Hotel online reviews: different languages, different opinions

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.

[1]  Rob Law,et al.  A review of the literature on culture in hotel management research: What is the future? , 2012 .

[2]  Linchi Kwok,et al.  Thematic framework of online review research: A systematic analysis of contemporary literature on seven major hospitality and tourism journals , 2017 .

[3]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[4]  Rob Law,et al.  Investigating cross-national heterogeneity in the adoption of online hotel reviews , 2016 .

[5]  G. Hofstede,et al.  Culture′s Consequences: International Differences in Work-Related Values , 1980 .

[6]  Jacques Bulchand-Gidumal,et al.  Online Customer Reviews of Hotels , 2013 .

[7]  Douglas Dow,et al.  Developing a multidimensional instrument to measure psychic distance stimuli , 2006 .

[8]  Patrick Haffner,et al.  Capturing the Stars: Predicting Ratings for Service and Product Reviews , 2010, HLT-NAACL 2010.

[9]  Edwin N. Torres,et al.  Consumer Reviews and the Creation of Booking Transaction Value : Lessons from the Hotel Industry 8-8-2015 , 2018 .

[10]  Juan Pedro Mellinas,et al.  Booking.com: the unexpected scoring system. , 2015 .

[11]  Kjetil Nørvåg,et al.  A study of opinion mining and visualization of hotel reviews , 2012, IIWAS '12.

[12]  Hugo Gonçalo Oliveira,et al.  ECO and Onto.PT: a flexible approach for creating a Portuguese wordnet automatically , 2014, Lang. Resour. Evaluation.

[13]  A. S. Cantallops,et al.  International Journal of Hospitality Management New Consumer Behavior: a Review of Research on Ewom and Hotels , 2022 .

[14]  Vadlamani Ravi,et al.  A survey on opinion mining and sentiment analysis: Tasks, approaches and applications , 2015, Knowl. Based Syst..

[15]  I. Vermeulen,et al.  Tried and tested: The impact of online hotel reviews on consumer consideration , 2009 .

[16]  Colin Ware,et al.  Information Visualization: Perception for Design , 2000 .

[17]  P. Dorfman,et al.  Leadership and Organizations: The GLOBE Study of 62 Societies , 2004 .

[18]  Z. Schwartz,et al.  What can big data and text analytics tell us about hotel guest experience and satisfaction , 2015 .

[19]  Mário J. Silva,et al.  Building a Sentiment Lexicon for Social Judgement Mining , 2012, PROPOR.

[20]  Lei Zhang,et al.  A Survey of Opinion Mining and Sentiment Analysis , 2012, Mining Text Data.

[21]  Ronen Feldman,et al.  Book Reviews: The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data by Ronen Feldman and James Sanger , 2008, CL.

[22]  B. Gu,et al.  The impact of online user reviews on hotel room sales , 2009 .

[23]  Guy Deutscher,et al.  Through the Language Glass: Why the World Looks Different in Other Languages , 2010 .

[24]  Rohit Verma,et al.  What Guests Really Think of Your Hotel: Text Analytics of Online Customer Reviews , 2016 .

[25]  Danny Holten,et al.  Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data , 2006, IEEE Transactions on Visualization and Computer Graphics.

[26]  Yang Yu,et al.  Exploring the Impact of Social Media on Hotel Service Performance , 2016 .

[27]  Thorsten Teichert,et al.  Big data for big insights: Investigating language-specific drivers of hotel satisfaction with 412,784 user-generated reviews , 2017 .

[28]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[29]  Rob Law,et al.  A segmentation of online reviews by language groups: How English and non-English speakers rate hotels differently , 2015 .

[30]  Stuart J. Barnes,et al.  Understanding the Impact of Online Reviews on Hotel Performance , 2017 .

[31]  R. Law,et al.  Hospitality and Tourism Online Reviews: Recent Trends and Future Directions , 2015 .

[32]  Hulisi Ögüt,et al.  The influence of internet customer reviews on the online sales and prices in hotel industry , 2012 .

[33]  Yongfeng Huang,et al.  Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating , 2016, Comput. Intell. Neurosci..

[34]  Dean Abbott,et al.  Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst , 2014 .

[35]  W. Kim,et al.  The effectiveness of managing social media on hotel performance. , 2015 .

[36]  L. Pacheco.  An analysis of online reviews by language groups: the case of hotels in Porto, Portugal , 2016, European Journal of Tourism Research.

[37]  C. Anderson.  The Impact of Social Media on Lodging Performance , 2012 .

[38]  Scott A. Hale.  User Reviews and Language: How Language Influences Ratings , 2016, CHI Extended Abstracts.

[39]  G. Hofstede,et al.  Culture′s Consequences: International Differences in Work-Related Values , 1980 .

[40]  Xun Xu,et al.  The antecedents of customer satisfaction and dissatisfaction toward various types of hotels: A text mining approach , 2016 .