IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v7y2024i4p90-1539d1550837.html
   My bibliography  Save this article

Sentiment Analysis for Tourism Insights: A Machine Learning Approach

Author

Listed:
  • Kenza Charfaoui

    (Faculty of Governance, Economics and Social Sciences, Mohammed VI Polytechnic University, Rabat 11100, Morocco
    These authors contributed equally to this work.)

  • Stéphane Mussard

    (Faculty of Governance, Economics and Social Sciences, Mohammed VI Polytechnic University, Rabat 11100, Morocco
    CHROME, University of Nîmes, Avenue du Dr. Georges Salan, 30000 Nimes, France
    These authors contributed equally to this work.)

Abstract

This paper explores international tourism regarding Morocco’s leading touristic city Marrakech, and, more precisely, its two prominent public spaces, Jemaa el-Fna and the Medina. Following a web-scraping process of English reviews on TripAdvisor, a machine learning technique is proposed to gather insights into prominent topics in the data, and their corresponding sentiment with a specific voting model. This process allows decision makers to direct their focus onto certain issues, such as safety concerns, animal conditions, health, or pricing issues. In addition, the voting method outperforms Vader, a widely used sentiment prediction tool. Furthermore, an LLM (Large Language Model) is proposed, the SieBERT-Marrakech. It is a SieBERT model fine-tuned on our data. The model outlines good performance metrics, showing even better results than GPT-4o, and it may be an interesting choice for tourism sentiment predictions in the context of Marrakech.

Suggested Citation

  • Kenza Charfaoui & Stéphane Mussard, 2024. "Sentiment Analysis for Tourism Insights: A Machine Learning Approach," Stats, MDPI, vol. 7(4), pages 1-13, December.
    • Handle: RePEc:gam:jstats:v:7:y:2024:i:4:p:90-1539:d:1550837
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/7/4/90/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/7/4/90/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Krämer, Nicole & Sugiyama, Masashi, 2011. "The Degrees of Freedom of Partial Least Squares Regression," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 697-705.
    2. Hicham Saddou, 2019. "Tourisme à Marrakech; Impacts économiques, socioculturels et environnementaux éminents," Post-Print halshs-02307665, HAL.
    Full references (including those not matched with items on IDEAS)

    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. Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
    2. Jisu Yoon & Stephan Klasen, 2018. "An Application of Partial Least Squares to the Construction of the Social Institutions and Gender Index (SIGI) and the Corruption Perception Index (CPI)," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(1), pages 61-88, July.
    3. Christian Gayer & Alessandro Girardi & Andreas Reuter, 2016. "Replacing Judgment by Statistics: Constructing Consumer Confidence Indicators on the basis of Data-driven Techniques. The Case of the Euro Area," Working Papers LuissLab 16125, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    4. Barbarino, Alessandro & Bura, Efstathia, 2024. "Forecasting Near-equivalence of Linear Dimension Reduction Methods in Large Panels of Macro-variables," Econometrics and Statistics, Elsevier, vol. 31(C), pages 1-18.
    5. Florian Rohart & Benoît Gautier & Amrit Singh & Kim-Anh Lê Cao, 2017. "mixOmics: An R package for ‘omics feature selection and multiple data integration," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-19, November.
    6. Kailin Zeng & Ebenezer Fiifi Emire Atta Mills, 2023. "Can economic links explain lead–lag relations across firms?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1338-1363, April.
    7. Jan J. J. Groen & Michael Nattinger, 2020. "Alternative Indicators for Chinese Economic Activity Using Sparse PLS Regression," Economic Policy Review, Federal Reserve Bank of New York, vol. 26(4), pages 39-68, October.

    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:jstats:v:7:y:2024:i:4:p:90-1539:d:1550837. 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.