IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i8p3603-d1636103.html

A Deep Learning-Based Analysis of Customer Concerns and Satisfaction: Enhancing Sustainable Practices in Luxury Hotels

Author

Listed:
  • Tiantian Pang

    (School of Management, Zhengzhou University, No. 100 Science Avenue, Gaoxin District, Zhengzhou 450001, China)

  • Juan Liu

    (School of Management, Zhengzhou University, No. 100 Science Avenue, Gaoxin District, Zhengzhou 450001, China)

  • Li Han

    (School of Management, Zhengzhou University, No. 100 Science Avenue, Gaoxin District, Zhengzhou 450001, China)

  • Haiyan Liu

    (School of Management, Zhengzhou University, No. 100 Science Avenue, Gaoxin District, Zhengzhou 450001, China)

  • Dan Yan

    (School of Management, Zhengzhou University, No. 100 Science Avenue, Gaoxin District, Zhengzhou 450001, China)

Abstract

Hotels are one of the fastest-growing sectors in the tourism industry, and sentiment analysis plays a vital role in improving business performance and supporting sustainable practices. This paper proposes a novel framework combining topic mining and aspect-based sentiment analysis to examine 29,334 hotel reviews in Henan province in China, with the aim of informing strategies for sustainable hotel development. Our results reveal six key attributes of customer concern, particularly emphasizing family experiences, which reflect Henan’s appeal as a family tourism destination. Additionally, we uncover sentiment quadruples, including categories, aspect terms, opinion terms, and polarities, thus enabling a dual-dimensional evaluation of factors influencing customer satisfaction. The results reveal that service mainly influences overall category-level satisfaction, while bed, front desk, and breakfast primarily drive aspect-level satisfaction. This study provides valuable insights into customer feedback, offering empirical support for optimizing services and guiding the sustainable strategic development of regional hotels.

Suggested Citation

  • Tiantian Pang & Juan Liu & Li Han & Haiyan Liu & Dan Yan, 2025. "A Deep Learning-Based Analysis of Customer Concerns and Satisfaction: Enhancing Sustainable Practices in Luxury Hotels," Sustainability, MDPI, vol. 17(8), pages 1-23, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3603-:d:1636103
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/8/3603/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/8/3603/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Zhenshan & Cai, Jianming, 2016. "Do regional factors matter? Determinants of hotel industry performance in China," Tourism Management, Elsevier, vol. 52(C), pages 242-253.
    2. Thakur, Rakhi, 2018. "Customer engagement and online reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 41(C), pages 48-59.
    3. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sareeya Wichitsathian & Sumalee Ekkaphol, 2025. "The Mediating Role of Sustainable Competitive Advantage: A Comparative Study of Disaggregated vs. Holistic Models in Green Hotels," Sustainability, MDPI, vol. 17(19), pages 1-31, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. M. Narciso, 2022. "The Unreliability of Online Review Mechanisms," Journal of Consumer Policy, Springer, vol. 45(3), pages 349-368, September.
    2. Gül Yazıcı & Tuğçe Ozansoy Çadırcı, 2024. "Creating meaningful insights from customer reviews: a methodological comparison of topic modeling algorithms and their use in marketing research," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 865-887, December.
    3. Young Joon Park & Jaewoo Joo & Charin Polpanumas & Yeujun Yoon, 2021. "“Worse Than What I Read?” The External Effect of Review Ratings on the Online Review Generation Process: An Empirical Analysis of Multiple Product Categories Using Amazon.com Review Data," Sustainability, MDPI, vol. 13(19), pages 1-22, September.
    4. Wang, Binni & Wang, Pong & Tu, Yiliu, 2021. "Customer satisfaction service match and service quality-based blockchain cloud manufacturing," International Journal of Production Economics, Elsevier, vol. 240(C).
    5. Joesri Mohamad Saber & Kardina Kamaruddin, 2025. "A Bibliometric Analysis of Training, Competencies, and Job Readiness in the Hotel and Tourism Industry: Trends and Future Directions," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(14), pages 282-297, February.
    6. Jiacong Wu & Yu Wang & Ru Zhang & Jing Cai, 2018. "An Approach to Discovering Product/Service Improvement Priorities: Using Dynamic Importance-Performance Analysis," Sustainability, MDPI, vol. 10(10), pages 1-26, October.
    7. Shuyue Huang & Lena Jingen Liang & Hwansuk Chris Choi, 2022. "How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    8. Carmela Iorio & Giuseppe Pandolfo & Antonio D’Ambrosio & Roberta Siciliano, 2020. "Mining big data in tourism," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(5), pages 1655-1669, December.
    9. Mohamed M. Mostafa, 2023. "A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3905-3935, August.
    10. Llorens-Marin Miguel & Hernandez Adolfo & Puelles-Gallo Maria, 2025. "Decoding hotel reviewers: Insights from a decision tree analysis," Management & Marketing, Sciendo, vol. 20(2), pages 81-92.
    11. Liu, Xiao & Li, Ming-Yang & Yuan, Duo-Ning, 2026. "Analyzing consumer satisfaction using Interpretive Structural Modeling driven by online reviews: An integrated approach," Journal of Business Research, Elsevier, vol. 205(C).
    12. Ian Sutherland & Kiattipoom Kiatkawsin, 2020. "Determinants of Guest Experience in Airbnb: A Topic Modeling Approach Using LDA," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    13. Omer Unsal & M. Kabir Hassan, 2025. "Employee treatment and firm performance: evidence from topic modelling in lawsuit announcements," Review of Quantitative Finance and Accounting, Springer, vol. 65(1), pages 1-38, July.
    14. Yongrong Xin & Muhammad Irfan & Bilal Ahmad & Madad Ali & Lanqi Xia, 2023. "Identifying How E-Service Quality Affects Perceived Usefulness of Online Reviews in Post-COVID-19 Context: A Sustainable Food Consumption Behavior Paradigm," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    15. Ren, Xiangming & Fu, Xiaorong & Luan, Rui & Bai, Lifei, 2025. "The impact of relationship strategy on customer engagement in paid membership programs," Journal of Retailing and Consumer Services, Elsevier, vol. 87(C).
    16. Gao, Mengjia & Huang, Lin, 2021. "Quality of channel integration and customer loyalty in omnichannel retailing: The mediating role of customer engagement and relationship program receptiveness," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    17. Hai Dong & Nicolas Peypoch & Linjia Zhang, 2020. "Do contextual factors matter? Evidence from Chinese hotel productivity with heterogeneity," Tourism Economics, , vol. 26(2), pages 257-275, March.
    18. Agrawal, Shiv Ratan & Mittal, Divya, 2022. "Optimizing customer engagement content strategy in retail and E-tail: Available on online product review videos," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    19. Boccali, Filippo & Mariani, Marcello M. & Visani, Franco & Mora-Cruz, Alexandra, 2022. "Innovative value-based price assessment in data-rich environments: Leveraging online review analytics through Data Envelopment Analysis to empower managers and entrepreneurs," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    20. Yang Li & Junrui Zhang & Miao Feng & Haoran Si, 2026. "Dynamic interaction effects between online reviews and spoilers: a PVAR approach," Electronic Commerce Research, Springer, vol. 26(1), pages 297-317, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3603-:d:1636103. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.