Sentiment Analysis for Tourism



Sentiment analysis software is a key component of tourism big data research for its ability to detect positive and negative opinions in text. This supports large-scale analyses of the key affective dimension of reviews and social web posts about tourism venues and experiences. Sentiment analysis is fast and reasonably accurate, enabling patterns to be mined from large numbers of texts that would not be evident to experts reading those texts, such as differences between genders or venues in the aspects of destinations that are liked. This chapter reviews the main sentiment analysis approaches with a focus on practical descriptions of how the methods work and how they can be applied. The chapter also illustrates the value of sentiment analysis for tourism research.


Sentiment analysis Tourism research Social web posts Online reviews Tourism experiences 


  1. Alcoba J, Mostajo S, Paras R, Ebron RA (2017) Beyond quality of service: exploring what tourists really value. International conference on exploring services science. Springer, Berlin, Germany, pp 261–271Google Scholar
  2. Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC2010, pp 2200–2204Google Scholar
  3. Balahur A, Turchi M (2014) Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Comput Speech Lang 28(1):56–75Google Scholar
  4. Barbagallo D, Bruni L, Francalanci C, Giacomazzi P (2012) An empirical study on the relationship between Twitter sentiment and influence in the tourism domain. In: Information and communication technologies in tourism 2012. Springer, Vienna, Austria, pp 506–516Google Scholar
  5. Cambria E, Poria S, Gelbukh A, Thelwall M (2017) Sentiment analysis is a big suitcase. IEEE Intell Syst 32(6):74–80Google Scholar
  6. Chalfen RM (1979) Photograph’s role in tourism: some unexplored relationships. Ann Tourism Res 6(4):435–447Google Scholar
  7. Chang WL (2015) Discovering the voice from travelers: a sentiment analysis for online reviews. Workshop on E-Business. Springer, Berlin, Germany, pp 15–26Google Scholar
  8. Chang YC, Ku CH, Chen CH (in press) Social media analytics: extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. Int J Inf ManageGoogle Scholar
  9. Chen JS (2003) Market segmentation by tourists’ sentiments. Ann Tourism Res 30(1):178–193Google Scholar
  10. Cherif W, Madani A, Kissi M (2016) A combination of low-level light stemming and support vector machines for the classification of Arabic opinions. 11th international conference on Intelligent Systems: Theories and Applications (SITA). IEEE Press, Los Alamitos, pp 1–5Google Scholar
  11. Chmiel A, Sobkowicz P, Sienkiewicz J, Paltoglou G, Buckley K, Thelwall M, Hołyst JA (2011) Negative emotions boost user activity at BBC forum. Physica A: Stat Mech Appl 390(16):2936–2944Google Scholar
  12. Claster WB, Cooper M, Sallis P (2010) Thailand—tourism and conflict: modeling sentiment from Twitter tweets using naïve Bayes and unsupervised artificial neural nets. In: Second international conference on Computational Intelligence, Modelling and Simulation (CIMSiM). IEEE Press, Los Alamitos, A, pp 89–94Google Scholar
  13. Cresci S, D’Errico A, Gazzé D, Lo Duca A, Marchetti A, Tesconi M (2014) Tourpedia: a web application for sentiment visualization in tourism domain. In: Proceedings of the 9th Language Resources and Evaluation Conference (LREC 2014), pp 18–21Google Scholar
  14. Das S, Das A (2016) Fusion with sentiment scores for market research. 19th international conference on Information Fusion (FUSION). IEEE Press, Los Alamitos, CA, pp 1003–1010Google Scholar
  15. Dong R, Smyth B (2016) Personalized opinion-based recommendation. International conference on case-based reasoning. Springer, Berlin, Germany, pp 93–107Google Scholar
  16. Dragouni M, Filis G, Gavriilidis K, Santamaria D (2016) Sentiment, mood and outbound tourism demand. Ann Tourism Res 60:80–96Google Scholar
  17. Farhadloo M, Patterson RA, Rolland E (2016) Modeling customer satisfaction from unstructured data using a Bayesian approach. Decis Support Syst 90(1):1–11Google Scholar
  18. Fondevila-Gascón JF, Mir-Bernal P, Puiggròs-Román E, Muñoz-González M, Berbel-Giménez G, Gutiérrez-Aragón Ó, Crespo JL (2016) Semantic fields to improve business: the hotels case. 5(2):47–59Google Scholar
  19. Gamon M, Aue A, Corston-Oliver S, Ringger E (2005) Pulse: mining customer opinions from free text. Lect Notes Comput Sci 3646:121–132Google Scholar
  20. Gan Q, Ferns BH, Yu Y, Jin L (2017) A text mining and multidimensional sentiment analysis of online restaurant reviews. J Qual Assur Hospitality & Tourism 18(4):465–492Google Scholar
  21. García-Pablos A, Cuadros M, Linaza MT (2016) Automatic analysis of textual hotel reviews. Inf Technol Tourism 16(1):45–69Google Scholar
  22. Geetha M, Singha P, Sinha S (2017) Relationship between customer sentiment and online customer ratings for hotels–an empirical analysis. Tour Manage 61(1):43–54Google Scholar
  23. Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11). ICML Press, Bellevue, WA, pp 513–520Google Scholar
  24. Gräbner D, Zanker M, Fliedl G, Fuchs M (2012) Classification of customer reviews based on sentiment analysis. Springer, Helsingborg, Sweden, pp 460–470Google Scholar
  25. Hays S, Page SJ, Buhalis D (2013) Social media as a destination marketing tool: its use by national tourism organisations. Curr Issues Tourism 16(3):211–239Google Scholar
