Page 171 - Kỷ yếu hội thảo khoa học lần thứ 12 - Công nghệ thông tin và Ứng dụng trong các lĩnh vực (CITA 2023)

Linh Bui Khanh, Anh Nguyen Quynh, Hai Tran Van, Ha Nguyen Thi Thu 155

tourist destinations. Vietnam is a country rated by tourism organizations as the 3rd most
attractive destination for tourists in Southeast Asia and in the top 30 most attractive
destinations in the world. The contribution of tourism sector to the national GDP
accounts for about 8%, and the contribution of accommodation services to the tourism
industry accounts for more than 70%, which shows the importance of the tourism sector
in which including hotels industry for the country economic development.
Understanding customer emotions to help improve the quality of hotel services,
increase guest satisfaction and increase customer return rate is important for the hotel
industry in Vietnam today.
In the context of the explosion of online booking platforms, understanding the
psychology and emotions of customers is an important task for hotels industry, which
makes hotel management become more effective by mining customer-generated
content.
This study applied an LSTM model to measure and predict customers' sentiments
through their reviews. We collected data from TripAdvisor by us, performed
preprocessing, and used LSTM to predict with high accuracy. This is showing that the
model is suitable for predicting customer sentiment with Vietnamese hotel services. It
has practical significance in developing the hotel industry in Vietnam.

References

1. Akhtar, Nadeem, et al. "Aspect based sentiment oriented summarization of hotel
reviews." Procedia computer science 115 (2017): 563-571.
2. El-Said, Osman Ahmed. "Impact of online reviews on hotel booking intention: The
moderating role of brand image, star category, and price." Tourism Management
Perspectives 33 (2020): 100604
3. Kim, Molan, et al. "Impact of visual information on online consumer review behavior:
Evidence from a hotel booking website." Journal of Retailing and Consumer Services 60
(2021): 102494.
4. Masiero, Lorenzo, and Juan L. Nicolau. "Choice behaviour in online hotel
booking." Tourism Economics 22.3 (2016): 671-678.
5. Dong, Jian, Hongxiu Li, and Xianfeng Zhang. "Classification of customer satisfaction
attributes: An application of online hotel review analysis." Conference on e-Business, e-
Services and e-Society. Springer, Berlin, Heidelberg, 2014.
6. Li, Hongxiu, et al. "Comprehending customer satisfaction with hotels: Data analysis of
consumer-generated reviews." International Journal of Contemporary Hospitality
Management (2020).
7. Padma, Panchapakesan, and Jiseon Ahn. "Guest satisfaction & dissatisfaction in luxury
hotels: An application of big data." International Journal of Hospitality Management 84
(2020): 102318.
8. Padma, P., & Ahn, J. (2020). Guest satisfaction & dissatisfaction in luxury hotels: An
application of big data. International Journal of Hospitality Management, 84, 102318.
9.
of user-generated content for Slove
management." Management: Journal of Contemporary Management Issues 23.1 (2018): 29-
57.

ISBN: 978-604-80-8083-9 CITA 2023