Information Technology & Tourism

, Volume 18, Issue 1–4, pp 157–185 | Cite as

Hotel online reviews: different languages, different opinions

  • Nuno AntonioEmail author
  • Ana de Almeida
  • Luis Nunes
  • Fernando Batista
  • Ricardo Ribeiro
Original Research


Online reviews are one of the main influencers of hotel purchase decisions. This study performs an analysis of reviews extracted from well-known online review sources in combination with hotel sales data and concludes that ratings differ according to the language of reviews. Data science tools have been applied to English, Spanish, and Portuguese reviews, revealing that reviews written in English achieve higher ratings when compared with Spanish or Portuguese reviews. A new visualization method is proposed to quickly depict the sentiment of main topics mentioned in reviews, clearly revealing that not all customers are influenced by reviews in the same way or look for the same things in a hotel. This study has great implications for online reviews research and for hotel management as it clearly shows that language can be used to identify preferences of guests from different origins and because it gives hoteliers more information on how to provide a better service according to guests’ cultural background.


Data science Hospitality industry Language Natural language processing Online reviews 


  1. Abbott D (2014) Applied predictive analytics: principles and techniques for the professional data analyst. Wiley, IndianapolisGoogle Scholar
  2. Anderson CK (2012) The impact of social media on lodging performance. Cornell Hosp Rep 12:4–11Google Scholar
  3. Ayeh JK, Au N, Law R (2016) Investigating cross-national heterogeneity in the adoption of online hotel reviews. Int J Hosp Manag 55:142–153. CrossRefGoogle Scholar
  4. Bjørkelund E, Burnett TH, Nørvag K (2012) A study of opinion mining and visualization of hotel reviews. In: Proceedings of the 14th international conference on information integration and web-based applications & services. ACM, New York, pp 229–238Google Scholar
  5. Cantallops AS, Salvi F (2014) New consumer behavior: a review of research on eWOM and hotels. Int J Hosp Manag 36:41–51. CrossRefGoogle Scholar
  6. Central Intelligence Agency (2016) The world factbook: field listing: languages. Accessed 7 Feb 2016
  7. Chen RXY, Cheung C, Law R (2012) A review of the literature on culture in hotel management research: what is the future? Int J Hosp Manag 31:52–65. CrossRefGoogle Scholar
  8. Commission European (ed) (2014) Study on online consumer reviews in the hotel sector. Final report. European Commission, BrusselsGoogle Scholar
  9. Deutscher G (2010) Through the language glass: why the world looks different in other languages. Macmillan, New YorkGoogle Scholar
  10. Dow D, Karunaratna A (2006) Developing a multidimensional instrument to measure psychic distance stimuli. J Int Bus Stud 37:578–602CrossRefGoogle Scholar
  11. Duan W, Yu Y, Cao Q, Levy S (2016) Exploring the impact of social media on hotel service performance: a sentimental analysis approach. Cornell Hosp Q 57:282–296. CrossRefGoogle Scholar
  12. Euskal Herriko Unibertsitatea, IXA Taldea (2014) Spanish WordnNet-LMF. Accessed 6 Feb 2016
  13. Feldman R, Sanger J (2007) The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press, New YorkGoogle Scholar
  14. Giraudoux P (2016) Pgirmess: data analysis in ecology. R package version 1.6.5. Accessed 5 Mar 2018
  15. Gupta N, Di Fabbrizio G, Haffner P (2010) Capturing the stars: predicting ratings for service and product reviews. In: SS’10 proceedings of the NAACL HLT 2010 workshop on semantic search. Association for Computational Linguistics, Stroudsburg, pp 36–43Google Scholar
  16. Hale SA (2016) User reviews and language: how language influences ratings. Hum-Comput Interact. Google Scholar
  17. Han HJ, Mankad S, Gavirneni N, Verma R (2016) What guests really think of your hotel: text analytics of online customer reviews. Cornell Hosp Rep 16:3–17Google Scholar
  18. Hofstede G (1984) Culture’s consequences: international differences in work-related values. SAGE, LondonGoogle Scholar
  19. Holten D (2006) Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. IEEE Trans Vis Comput Graph 12:741–748CrossRefGoogle Scholar
  20. House RJ, Hanges PJ, Javidan M et al (eds) (2004) Culture, leadership, and organizations: the GLOBE study of 62 societies. SAGE Publications Inc, Thousand OaksGoogle Scholar
  21. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Kim W, Kohavi R (eds) Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 168–177Google Scholar
  22. Instituto Nacional de Estatística (2016) Tourism statistics—2015. Accessed 1 Sep 2016
  23. Kim WG, Lim H, Brymer RA (2015) The effectiveness of managing social media on hotel performance. Int J Hosp Manag 44:165–171. CrossRefGoogle Scholar
