Improving guest satisfaction by identifying hotel service micro-elements failures through Deep Learning of online reviews
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Published
21-03-2025
Sergey Kazakov
Pedro Cuesta-Valiño
Vera Butkouskaya
Daniel Muravsky
https://orcid.org/0000-0001-5532-5791
Pedro Cuesta-Valiño
Vera Butkouskaya
Daniel Muravsky
Abstract
This study thoroughly examines often-overlooked micro-service elements within the broader spectrum of hotel services, aiming to improve hospitality services and ensure guest satisfaction. To achieve this, this research developed a methodological framework, integrating (a) the VADER text sentiment analysis framework, (b) a robust logistic regression procedure to pinpoint specific hotel service components culprit for guest frustration, and (c) the application of semantic network analysis to yield guest insights contextualised within the realm of underperforming hotel service micro-elements.
Research findings highlight fifty specific service micro-elements identified as triggers of negative sentiment and subsequent degrees of diminished guest satisfaction. Furthermore, this study zooms into the top ten underperforming service micro-elements by employing semantic network analysis to uncover the roots of typical guest frustrations with their hotel experiences. Though identified within hotel reviews, certain service malfunctions have relevance within the broader domain of destination management.
The outcomes of this study suggest a valuable resource for managers in detecting and rectifying inadequately performing hotel service micro-elements, which are pivotal for elevating guest satisfaction within their respective hotel properties. Additionally, the findings provide impetus for hotel and destination managers to implement tailored strategies to increase guest satisfaction across hotels and destinations.
How to Cite
Kazakov, . S., Cuesta-Valiño, P., Butkouskaya, V., & Muravsky, . D. (2025). Improving guest satisfaction by identifying hotel service micro-elements failures through Deep Learning of online reviews. Cuadernos De Gestión, 25(1), 71–88. https://doi.org/10.5295/cdg.242191sk
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Keywords
hotel service elements, online reviews, natural language processing, big data, tourist satisfaction policy, eWOM
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Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding satisfied and dissatisfied hotel custom-ers: Text mining of online hotel reviews. Journal of Hospitality Marketing and Management, 25(1), 1-24. https://doi.org/10.1080/19368623.2015.983631
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Chang, Y. C., Ku, C. H., & Chen, C. H. (2020). Using deep learning and visual analytics to explore hotel reviews and responses. Tourism Management, 80, 104129. https://doi.org/10.1016/j.tourman.2020.104129
Chatterjee, S. (2020). Drivers of helpfulness of online hotel reviews: A sentiment and emotion mining approach. International Journal of Hospitality Management, 85, 102356. https://doi.org/10.1016/j.ijhm.2019.102356
Cooksey, R. W. (2020). Illustrating statistical procedures: Finding meaning in quantitative data. Springer Nature. https://doi.org/10.1007/978-981-15-2537-7_5
Cuesta-Valiño, P., Bolifa, F., & Núñez-Barriopedro, E. (2020). Sustainable, smart and Muslim-friendly tourist destina-tions. Sustainability, 12(5), 1778. https://doi.org/10.3390/su12051778
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
Henche, B. G., Salvaj, E., & Cuesta-Valiño, P. (2020). A sustainable management model for cultural creative tourism ecosystems. Sustainability, 12(22), 9554. https://doi.org/10.3390/su12229554
Geetha, M., Singha, P., & Sinha, S. (2017). Relationship between customer sentiment and online customer ratings for hotels-An empirical analysis. Tourism Management, 61, 43-54. https://doi.org/10.1016/j.tourman.2016.12.022
Hu, F., Li, H., Liu, Y., & Teichert, T. (2020). Optimizing service offerings using asymmetric impact-sentiment-performance analysis. International Journal of Hospitality Management, 89, 102557. https://doi.org/10.1016/j.ijhm.2020.102557
Hu, M., & B, L. (2004). Mining opinion features in customer reviews. In Proceedings of AAAI Conference on Artificial Intelligence, 4, 755-760.
Hu, X., & Yang, Y. (2021). What makes online reviews helpful in tourism and hospitality? A bare-bones meta-analysis. Journal of. Hospitality Marketing and Management, 30(2), 139-158. https://doi.org/10.1080/19368623.2020.1780178
Huang, M., Xie, H., Rao, Y., Feng, J., & Wang, F. L. (2020). Sentiment strength detection with a context-dependent lexi-con-based convolutional neural network. Information Sciences, 520, 389-399. https://doi.org/10.1016/j.ins.2020.02.026
Huang, S., Liang, L. J., & Choi, H. C. (2022). How we failed in context: A text-mining approach to understanding hotel service failures. Sustainability, 14(5), 2675.
Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216-225. https://doi.org/10.1609/icwsm.v8i1.14550
Israeli, A. A., Lee, S. A., & Bolden III, E. C. (2019). The impact of escalating service failures and internet addiction be-havior on young and older customers' negative eWOM. Journal of Hospitality and Tourism Management, 39, 150-157. https://doi.org/10.1016/j.jhtm.2019.04.006
Jang, S., & Lee, J. (2015). The Impact of Hotel Attributes on Online Hotel Reviews: An Analysis of Reviewer Comments. Journal of Hospitality Marketing & Management, 24(3), 289-312.
Khan, F. M., Khan, S. A., Shamim, K., Gupta, Y., & Sherwani, S. I. (2022). Analysing customers' reviews and ratings for online food deliveries: A text mining approach. International Journal of Consumer Studies, 1, 24. https://doi.org/10.1111/ijcs.12877
Kim, J., & Im, J. (2018). Proposing a missing data method for hospitality research on online customer reviews: An application of imputation approach. International Journal of Contemporary Hospitality Management, 30(11), 3250-3267. https://doi.org/10.1108/IJCHM-10-2017-0708
Kirilenko, A. P., Stepchenkova, S. O., Kim, H., & Li, X. (2018). Automated sentiment analysis in tourism: Comparison of approaches. Journal of Travel Research, 57(8), 1012-1025. https://doi.org/10.1177/0047287517729757
Kuruuzum, A., & Koksal, C. D. (2010). The impact of service quality on behavioral intention in hospitality industry. Inter-national journal of. Business and Management Studies, 2(1), 9-15.
Latinopoulos, D. (2020). Analysing the role of urban hotel location in guests' satisfaction. Anatolia, 31(4), 636-650. https://doi.org/10.1080/13032917.2020.1808489
Le, T. D., Robinson, L. J., & Dobele, A. R. (2023). eWOM processing from receiver perspective: Conceptualising the relationships. International Journal of Consumer Studies, 47(1), 434-450. https://doi.org/10.1111/ijcs.12864
Lee, B. Y., & Park, S. Y. (2019). The role of customer delight and customer equity for loyalty in upscale hotels. Journal of Hospitality and Tourism Management, 39, 175-184. https://doi.org/10.1016/j.jhtm.2019.04.003
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