IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-14343-9_1.html
   My bibliography  Save this book chapter

A Method for Analysing Large-Scale UGC Data for Tourism: Application to the Case of Catalonia

In: Information and Communication Technologies in Tourism 2015

Author

Listed:
  • Estela Marine-Roig

    (Rovira i Virgili University)

  • Salvador Anton Clave

    (Rovira i Virgili University)

Abstract

In recent years, many articles have been published about the study of user-generated content (UGC) data in the domains of tourism and hospitality, in particular concerning quantitative and qualitative content analysis of travel blogs and online travel reviews (OTR). In general, researchers have worked on more or less population-representative samples of travel diaries, of tens or hundreds of files, which enables their manual processing. However, due to their dramatic growth, especially in the case of hospitality OTRs, this article proposes a method for semi-automatic downloading, arranging, cleaning, debugging, and analysing large-scale travel blog and OTR data. The main goal is to classify the collected webpages by dates and destinations and to be able to perform offline content analysis of the written text as provided by the author. This methodology is applied to analyse about 85,000 diaries of tourists who visited Catalonia between 2004 and 2013, and significant results are obtained in terms of content analysis.

Suggested Citation

  • Estela Marine-Roig & Salvador Anton Clave, 2015. "A Method for Analysing Large-Scale UGC Data for Tourism: Application to the Case of Catalonia," Springer Books, in: Iis Tussyadiah & Alessandro Inversini (ed.), Information and Communication Technologies in Tourism 2015, edition 127, pages 3-17, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-14343-9_1
    DOI: 10.1007/978-3-319-14343-9_1
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Cheng Chai & Yao Song & Zhenzhen Qin, 2021. "A Thousand Words Express a Common Idea? Understanding International Tourists’ Reviews of Mt. Huangshan, China, through a Deep Learning Approach," Land, MDPI, vol. 10(6), pages 1-15, May.
    2. Mihaela Constantinescu & Andreea Orindaru & Andreea Pachitanu & Laura Rosca & Stefan-Claudiu Caescu & Mihai Cristian Orzan, 2019. "Attitude Evaluation on Using the Neuromarketing Approach in Social Media: Matching Company’s Purposes and Consumer’s Benefits for Sustainable Business Growth," Sustainability, MDPI, vol. 11(24), pages 1-21, December.

    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:spr:sprchp:978-3-319-14343-9_1. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.