  26. Hofer-Shall Z (2010) The forrester wave: listening platforms, Q3 2010. Forrester Res.
  27. Hu YH, Chen YL, Chou HL (2017a) Opinion mining from online hotel reviews–a text summarization approach. Inf Process Manage 53(2):436–449Google Scholar
  28. Hu YH, Chen K, Lee PJ (2017b) The effect of user-controllable filters on the prediction of online hotel reviews. Inf Manage 54(6):728–744Google Scholar
  29. Jo Y, Oh AH (2011) Aspect and sentiment unification model for online review analysis. In: Proceedings of the fourth ACM international conference on web search and data mining. ACM Press, New York, NY, pp 815–824Google Scholar
  30. Jurowski C (1998) A study of community sentiments in relation to attitudes toward tourism development. Tourism Anal 3(1):17–24Google Scholar
  31. Kim K, Park OJ, Yun S, Yun H (2017) What makes tourists feel negatively about tourism destinations? application of hybrid text mining methodology to smart destination management. Technol Forecast Soc Chang 123:362–369Google Scholar
  32. Kirilenko AP, Stepchenkova SO (2017) Sochi 2014 olympics on Twitter: perspectives of hosts and guests. Tour Manag 63:54–65Google Scholar
  33. Kirilenko AP, Stepchenkova SO, Kim H, Li X (in press) Automated sentiment analysis in tourism: comparison of approaches. J Travel ResGoogle Scholar
  34. Lee M, Jeong M, Lee J (2017) Roles of negative emotions in customers’ perceived helpfulness of hotel reviews on a user-generated review website: a text mining approach. Int J Contemp Hospitality Manag 29(2):762–783Google Scholar
  35. Li X, Li J, Wu Y (2015) A global optimization approach to multi-polarity sentiment analysis. PloS ONE 10(4):e0124672Google Scholar
  36. Liu B (2012) Sentiment analysis and opinion mining. Morgan and Claypool, San Rafael, CAGoogle Scholar
  37. Lockyer T (2003) Hotel cleanliness—how do guests view it? let us get specific. A New Zealand study. Int J Hospitality Manag 22(3):297–305Google Scholar
  38. López Barbosa RR, Sánchez-Alonso S, Sicilia-Urban MA (2015) Evaluating hotels rating prediction based on sentiment analysis services. Aslib J Inf Manage 67(4):392–407Google Scholar
  39. Luo Q, Zhai X (2017) “I will never go to Hong Kong again!” How the secondary crisis communication of “Occupy Central” on Weibo shifted to a tourism boycott. Tour Manag 62:159–172Google Scholar
  40. Lyu SO (2016) Travel selfies on social media as objectified self-presentation. Tourism Manag 54(1):185–195Google Scholar
  41. Marrese-Taylor E, Velásquez JD, Bravo-Marquez F, Matsuo Y (2013a) Identifying customer preferences about tourism products using an aspect-based opinion mining approach. Procedia Comput Sci 22:182–191Google Scholar
  42. Marrese-Taylor E, Velásquez JD, Bravo-Marquez F (2013b) Opinion zoom: a modular tool to explore tourism opinions on the web. In: Proceedings of the 2013 IEEE/WIC/ACM international joint conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol 3. IEEE Computer Society, pp 261–264Google Scholar
  43. Micu A, Micu AE, Geru M, Lixandroiu RC (2017) Analyzing user sentiment in social media: implications for online marketing strategy. Psychol Mark 34(12):1094–1100Google Scholar
  44. Mittal R, Sinha V (2017) A personalized time-bound activity recommendation system. IEEE 7th annual Computing and Communication Workshop and Conference (CCWC). IEEE Press, Menlo Park, CA, pp 1–7Google Scholar
  45. Neviarouskaya A, Prendinger H, Ishizuka M (2009) Compositionality principle in recognition of fine-grained emotions from text. In: Proceedings of the Third International ICWSM Conference (ICWSM2009). IEEE Press, Menlo Park, CA, pp 278–281Google Scholar
  46. Oquab M, Bottou L, Laptev I, Sivic J (2014) Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE Press, Los Alamitos, CA, pp 1717–1724Google Scholar
  47. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retrieval 2:1–135Google Scholar
  48. Park SB, Kim HJ, Ok CM (2018) Linking emotion and place on Twitter at Disneyland. J Travel Tourism Mark 35(5):664–677Google Scholar
  49. Philander K, Zhong Y (2016) Twitter sentiment analysis: capturing sentiment from integrated resort tweets. Int J Hospitality Manage 55(1):16–24Google Scholar
  50. Reis G, Blair-Goldensohn S, McDonald RT (2014) U.S. Patent No. 8,799,773. U.S. Patent and Trademark Office, Washington, DCGoogle Scholar
