Exploring the Impact of Negative Words Used in Online Feedback in Hotel Industry: A Sentiment Analysis, N-gram, and Text Network Analysis Approach

• Autorzy: Daniel Dan
• Języki publikacji: EN

Abstrakty

EN

This study examines the words and situations that trigger and those that do not trigger a hotel response when customers post negative online feedback. The research explores, through sentiment analysis, bigrams, trigrams, and word networking, the valence of online reviews of five important hotels in Las Vegas. Only the feedback that has been categorized as negative by the algorithm is selected. In correspondence to this feedback, the existence of answers from the hotels is checked together with the response style. While the negative valence of the feedback can represent a mixture of subjective and objective emotions, there are common features present in their expression. On the responses side from the hotel, not all the reviews receive attention. As such, the negative feedback words are extracted and separated into those that belong to reviews that obtain a response and those that do not. The replies are standardised by following an established pattern. This paper aims to contribute to a prominent issue in tourism that is little tackled: responses to feedback. The findings may help the hotels' management explore different paths to improve their services and responses alike. Behavioural marketing researchers might want to use these results to confirm the existence of such patterns in different datasets or situations. (original abstract)

Słowa kluczowe

EN

Sentiment analysis; Tourism; Hotel industry; Marketing; Customer; Opinion research

PL

Analiza wydźwięku; Turystyka; Hotelarstwo; Marketing; Klient; Badanie opinii

• Czasopismo: Journal of Marketing and Consumer Behaviour in Emerging Markets
• Rocznik: 2023
• Numer: nr 1 (16)
• Strony: 39--50

