Analyzing Airbnb Customer Experience Feedback Using Text Mining



The objective of this chapter is to present a case of text mining on Airbnb user reviews to analyze and understand various aspects that drive customer satisfaction. The study can be extended further to discover segmentation and targeting of spaces that can take customer satisfaction to the next level and can also consider possibilities of geography specific, travel and purpose specific guest and host requirements. We are trying to gain insights about the challenges faced by customers in sharing economy along with ways to develop “super hosts”. Thus, this work will try to advance our understanding about tourism and hospitality industry by presenting a case of big data analyses on Airbnb user reviews.


Text mining Airbnb Sentiment analysis Rapid miner Customer feedback 

Supplementary material


  1. Choi S, Lehto XY, Morrison AM (2007) Destination image representation on the web: content analysis of Macau travel related websites. Tourism Manage 28(1):118–129. Scholar
  2. Choi TY, Chu R (2001) Determinants of hotel guests’ satisfaction and repeat patronage in the Hong Kong hotel industry. Int J Hospitality Manage 20(3):277–297. Scholar
  3. Dagger TS, Sweeney JC (2006) The effect of service evaluations on behavioural intentions and quality of life. J Ser Res 9(1):3–18CrossRefGoogle Scholar
  4. Gémar G, Jiménez-Quintero JA (2015) Text mining social media for competitive analysis. Tourism Manage Stud 11(1):84–90Google Scholar
  5. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Marketing Lett 12(3):211–223CrossRefGoogle Scholar
  6. Govers R, Go FM (2005) Projected destination image online: website content analysis of pictures and text. Information Technol Tourism 7:73–89CrossRefGoogle Scholar
  7. Gwinner KP, Gremler DD, Bitner MJ (1998) Relational benefits in services industries: the customer’s perspective. J Acad Marketing Sci 26(2):101–114. Scholar
  8. Hassani H, Silva ES (2015) Forecasting with big data: a review. Ann Data Sci 2(1):5–19CrossRefGoogle Scholar
  9. He W, Zha S, Li L (2013) Social media competitive analysis and text mining: A case study in the pizza industry. Int J Information Manage 33(3):464–472CrossRefGoogle Scholar
  10. Hu N, Koh NS, Reddy SK (2014) Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decis Support Syst 57:42–53. Scholar
  11. Krajnović A, Bosna J, Jašić D (2013) Umbrella branding in tourism—model regions of Istria and Dalmatia. Tourism Hospitality Manage 19(2):201–215Google Scholar
  12. Kwok L, Xie KL (2016) Factors contributing to the helpfulness of online hotel reviews. Int J Contemporary Hospitality Manage 28(10):2156–2177. Scholar
  13. Litvin SW, Goldsmith RE, Pan B (2008) Electronic word-of-mouth in hospitality and tourism management. Tourism Manage 29(3):458–468. Scholar
  14. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Foundations Trends Information Retrieval 2(1–2):1–135. Scholar
  15. Peters M, Pikkemaat B (2006) Innovation in tourism. J Q Assurance in Hospitality Tourism 6(3–4):1–6. Scholar
  16. Pudliner BA (2007) Alternative literature and tourist experience: travel and tourist weblogs. J Tourism Cultural Change 5(1):46–59. Scholar
  17. Rintamäki T, Kuusela H, Mitronen L (2007) Identifying competitive customer value propositions in retailing. Manag Ser Q 17(6):621–634Google Scholar
  18. Šerić M, Gil-Saura I, Ruiz-Molina ME (2014) How can integrated marketing communications and advanced technology influence the creation of customer-based brand equity? Evidence from the hospitality industry. Int J Hospitality Manage 39:144–156. Scholar
  19. Snijders C, Matzat U, Reips UD (2012) Big Data: big gaps of knowledge in the field of Internet Science. Int J Internet Sci 7(1):1–5Google Scholar
  20. Upshall M (2014) Text mining. Business Information Rev 31(2):91–99. Scholar
  21. Vijayadurai J (2008) Service quality, customer satisfaction, and behavioral intention in hotel industry. J Marketing Commun 3(3):14–26Google Scholar
  22. Xiang Z, Du Q, Ma Y, Fan W (2017) A comparative analysis of major online review platforms: implications for social media analytics in hospitality and tourism. Tourism Manage 58:51–65. Scholar
  23. Xiang Z, Schwartz Z, Gerdes JH, Uysal M (2015) What can big data and text analytics tell us about hotel guest experience and satisfaction? Int J Hospitality Manage 44:120–130. Scholar
  24. Xie KL, So KK, Wang W (2017) Joint effects of management responses and online reviews on hotel financial performance: a data-analytics approach. Int J Hospitality Manage 62:101–110. Scholar
  25. Zeithaml VA, Berry LL, Parasuraman A (1996) The behavioral consequences of service quality. J Marketing 60(2):31. Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.DDUKK, CUSATCochinIndia
  2. 2.University of RoehamptonLondonUK

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