  51. Salas-Zárate MP, Paredes-Valverde MA, Rodríguez-García MÁ, Valencia-García R, Alor-Hernández G (2017) Sentiment analysis based on psychological and linguistic features for Spanish language. Current trends on knowledge-based systems. Springer, Berlin, Germany, pp 73–92Google Scholar
  52. Sanz-Blas S, Buzova D (2016) Guided tour influence on cruise tourist experience in a port of call: an eWOM and questionnaire-based approach. Int J Tourism Res 18(6):558–566Google Scholar
  53. Scharl A, Dickinger A, Weichselbraun A (2008) Analyzing news media coverage to acquire and structure tourism knowledge. Inf Technol Tourism 10(1):3–17Google Scholar
  54. Schmunk S, Höpken W, Fuchs M, Lexhagen M (2013) Sentiment analysis: extracting decision-relevant knowledge from UGC. In: Information and communication technologies in tourism 2014. Springer, Cham, pp 253–265Google Scholar
  55. Schuckert M, Liu X, Law R (2015) Hospitality and tourism online reviews: recent trends and future directions. J Travel Tourism Mark 32(5):608–621Google Scholar
  56. Schweidel DA, Moe WW (2014) Listening in on social media: a joint model of sentiment and venue format choice. J Mark Res 51(4):387–402Google Scholar
  57. Sharma S, Chakraverty S, Jauhari A (2017) Leveraging polarity switches and domain ontologies for sentiment analysis in text. In: International conference on computational intelligence, communications, and business analytics. Springer, Singapore, pp 84–92Google Scholar
  58. Shimada K, Inoue S, Maeda H, Endo T (2011) Analyzing tourism information on Twitter for a local city. First ACIS International Symposium on Software and Network Engineering (SSNE2011). IEEE Press, Los Alamitos, CA, pp 61–66Google Scholar
  59. Soleymani M, Garcia D, Jou B, Schuller B, Chang SF, Pantic M (2017) A survey of multimodal sentiment analysis. Image Vision Comput 65(1):3–14Google Scholar
  60. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307Google Scholar
  61. Teso E, Olmedilla M, Martínez-Torres MR, Toral SL (2018) Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective. Technol Forecast Soc Chang 129(1):131–142Google Scholar
  62. Thelwall M (2018a) Gender bias in sentiment analysis. Online Inf Rev 42(1):45–57Google Scholar
  63. Thelwall M (2018b) Gender bias in machine learning for sentiment analysis. Online Inf Rev 42(3):343–354Google Scholar
  64. Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol 61(12):2544–2558Google Scholar
  65. Thelwall M, Buckley K, Paltoglou G (2012) Sentiment strength detection for the social Web. J Am Soc Inf Sci Technol 63(1):163–173Google Scholar
  66. Thelwall M, Goriunova O, Vis F, Faulkner S, Burns A, Aulich J, Mas-Bleda A, Stuart E, D’Orazio F (2016) Chatting through pictures? A classification of images tweeted in one week in the UK and USA. J Assoc Inf Sci Technol 67(11):2575–2586Google Scholar
  67. Tian X, He W, Tao R, Akula V (2016) Mining online hotel reviews: a case study from hotels in China. In: AMCIS 2016: surfing the IT innovation wave—22nd Americas conference on information systems, pp 1–8Google Scholar
  68. Valdivia A, Luzón MV, Herrera F (2017a) Sentiment analysis in TripAdvisor. IEEE Intell Syst 32(4):72–77Google Scholar
  69. Valdivia A, Luzón MV, Herrera F (2017b) Sentiment analysis on TripAdvisor: are there inconsistencies in user reviews? International conference on hybrid artificial intelligence systems. Springer, Berlin, Germany, pp 15–25Google Scholar
  70. Volkova S, Wilson T, Yarowsky D (2013) Exploring demographic language variations to improve multilingual sentiment analysis in social media. In: EMNLP2013, pp 1815–1827Google Scholar
  71. 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. Tour Manag 58(1):51–65Google Scholar
  72. Yan Q, Zhou S, Wu S (2018) The influences of tourists’ emotions on the selection of electronic word of mouth platforms. Tour Manag 66:348–363Google Scholar
  73. Ye Q, Zhang Z, Law R (2009) Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst Appl 36(3):6527–6535Google Scholar
  74. You Q, Luo J, Jin H, Yang J (2015) Robust image sentiment analysis using progressively trained and domain transferred deep networks. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence. AAAI Press, Menlo Park, pp 381–388Google Scholar
  75. Zemke DMV, Neal J, Shoemaker S, Kirsch K (2015) Hotel cleanliness: will guests pay for enhanced disinfection? Int J Contemp Hospitality Manage 27(4):690–710Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Statistical Cybermetrics Research GroupUniversity of WolverhamptonWolverhamptonUK

Personalised recommendations