IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-319-15859-4_31.html
   My bibliography  Save this book chapter

Sentiment Analysis of Hotel Reviews in Greek: A Comparison of Unigram Features

In: Cultural Tourism in a Digital Era

Author

Listed:
  • George Markopoulos

    (University of Athens)

  • George Mikros

    (University of Athens, School of Philosophy)

  • Anastasia Iliadi

    (University of Athens)

  • Michalis Liontos

    (University of Athens)

Abstract

Web 2.0 has become a very useful information resource nowadays, as people are strongly inclined to express online their opinion in social media, blogs and review sites. Sentiment analysis aims at classifying documents as positive or negative according to their overall expressed sentiment. In this paper, we create a sentiment classifier applying Support Vector Machines on hotel reviews written in Modern Greek. Using a unigram language model, we compare two different methodologies and the emerging results look very promising.

Suggested Citation

  • George Markopoulos & George Mikros & Anastasia Iliadi & Michalis Liontos, 2015. "Sentiment Analysis of Hotel Reviews in Greek: A Comparison of Unigram Features," Springer Proceedings in Business and Economics, in: Vicky Katsoni (ed.), Cultural Tourism in a Digital Era, edition 127, pages 373-383, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-15859-4_31
    DOI: 10.1007/978-3-319-15859-4_31
    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. Maria Nefeli Nikiforos & Yorghos Voutos & Anthi Drougani & Phivos Mylonas & Katia Lida Kermanidis, 2021. "The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications," Data, MDPI, vol. 6(5), pages 1-29, May.
    2. Sofia Karampela & George Papapanos & Thanasis Kizos, 2019. "Perceptions of Agritourism and Cooperation: Comparisons between an Island and a Mountain Region in Greece," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
    3. Yuguo Tao & Feng Zhang & Chunyun Shi & Yun Chen, 2019. "Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions," Sustainability, MDPI, vol. 11(18), pages 1-23, September.

    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:prbchp:978-3-319-15859-4_31. 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.