Twórcy

autor

Daniel Dan

• Modul University, Austria

Bibliografia

• 1. Ali, T., Marc, B., Omar, B., Soulaimane, K., & Larbi, S. (2021). Exploring destination's negative e-reputation using aspect based sentiment analysis approach: Case of Marrakech destination on TripAdvisor. Tourism Management Perspectives, 40, 100892. https://doi.org/10.1016/j.tmp.2021.100892
• 2. Alrawadieh, Z., & Dincer, M.Z. (2019). Reputation management in cyberspace: Evidence from Jordan's luxury hotel market. Journal of Hospitality and Tourism Technology, 10(1), 107-120. https://doi.org/10.1108/JHTT-09-2017-0093
• 3. Assimakopoulos, C., Papaioannou, E., Sarmaniotis, C., & Georgiadis, C.K. (2015). Online reviews as a feedback mechanism for hotel CRM systems. Anatolia, 26(1), 5-20. https://doi.org/10.1080/13032917.2014.933707
• 4. Bazargani, R.H.Z., & Kiliç, H. (2021). Tourism competitiveness and tourism sector performance: Empirical insights from new data. Journal of Hospitality and Tourism Management, 46, 73-82. doi: https://doi.org/10.1016/j.jhtm.2020.11.011
• 5. Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding satisfied and dissatisfied hotel customers: Text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25(1), 1-24. doi: https://doi.org/10.1080/19368623.2015.983631
• 6. Bird, Y., Kashaniamin, L., Nwankwo, C., & Moraros, J. (2020, January). Impact and effectiveness of legislative smoking bans and anti-tobacco media campaigns in reducing smoking among women in the US: a systematic review and meta-analysis. Healthcare, 8(1), p. 20. MDPI. https://doi.org/10.3390/healthcare8010020
• 7. Borges-Tiago, M.T., Arruda, C., Tiago, F., & Rita, P. (2021). Differences between TripAdvisor and Booking.com in branding co-creation. Journal of Business Research, 123, 380-388. doi: https://doi.org/10.1016/j.jbusres.2020.09.050
• 8. Büschken, J., & Allenby, G.M. (2016). Sentence-based text analysis for customer reviews. Marketing Science, 35(6), 953-975. https://doi.org/10.1287/mksc.2016.0993
• 9. Chan, I.C.C., Lam, L.W., Chow, C.W., Fong, L.H.N., & Law, R. (2017). The effect of online reviews on hotel booking intention: The role of reader-r eviewer similarity. International Journal of Hospitality Management, 66, 54-65. doi: https://doi.org/10.1016/j.ijhm.2017.06.007
• 10. Chatterjee, P. (2001). Online reviews: Do consumers use them? In M.C. Gilly & J. Myers-Levy (Eds.), ACR 2001 Proceedings (pp. 129-134). Association for Consumer Research. https://ssrn.com/abstract=900158
• 11. Chen, Y.F., Law, C.H.R., & Yan, K.K. (2016). Managing negative electronic word of mouth (eWOM) from the perspective of luxury hotel managers. e-Review of Tourism Research, 7, 1-5.
• 12. Chen, Y.F., Law, R., & Yan, K.K. (2022). Negative eWOM management: How do hotels turn challenges into opportunities? Journal of Quality Assurance in Hospitality & Tourism, 23(3), 692-715. doi: https://doi.org/10.1080/1528008X.2021.1911729
• 13. Colladon, A.F., Guardabascio, B., & Innarella, R. (2019). Using social network and semantic analysis to analyze online travel forums and forecast tourism demand. Decision Support Systems, 123, 113075. doi: https://doi.org/10.1016/j.dss.2019.113075
• 14. Cox, D.F. (1967). Risk taking and information handling in consumer behavior. Harvard University Press.
• 15. Dadhich, A., & Thankachan, B. (2022). Sentiment analysis of amazon product reviews using hybrid rule-based approach. In Smart systems: Innovations in computing (pp. 173-193). Springer. doi: https://doi.org/10.1007/978-981-16-2877-1_17
• 16. Davidow, M. (2003). Organizational responses to customer complaints: What works and what doesn't. Journal of Service Research, 5(3), 225-250. doi: https://doi.org/10.1177/1094670502238917
• 17. De Maeyer, P. (2012). Impact of online consumer reviews on sales and price strategies: A review and directions for future research. Journal of Product & Brand Management, 21(2), 132-139. doi: https://doi.org/10.1108/10610421211215599
