Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/96547
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Title: Topic modelling for ski resorts : an analysis of experience attributes and seasonality
Authors: Shang, Z
Luo, JM
Kong, A 
Issue Date: Mar-2022
Source: Sustainability, Mar. 2022, v. 14, no. 6, 3533
Abstract: Knowing how to improve skiers’ experiences in ski resorts is vital for developing the ski industry. This study aims to provide a holistic understanding of the key attributes of skiers’ experiences and explore them in the context of seasonality. Based on the user-generated content of 14 ski resorts and the topic modelling and sentiment analysis method, a framework of skiing experience attributes was built. Compared with the seasonal data, the dynamic of skiers’ concerns and perceived performance was revealed. The skiers’ concerns in peak seasons and off seasons manifested different orientations. The results show that the relatively important attributes tend to have relatively low performance in the peak seasons. In off seasons, skiers emphasise non-skiing-oriented attributes. This study showcases that skier’s interests and evaluations of various experience attributes vary with seasons. The findings help to understand the skiers’ peak and supporting experiences, which could be used to build ski resorts management and seasonal hedging strategies.
Keywords: Importance-performance analysis
Seasonality
Ski resort experience
Topic modelling
User-generated content
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Sustainability 
EISSN: 2071-1050
DOI: 10.3390/su14063533
Rights: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
The following publication Shang, Z., Luo, J. M., & Kong, A. (2022). Topic Modelling for Ski Resorts: An Analysis of Experience Attributes and Seasonality. Sustainability, 14(6), 3533 is available at https://doi.org/10.3390/su14063533.
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