Study on the Hotel Customer Satisfaction Evaluation System——Based on Hangzhou High Star Hotel Reviews Data from Tripadvisor.com
- DOI
- 10.2991/aebmr.k.191217.138How to use a DOI?
- Keywords
- Online Reviews, Evaluation System, Customer Satisfaction
- Abstract
In recent years, with the rapid development and popularization of the mobile Internet, online booking and purchasing of products has become a consumption habit. In order to better meet the needs of consumers, many online booking and purchasing platforms provide consumers with a free and anonymous evaluation model of products. Based on this, a huge amount of product evaluation information has been formed on major booking platforms. This paper uses the review information of Hangzhou high-star hotel in the third-party platform of Tripadvisor.com as the data source of Hangzhou customer satisfaction evaluation system, and using the method text analysis on big data to sort and analyze the collected hotel review information. Then, the Bayesian network model is used to construct the structural learning and parameter learning for each index to determine the factors affecting customer satisfaction. The research shows that the customer satisfaction evaluation system based on the online comment text is more accurate and scientific than the traditional one, which is beneficial to the hotel’s accurate evaluation of customer satisfaction and customer demand identification.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Miao Zhangwei PY - 2019 DA - 2019/12/20 TI - Study on the Hotel Customer Satisfaction Evaluation System——Based on Hangzhou High Star Hotel Reviews Data from Tripadvisor.com BT - Proceedings of the 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019) PB - Atlantis Press SP - 771 EP - 778 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.191217.138 DO - 10.2991/aebmr.k.191217.138 ID - Zhangwei2019 ER -