• 18. de Oliveira Lima, T., Colaço, M., Prado, K.H.D.J., & de Oliveira, F.R. (2021, December). A big data experiment to evaluate the effectiveness of traditional machine learning techniques against LSTM neural networks in the hotels clients opinion mining. In Proceedings of 2021 IEEE International Conference on Big Data (Big Data) (pp. 5199-5208). IEEE. doi: https://doi.org/10.1109/BigData52589.2021.9671939
• 19. Desmet, B., & Hoste, V. (2013). Emotion detection in suicide notes. Expert Systems with Applications, 40(16), 6351- 6358. https://doi.org/10.1016/j.eswa.2013.05.050
• 20. Esmark Jones, C.L., Stevens, J.L., Breazeale, M., & Spaid, B.I. (2018). Tell it like it is: The effects of differing responses to negative online reviews. Psychology & Marketing, 35(12), 891-901. doi: https://doi.org/10.1002/mar.21142
• 21. Feinerer, I. (2013). Introduction to the tm Package Text Mining in R. http://cran. r-project. org/web/packages/tm/vignettes/tm. pdf.
• 22. Filieri, R., & McLeay, F. (2014). E-WOM and accommodation: An analysis of the factors that influence travelers' adoption of information from online reviews. Journal of Travel Research, 53(1), 44-57. doi: https://doi.org/10.1177/0047287513481274
• 23. Gavilan, D., Avello, M., & Martinez-Navarro, G. (2018). The influence of online ratings and reviews on hotel booking consideration. Tourism Management, 66, 53-61. doi: https://doi.org/10.1016/j.tourman.2017.10.018
• 24. Hammer, H.L. (2014, September). Detecting threats of violence in online discussions using bigrams of important words. In Proceedings of 2014 IEEE Joint Intelligence and Security Informatics Conference (pp. 319-319). IEEE. https://doi.org/10.1109/jisic.2014.64
• 25. Henry, E. (2008). Are investors influenced by how earnings press releases are written? The Journal of Business Communication (1973), 45(4), 363-407. https://doi.org/10.1177/0021943608319388
• 26. Ho, L.H., Feng, S.Y., & Yen, T.M. (2014). Using modified IPA to improve service quality of standard hotel in Taiwan. Journal of Service Science and Management. doi: http://dx.doi.org/10.4236/jssm.2014.73020
• 27. Jivani, A.G. (2011). A comparative study of stemming algorithms. Int. J. Comp. Tech. Appl, 2(6), 1930-1938. https://kenbenoit.net/assets/courses/tcd2014qta/readings/Jivani_ijcta2011020632.pdf
• 28. Kim, J.J., Fesenmaier, D.R., & Johnson, S.L. (2013, July). The effect of feedback within social media in tourism experiences. In Proceedings of International Conference of Design, User Experience, And Usability (pp. 212- 220). Springer. Doi: https://doi.org/10.1007/978-3-642-39253-5_23
• 29. Lee, C.C., & Hu, C. (2005). Analyzing hotel customers' e-complaints from an internet complaint forum. Journal of Travel & Tourism Marketing, 17(2-3), 167-181. doi: https://doi.org/10.1300/J073v17n02_13
• 30. 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. International Journal of Contemporary Hospitality Management. doi: https://doi.org/10.1108/IJCHM-10-2015-0626
• 31. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74-89. doi: https://doi.org/10.1509/jmkg.70.3.074
• 32. McAuley, J., & Yang, A. (2016, April). Addressing complex and subjective product-related queries with customer reviews. In Proceedings of the 25th International Conference on World Wide Web (pp. 625-635). doi: https://doi.org/10.1145/2872427.2883044
• 33. Öğüt, H., & Onur Taş, B.K. (2012). The influence of internet customer reviews on the online sales and prices in hotel industry. The Service Industries Journal, 32(2), 197-214. doi: https://doi.org/10.1080/02642069.2010.529436
• 34. Park, S.Y., & Allen, J.P. (2013). Responding to online reviews: Problem solving and engagement in hotels. Cornell Hospitality Quarterly, 54(1), 64-73. doi: https://doi.org/10.1177/1938965512463118
• 35. Park, S.T., & Liu, C. (2020). A study on topic models using LDA and Word2Vec in travel route recommendation: Focus on convergence travel and tours reviews. Personal and Ubiquitous Computing, 1-17. doi: https://doi.org/10.1007/s00779-020-01476-2
• 36. Proserpio, D., & Zervas, G. (2017). Online reputation management: Estimating the impact of management responses on consumer reviews. Marketing Science, 36(5), 645-665. doi: https://doi.org/10.1287/mksc.2017.1043
• 37. R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. url: https://www.R-project.org/.