  24. Kwok L, Xie KL, Richards T (2017) Thematic framework of online review research: a systematic analysis of contemporary literature on seven major hospitality and tourism journals. Int J Contemp Hosp Manag 29:307–354. CrossRefGoogle Scholar
  25. Liu B, Zhang L (2012) A survey of opinion mining and sentiment analysis. In: Aggarwal CC, Zhai CX (eds) Mining text data. Springer, Boston, pp 415–463CrossRefGoogle Scholar
  26. Liu Y, Teichert T, Rossi M et al (2017) Big data for big insights: investigating language-specific drivers of hotel satisfaction with 412,784 user-generated reviews. Tour Manag 59:554–563. CrossRefGoogle Scholar
  27. Melian-Gonzalez S, Bulchand-Gidumal J, Gonzalez Lopez-Valcarcel B (2013) Online customer reviews of hotels: as participation increases, better evaluation is obtained. Cornell Hosp Q 54:274–283. CrossRefGoogle Scholar
  28. Mellinas JP, María-Dolores S-MM, Bernal García JJB (2015) the unexpected scoring system. Tour Manag 49:72–74. CrossRefGoogle Scholar
  29. Miller GA (1998) WordNet: a lexical database for English. Communications of the ACM. MIT Press, Cambridge, pp 39–41Google Scholar
  30. Öğüt H, Onur Taş BKO (2012) The influence of Internet customer reviews on the online sales and prices in hotel industry. Serv Ind J 32:197–214. CrossRefGoogle Scholar
  31. Oliveira HG, Gomes P (2014) ECO and Onto.PT: a flexible approach for creating a Portuguese WordNet automatically. Lang Resour Eval 48:373–393. CrossRefGoogle Scholar
  32. Pacheco L (2016) An analysis of online reviews by language groups: the case of hotels in Porto, Portugal. Eur J Tour Res 14:66–74Google Scholar
  33. Phillips P, Barnes S, Zigan K, Schegg R (2016) Understanding the impact of online reviews on hotel performance: an empirical analysis. J Travel Res 56:235–249. CrossRefGoogle Scholar
  34. Pohlert T (2014) The pairwise multiple comparison of mean ranks package (PMCMR). R package. Accessed 5 Mar 2018
  35. Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl Based Syst 89:14–46. CrossRefGoogle Scholar
  36. Saralegi X, San Vincente I (2013) Elhuyar at TASS 2013. In: Esteban AD, Loinaz IA, Román JV (eds) Proceedings of XXIX congreso de la sociedad española de procesamiento de lenguaje natural. El Congreso Español de Informática, Madrid, pp 143–150Google Scholar
  37. Schuckert M, Liu X, Law R (2015a) A segmentation of online reviews by language groups: how English and non-English speakers rate hotels differently. Int J Hosp Manag 48:143–149. CrossRefGoogle Scholar
  38. Schuckert M, Liu X, Law R (2015b) Hospitality and tourism online reviews: recent trends and future directions. J Travel Tour Mark 32:608–621. CrossRefGoogle Scholar
  39. Silva MJ, Carvalho P, Sarmento L (2012) Building a sentiment lexicon for social judgement mining. In: Caseli H, Villavicencio A, Teixeira A, Perdigão F (eds) Computational processing of the Portuguese language. Springer, Berlin, pp 218–228CrossRefGoogle Scholar
  40. Surowiecki J (2005) The wisdom of crowds, reprint edn. Anchor, New YorkGoogle Scholar
  41. R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.
  42. Torres EN, Singh D, Robertson-Ring A (2015) Consumer reviews and the creation of booking transaction value: lessons from the hotel industry. Int J Hosp Manag 50:77–83. CrossRefGoogle Scholar
  43. Vermeulen IE, Seegers D (2009) Tried and tested: the impact of online hotel reviews on consumer consideration. Tour Manag 30:123–127. CrossRefGoogle Scholar
  44. Wang B, Huang Y, Li X (2016) Combining review text content and reviewer-item rating matrix to predict review rating. Comput Intell Neurosci 5968705(2016):1–11. Google Scholar
  45. Ware C (2009) Information visualization: perception for design, 2nd edn. Elsevier, AmsterdamGoogle Scholar
  46. World Travel & Tourism Council (2016) Travel & tourism: economic impact 2016 Portugal. World Travel & Tourism Council, LondonGoogle Scholar
  47. Xiang Z, Schwartz Z, Gerdes JHJ, Uysal M (2015) What can big data and text analytics tell us about hotel guest experience and satisfaction? Int J Hosp Manag 44:120–130. CrossRefGoogle Scholar
  48. Xu X, Li Y (2016) The antecedents of customer satisfaction and dissatisfaction toward various types of hotels: a text mining approach. Int J Hosp Manag 55:57–69. CrossRefGoogle Scholar
  49. Ye Q, Law R, Gu B (2009) The impact of online user reviews on hotel room sales. Int J Hosp Manag 28:180–182. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto Universitário de Lisboa (ISCTE-IUL)LisbonPortugal
  2. 2.CISUCCoimbraPortugal
  3. 3.Instituto de TelecomunicaçõesLisbonPortugal
  4. 4.ISTAR-IULLisbonPortugal
  5. 5.Spoken Language Systems LabINESC-ID LisboaLisbonPortugal

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