Comparison of Tourist Satisfaction Characteristics Based on Text Mining
Take Yunnan Minzu Village and Stone Forest Scenic Area as Examples
- https://doi.org/10.2991/assehr.k.220504.021How to use a DOI?
- Text Mining; Tourist Reviews; Tourist Satisfaction; Semantic Web; IPA Analysis; Energy
In order to promote the goal of building a number of world-class tourist attractions mentioned in the 14th Five-Year Plan and the 2035 Vision of Yunnan Province, and to promote the recovery of Yunnan’s tourism industry, which has been hit by the new crown epidemic, this article uses the text based on tourists’ online reviews. The mining method compares and analyzes the characteristics of tourists’ satisfaction with two different types of scenic spots in Yunnan Minzu Village and Stone Forest Scenic Area from three aspects: high-frequency word analysis, semantic network analysis and sentiment analysis, and analyzes tourists’ satisfaction with the two different types of scenic spots. The difference, combined with the emotional information of tourists’ comments, was used to compare the tourism experience value of the two scenic spots by using the importance-representation analysis method. Finally, the field investigation verifies the analysis results, and puts forward a series of feasible suggestions for the planning, construction and operation management of these two scenic spots and other scenic spots of the same type in Yunnan Province.
- © 2022 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license.
Cite this article
TY - CONF AU - Xingkai Wen AU - Xinghao Jin AU - Liping Che AU - Jiaying Liu AU - Jun Lu PY - 2022 DA - 2022/06/01 TI - Comparison of Tourist Satisfaction Characteristics Based on Text Mining BT - Proceedings of the 2022 8th International Conference on Humanities and Social Science Research (ICHSSR 2022) PB - Atlantis Press SP - 108 EP - 115 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.220504.021 DO - https://doi.org/10.2991/assehr.k.220504.021 ID - Wen2022 ER -