Analysis of Online Reviews of Tourists in 5A Scenic Spots Located in Sichuan Province based on Text Mining
- DOI
- 10.2991/978-94-6463-042-8_205How to use a DOI?
- Keywords
- text mining; online reviews; sentiment analysis
- Abstract
How to efficiently process and analyze online reviews of tourists has become one of the key issues for tourism operators today. This helps to identify user concerns and emotional tendencies to a large extent. More importantly, the knowledge gained from data mining can be used to improve the services of the tourist attractions and optimize the quality of the services. In this paper, we use natural language processing technology to analyze online reviews of tourists in 5A scenic spots of Sichuan Province, and determine the positive and negative polarity of tourists’ online reviews. Through text mining of tourists’ online reviews, the concerns and tendencies of tourists in the process of touring are analyzed. In this way, relevant suggestions and measures are proposed to scenic spot managers and tourists in order to optimize scenic spot services and improve the accuracy of tourists’ tourism decisions.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Qinglin Luo AU - Sheng Zhong AU - Zhaocai Li PY - 2022 DA - 2022/12/29 TI - Analysis of Online Reviews of Tourists in 5A Scenic Spots Located in Sichuan Province based on Text Mining BT - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022) PB - Atlantis Press SP - 1412 EP - 1416 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-042-8_205 DO - 10.2991/978-94-6463-042-8_205 ID - Luo2022 ER -