Online content mining & its potential for cruise management

  • Karsten Sohns
  • Breitner Michael


Customer-made Internet reviews of hotels and travel services are widespread in the so-called Web 2.0. Users publish hundreds of them every day in popular travel portals like Beside these hotel reviews, travellers also review cruise ships, cruise destinations and cruise services, e.g., on board service, on board catering or shore excursions. In the first part of the paper, the use of Web content mining techniques for mining cruise customers’ Internet reviews will be outlined. Further, a brief overview about state of the art Web content mining methods and technologies will be given. In the second part of the paper, results of a systematic survey of 57 Internet sources which provide cruise customers’ reviews will be presented. In the last part of this paper, we will outline the economic efficiency of the available technologies for mining and analysing user-generated content and also its strengths and weaknesses. The analysis is based on the survey and the scientific literature. The paper concludes with recommendations for the use of Web content mining technologies for cruise operators, cruise ship owners, cruise ship builders or ticket sellers.


Natural Language Processing Online Review Cruise Ship Travel Company Board Service 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Arbeitsgemeinschaft Online-Forschung e.V. (2008), Internet facts 2008-I, URL:, Access Date: 30/11/08
  2. Ben-Dov, M. & Feldman, R. (2005), Text Mining and Information Extraction, In: Data Mining and Knowledge Discovery Handbook (Maimon, O. & Rokach, L. Eds.), LondonGoogle Scholar
  3. Chellappa, R.K. & Sin, R.G. (2005), Personalization versus Privacy: An Empirical Examination of the Online Consumer’s Dilemma, Information Technology and Management 6.Google Scholar
  4. Ding, X. et al. (2008), A Holistic Lexicon-Based Approach to Opinion Mining, Proceedings of the international conference on Web search and Web data mining, Palo AltoGoogle Scholar
  5. Fürnkranz, J. (2005), Web Mining, In: Data Mining and Knowledge Discovery Handbook (Maimon, O. & Rokach, L. Eds.), LondonGoogle Scholar
  6. Fukuhura, T. et al. (2007), Analyzing concerns of people from Weblog articles, Al & Society 22(2)Google Scholar
  7. Guha, R. (2003), Semantic Search, Proceedings of the 12th international conference on World Wide Web, BudapestGoogle Scholar
  8. Gulliksen, V. (2008), The cruise industry, Society 45 (4)Google Scholar
  9. Hoeren, T. (2008), Internet und Kommunikationsrecht, KölnGoogle Scholar
  10. Kao, A. & Poteet, R. (2007), Natural Language Processing and Text Mining, London Kernahan, M. & Capretz, L.F. (2005), Different Strategies for Web Mining, In: Advances in Systems, Computing Sciences and Software Engineering (Sobh, T. and Elleithy, K. Eds.), LondonGoogle Scholar
  11. Klein, R.A. (2006), Turning Water into Money: The Economics of the Cruise Industry, In: Cruise Ship Tourism (Dowling, R. K. Ed.), WallingfordGoogle Scholar
  12. Kosala, R. & Blockeel, H. (2000), Web mining research: a survey, ACM SIGKDD Explorations Newsletter 2 (1)Google Scholar
  13. Liu, B. et al. (2005), Opinion Observer: Analyzing and Comparing Opinions on the Web, Proceedings of the 14th international conference on World Wide WebGoogle Scholar
  14. Oesterer, M. & Winkler, K. (2007), Web Mining, In: Leitfaden Online Marketing (Schwarz, T. Ed.), WaghäuselGoogle Scholar
  15. Pekar,V. & Ou, S. (2008), Discovery of subjective evaluations of product features in hotel reviews, Journal of Vacation Marketing 14 (2), 2008Google Scholar
  16. Poescu, A.-M. and Etzioni, O. (2007), Extracting Product Features and Opinions from Reviews, In: Natural Language Processing and Text Mining (Kao, A. and Poteet, S. R. Eds.)Google Scholar
  17. Stavianou, A. et al. (2007), Overview and Semantic Issues of Text Mining, SIGMOND Record 36(3)Google Scholar
  18. Theobald, A. (2007), Online Marktforschung. In: Leitfaden Online Marketing (Schwarz, T. Ed.), WaghäuselGoogle Scholar
  19. tns-infratest (2008), WebLedge™, URL:, Access Date: 30/11/08

Copyright information

© Gabler | GWV Fachverlage GmbH 2009

Authors and Affiliations

  • Karsten Sohns
    • 1
  • Breitner Michael
    • 1
  1. 1.Institute für WirtschaftsinformatikLeibniz University HannoverHannoverGermany

Personalised recommendations