Assessment of Hotel Guest Satisfaction Using Sentiment Analysis: A Case Study of Maldives Hotels


  • Hauwa’u Uraifa Shehu School of Science, Engineering and Environment, University of Salford Manchester, United Kingdom
  • A.F. Donfack Kana Department of Computer Science, Ahmadu Bello University, Zaria
  • Fatima Sulaiman Department of Computer Science Ahmadu Bello University Zaria, Nigeria.


text mining, sentiment analysis, Guest Satisfaction


Nowadays online reviews by hotel customers greatly influence business as potential new consumers seek unbiased information while making their hotel booking decisions. Hotel management and marketers are more aware of the impact of online reviews on financial performance. This awareness arises from the universal consensus that internet consumer reviews have a significant impact on hotel business performance. Customers use social media to share information about products and services, and online reviews have a substantial influence on customer purchasing decisions. The goal of this study is to provide formative assessment feedback on Maldives hotels using word cloud technique. This  include investigating the hotel that is mostly used by guests, finding out the percentage of positive and negative comments made about the hotel, and also assessing the type of comments the majority of customers give about the services rendered to them. Data from 104 distinct Maldives hotels were utilized in this case study to provide quick visual insight using a word cloud approach with R programming language. The result shows that, more than 80% of the comments are positive, implying that the vast majority of these hotels' customers are pleased with their accommodations and services.

text mining, sentiment analysis,Guest Satisfaction


Anderson, C. (2012). The impact of social media on lodging performance.

Camacho, D., Panizo-LLedot, A., Bello-Orgaz, G., Gonzalez-Pardo, A., & Cambria, E. (2020). The four dimensions of social network analysis: An overview of research methods, applications, and software tools. Information Fusion, 63, 88–120.

Chakraborty, G., Pagolu, M., & Garla, S. (2013). Text mining and analysis. In Practical methods, examples, and case studies using SAS. Cary: SAS Institute Inc.

Chaovalit, P., & Zhou, L. (2005). Movie review mining: A comparison between supervised and unsupervised classification approaches. Proceedings of the 38th Annual Hawaii International Conference on System Sciences, 112c-112c.

Chittiprolu, V., Samala, N., & Bellamkonda, R. S. (2021). Heritage hotels and customer experience: a text mining analysis of online reviews. International Journal of Culture, Tourism and Hospitality Research.

Coulter, K. S., & Roggeveen, A. (2012). “Like it or not”: Consumer responses to word‐of‐mouth communication in on‐line social networks. Management Research Review.

DeMers, J. (2015). How important are customer reviews for online marketing? Forbes, Http://Www. Forbes. Com/Sites/Jaysondemers/2015/12/28/How-Important-Are-Customer-Reviews-for-Online-Marketing.

Ding, K., Choo, W. C., Ng, K. Y., Ng, S. I., & Song, P. (2021). Exploring sources of satisfaction and dissatisfaction in Airbnb accommodation using unsupervised and supervised topic modeling. Frontiers in Psychology, 12.

Escandon-Barbosa, D., & Salas-Paramo, J. (2021). Tourism Amidst COVID-19: consumer experience in luxury hotels booked through digital platforms. Tourism Recreation Research, 1–6.

Gao, S., Hao, J., & Fu, Y. (2015). The application and comparison of web services for sentiment analysis in tourism. 2015 12th International Conference on Service Systems and Service Management (ICSSSM), 1–6.

Goslee, S. C., & Urban, D. L. (2007). The ecodist package for dissimilarity-based analysis of ecological data. Journal of Statistical Software, 22(7), 1–19.

Hatzivassiloglou, V., & McKeown, K. (1997). Predicting the semantic orientation of adjectives. 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, 174–181.

Joo, Y.-H., Kim, Y., & Yang, S.-J. (2011). Valuing customers for social network services. Journal of Business Research, 64(11), 1239–1244.

Kennedy, A., & Inkpen, D. (2006). Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence, 22(2), 110–125.

Ko, C.-H. (2021). Exploring Social Media Management On Hotel Performance. International Journal of Organizational Innovation, 14(2).

Li, H., Ye, Q., & Law, R. (2013). Determinants of customer satisfaction in the hotel industry: An application of online review analysis. Asia Pacific Journal of Tourism Research, 18(7), 784–802.

Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167.

Lu, Y., & Zheng, Q. (2021). Twitter public sentiment dynamics on cruise tourism during the COVID-19 pandemic. Current Issues in Tourism, 24(7), 892–898.

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135.

Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. ArXiv Preprint Cs/0205070.

Pejic-Bach, M., Bertoncel, T., Meško, M., & Krstić, Ž. (2019). Text mining of industry 4.0 job advertisements. International Journal of Information Management.

Raut, V. B., & Londhe, D. D. (2014). Opinion mining and summarization of hotel reviews. 2014 International Conference on Computational Intelligence and Communication Networks, 556–559.

Sparks, B. A., & Browning, V. (2010). Complaining in cyberspace: The motives and forms of hotel guests’ complaints online. Journal of Hospitality Marketing & Management, 19(7), 797–818.

Torres, E. N., Fu, X., & Lehto, X. (2014). Examining key drivers of customer delight in a hotel experience: A cross-cultural perspective. International Journal of Hospitality Management, 36, 255–262.

Venkatachalam, S. (2017). An exploratory study on the building information modeling adoption in United Arab Emirates municipal projects- current status and challenges. MATEC Web of Conferences, 120, 02015.

Xiang, Z., Schwartz, Z., Gerdes Jr, J. H., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction? International Journal of Hospitality Management, 44, 120–130.

Xie, X., Fu, Y., Jin, H., Zhao, Y., & Cao, W. (2019). A novel text mining approach for scholar information extraction from web content in Chinese. Future Generation Computer Systems.

Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60(2), 617–663.