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A study on online travel reviews through intelligent data analysis

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  • Michela Fazzolari

    (Institute of Informatics and Telematics, National Research Council)

  • Marinella Petrocchi

    (Institute of Informatics and Telematics, National Research Council)

Abstract

The purpose of this paper is to show the application of a set of intelligent data analysis techniques to about 7 million of online travel reviews, with the aim of automatically extracting useful information. The reviews, collected from two popular online tourism-related review platforms, are all those posted by reviewers about one specific Italian location, from 2010 to 2017. To carry out the study, the following methodology is applied: a preliminary statistical analysis is performed to acquire general knowledge about the datasets, such as the geographical distribution of reviewers, their activities, and a comparison among the time of visits and the average scores of the reviews. Then, Natural Language Processing techniques are applied to extract and compare the most frequent words used in the two platforms. Finally, an Association Rule Learning algorithm is applied, to extract preferred destinations for distinct groups of reviewers, by mining interesting associations among the countries of origin of the reviewers and the most frequent destinations visited. By elaborating the available data, it is possible to automatically disclose valuable information for consumers and providers. The information automatically extracted can be exploited, for example, to build a recommender system for customers or a market analysis tool for service providers.

Suggested Citation

  • Michela Fazzolari & Marinella Petrocchi, 2018. "A study on online travel reviews through intelligent data analysis," Information Technology & Tourism, Springer, vol. 20(1), pages 37-58, December.
    • Handle: RePEc:spr:infott:v:20:y:2018:i:1:d:10.1007_s40558-018-0121-z
    DOI: 10.1007/s40558-018-0121-z
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    Cited by:

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    3. Arpan Kumar Kar & Sunil Kumar & P. Vigneswara Ilavarasan, 2021. "Modelling the Service Experience Encounters Using User-Generated Content: A Text Mining Approach," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(4), pages 267-288, December.
    4. Işkın Merve & Prentice Catherine & Eker Nuray & Şengel Ümit, 2024. "Understanding the Effects of Reservation Systems and Online Transaction Capacity on the Competitiveness of Travel Agencies," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 14(1), pages 55-70.
    5. Min-Pei Lin & Estela Marine-Roig & Nayra Llonch-Molina, 2022. "Gastronomy Tourism and Well-Being: Evidence from Taiwan and Catalonia Michelin-Starred Restaurants," IJERPH, MDPI, vol. 19(5), pages 1-24, February.
    6. Ferreira Ana & Correia Marisol B. & Renda Ana Isabel, 2024. "Systematic Literature Review on the Profile of Tourists in Four- and Five-star Hotels Based on Online Reviews," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 14(1), pages 20-34.
    7. Jian-Wu Bi & Tian-Yu Han & Yanbo Yao & Hui Li, 2022. "Ranking hotels through multi-dimensional hotel information: a method considering travelers’ preferences and expectations," Information Technology & Tourism, Springer, vol. 24(1), pages 127-155, March.
    8. Archana Yadav & Biswajit Patra & Tanmay Basu, 2024. "Modeling International Tourist Arrivals: An NLP Perspective," SN Operations Research Forum, Springer, vol. 5(4), pages 1-19, December.
    9. Cristina Figueroa-Domecq & Jesús Palomo & Mª Dolores Flecha-Barrio & Mónica Segovia-Pérez, 2020. "Technology double gender gap in tourism business leadership," Information Technology & Tourism, Springer, vol. 22(1), pages 75-106, March.

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