Taichung city (Taiwan) were collected and analyzed through text and data mining. Destination attributes were grouped into two types: sustainable landscapes (aesthetics, water resource rehabilitation, sustainable lighting, emotional experiences, and low-impact development waterfronts) and sustainable recreational spaces (leisure activities, festivals, inclusive destinations, photography, and tourist experiences). Two destination attributes common to-- both types were identified: nightscapes and waterfronts. These attributes predicted tourists’ ratings through support vector machine analysis. Sensitivity analysis revealed that sustainable landscape-type attributes had a greater impact on tourists’ ratings than the sustainable recreational space type. In addition, three important association rules between twelve attributes were identified and these helped provide information pattern combination attributes from tourists’ comments with support and confidence for the destination attributes. These findings will contribute to urban planning and design in relation to sustainable waterfront destinations. They highlight the need for planners to consider both tourists’ landscapes and recreational needs in order to achieve economic and ecological sustainability.

Keywords: sustainable urban waterfront destinations; TripAdvisor; google maps; text mining; support vector machine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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