Measuring Satisfaction and Loyalty of Guests Based on Vietnamese Hotel Online Reviews

Measuring customer satisfaction is a key task for hotels today. Analyzing online reviews of experienced guests will help the managers to know if guests are satisfied or dissatisfied with the service that they provided. Hence, they have solutions to improve service quality. This study presents a method to measure guest satisfaction based on sentiment lexicon that is developed for hospitality domain to increase the accuracy of the analysis results. Actual data is downloaded from TripAdvisor with 35 four-star to five-star hotels of five cities in Vietnam to analyze guest satisfaction that shows that nearly 80% of customers are satisfied with the quality of Vietnamese hotels. Based on data analysis, the study also evaluating guest loyalty through phrases like “came here several,” “come back,” “recommend,” etc. This rate corresponds to the number that was reported by the Vietnam National Administration of Tourism.

[1]  Alessandro Bigi,et al.  Measuring Hotel Service Quality from Online Consumer Reviews: A Proposed Method , 2014, ENTER.

[2]  Hei-Chia Wang,et al.  Use of multi-lexicons to analyse semantic features for summarization of touring reviews , 2019, Electron. Libr..

[3]  Ji Zhang,et al.  A tourism destination recommender system using users’ sentiment and temporal dynamics , 2018, Journal of Intelligent Information Systems.

[4]  Changhee Kim,et al.  Measuring Customer Satisfaction and Hotel Efficiency Analysis: An Approach Based on Data Envelopment Analysis , 2020, Cornell Hospitality Quarterly.

[5]  S. Becken,et al.  Sentiment Analysis in Tourism: Capitalizing on Big Data , 2019 .

[6]  Luo Yonglong,et al.  Research on Sentiment Classification of Online Travel Review Text , 2020, Applied Sciences.

[7]  Yong Liu,et al.  Comprehending customer satisfaction with hotels , 2020 .

[8]  Yaxin Bi,et al.  Improved lexicon-based sentiment analysis for social media analytics , 2015, Security Informatics.

[9]  Zulkarnain,et al.  Opinion Mining on Mandalika Hotel Reviews Using Latent Dirichlet Allocation , 2019 .

[10]  Mehmet Eryilmaz,et al.  A Discussion on the Relationship Between Information and Communication Technologies (ICT) and Entrepreneurship , 2019, Int. J. E Entrepreneurship Innov..

[11]  Aiman Moyaid Said,et al.  Hotel Reviews Analysis based on Sentiment Classification: Oman Case Study , 2020, ICCTA.

[12]  Z. Schwartz,et al.  What can big data and text analytics tell us about hotel guest experience and satisfaction , 2015 .

[13]  Yong Shi,et al.  Improved New Word Detection Method Used in Tourism Field , 2017, ICCS.

[14]  Svetlana Stepchenkova,et al.  Automated Sentiment Analysis in Tourism: Comparison of Approaches , 2018 .

[15]  F. Okumus,et al.  Understanding Satisfied and Dissatisfied Hotel Customers: Text Mining of Online Hotel Reviews , 2016 .

[16]  Yong Shi,et al.  Build a Tourism-Specific Sentiment Lexicon Via Word2vec , 2018 .

[17]  Ruwan Jayathilaka,et al.  The impact of online reviews on inbound travellers’ decision making , 2020 .

[18]  Xiaoyu Li,et al.  Comparison of Machine Learning Models for Sentimental Analysis of Hotel Reviews , 2020 .

[19]  R. Law,et al.  Determinants of hotel guests’ satisfaction from the perspective of online hotel reviewers , 2019, International Journal of Culture, Tourism and Hospitality Research.

[20]  Yeamduan Narangajavana Kaosiri,et al.  User-Generated Content Sources in Social Media: A New Approach to Explore Tourist Satisfaction: , 2019 .

[21]  El haouta Imane Social big data analysis of Five Star hotels : A case study of hotel guest experience and satisfaction in Marrakech , 2019 .

[22]  Carmela Iorio,et al.  Mining big data in tourism , 2020, Quality & Quantity.

[23]  Walaa Medhat,et al.  Sentiment analysis algorithms and applications: A survey , 2014 .

[24]  Aitor García,et al.  A Lexicon based sentiment analysis retrieval system for tourism domain. , 2012, ICIT 2012.

[25]  José Alberto Martínez-González,et al.  Drivers of the formation of e-loyalty towards tourism destinations , 2018 .

[26]  Carlos Angel Iglesias,et al.  A semantic similarity-based perspective of affect lexicons for sentiment analysis , 2019, Knowl. Based Syst..

[27]  Mirna Adriani,et al.  Sentiment Lexicon Generation for an Under-Resourced Language , 2014, Int. J. Comput. Linguistics Appl..

[28]  Javier M. Moguerza,et al.  Business information architecture for successful project implementation based on sentiment analysis in the tourist sector , 2019, Journal of Intelligent Information Systems.

[29]  Zhanhuai Li,et al.  Constructing domain-dependent sentiment dictionary for sentiment analysis , 2020, Neural Computing and Applications.