Hikers’ experience satisfaction and visual perception based on online text-image data: an empirical study of hiking trails in Malaysia
Year: 2025
Type: article
Abstract: Hiking offers a valuable pathway for understanding individual leisure satisfaction and landscape perception. This study employs textual and image data from the AllTrails platform to systematically analyze hikers’ experiences and perceptions of hiking trails in Malaysia. Natural language processing (NLP) techniques were utilized to extract hikers’ perceptual dimensions, resulting in a comprehensive 12-indicator evaluation framework across three dimensions: landscapes, service facilities and hiking experiences. Concurrently, Baidu Intelligent Cloud was employed to analyze image content, enabling comparisons and assessments of consistency between visual and textual expressions. The findings revealed that: (1) elevation, facility maintenance, safety, difficulty and challenge, and social experience significantly influence satisfaction; (2) discrepancies exist between visual and textual expressions. Vegetation dominated the images, whereas geological landscapes and waterscape features were emphasized in textual descriptions. Additionally, waterscapes showed the highest consistency between text and image data, while sky-related features and geological landscapes demonstrated moderate consistency; (3) a clear mismatch was identified between visual perception and overall experience satisfaction, indicating that hikers’ satisfaction depends not only on visually appealing landscapes but also critically on psychological fulfillment, social interactions and functional needs during hikes. These insights provide theoretical contributions and practical guidance for enhancing hiking trail planning, design and management.
Source: Cogent Social Sciences
Institutions: Huaihua University, Universiti Sains Malaysia
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,165
Funder: Huaihua University
OpenAlex ID: W4417455799