• 38. RStudio Team. (2020). RStudio: Integrated development for R. RStudio, PBC. http://www.rstudio.com/
• 39. Raut, V.B., & Londhe, D.D. (2014, November). Opinion mining and summarization of hotel reviews. In Proceedings of 2014 International Conference on Computational Intelligence and Communication Networks (pp. 556- 559). IEEE. doi: https://doi.org/10.1109/CICN.2014.126
• 40. Rinker, T.W. (2021). sentimentr: Calculate text polarity sentiment version 2.9.0. https://github.com/trinker/sentimentr
• 41. Sakevich, E.K. (2016). Evaluating the Impact of a Smoking Ban in Las Vegas Casino Resorts. UNLV Theses, Dissertations, Professional Papers, and Capstones, 2837. http://dx.doi.org/10.34917/9680560
• 42. Sathiya R.R., Monish R.L., Deekshan, S., Arjun Dev P.K., & Aakash Mithiah S. (2022, April). Detection and summarization of honest reviews using text mining. In Proceedings of 2022 8th International Conference on Smart Structures and Systems (ICSSS) (pp. 01-05). IEEE. doi: https://doi.org/10.1109/ICSSS54381.2022.9782167
• 43. Singh, R. (2021). Hotel reviews dataset from Yelp. Unwrangle. https://blog.unwrangle.com/yelp-las-vegas-hotel-reviews-dataset/
• 44. Sparks, B. (2001). Managing service failure through recovery. Service Quality Management in Hospitality, Tourism And Leisure, 193-219.
• 45. Sparks, B.A., & Browning, V. (2010). Complaining in cyberspace: The motives and forms of hotel guests' complaints online. Journal of Hospitality Marketing & Management, 19(7), 797-818. doi: https://doi.org/10.1080/19368623.2010.508010
• 46. Sparks, B.A., & Bradley, G.L. (2017). A "Triple A" typology of responding to negative consumer-generated online reviews. Journal of Hospitality & Tourism Research, 41(6), 719-745. https://doi.org/10.1177/1096348014538052
• 47. Tax, S.S., Brown, S.W., & Chandrashekaran, M. (1998). Customer evaluations of service complaint experiences: Implications for relationship marketing. Journal of Marketing, 62(2), 60-76. doi: https://doi.org/10.2307/1252161
• 48. Team, C.A.R. (2020, March 27). Compare the best hotel brands. ConsumerAff airs. Retrieved August 8, 2022, from https://www.consumeraffairs.com/travel/hotels.html
• 49. Torres, E.N., Adler, H., Behnke, C., Miao, L., & Lehto, X. (2015). The use of consumer-generated feedback in the hotel industry: Current practices and their effects on quality. International Journal of Hospitality & Tourism Administration, 16(3), 224-250. https://doi.org/10.1080/15256480.2015.1054754
• 50. Willits, F.K., Theodori, G.L., & Luloff, A.E. (2016). Another look at Likert scales. Journal of Rural Social Sciences, 31(3), 6. https://egrove.olemiss.edu/jrss/vol31/iss3/6/
• 51. Woodside, A.G., & Delozier, M.W. (1976). Effects of word of mouth advertising on consumer risk taking. Journal of Advertising, 5(4), 12-19. doi: http://doi.org/10.1080/00913367.1976.10672658
• 52. Worsfold, K., Fisher, R., McPhail, R., Francis, M., & Thomas, A. (2016). Satisfaction, value and intention to return in hotels. International Journal of Contemporary Hospitality Management. doi: https://doi.org/10.1108/IJCHM-04-2015-0195
• 53. Xiang, Z., Wang, D., O'Leary, J.T., & Fesenmaier, D.R. (2015). Adapting to the internet: Trends in travelers' use of the web for trip planning. Journal of Travel Research, 54(4), 511-527. doi: https://doi.org/10.1177/0047287514522883
• 54. Yan, D., Li, K., Gu, S., & Yang, L. (2020). Network-based bag-of-words model for text classification. IEEE Access, 8, 82641-82652. https://doi.org/10.1109/ACCESS.2020.2991074
• 55. Zhan, J., Loh, H.T., & Liu, Y. (2009). Gather customer concerns from online product reviews-A text summarization approach. Expert Systems with Applications, 36(2), 2107-2115. doi: https://doi.org/10.1016/j.eswa.2007.12.039
• 56. Zhang, Y., Jin, R., & Zhou, Z.H. (2010). Understanding bag-of-words model: a statistical framework. International Journal of Machine Learning and Cybernetics, 1(1), 43-52. doi: https://doi.org/10.1007/s13042-010-0001-0
• 57. Zhang, Y., & Vásquez, C. (2014). Hotels՚ responses to online reviews: Managing consumer dissatisfaction. Discourse, Context & Media, 6, 54-64. doi: https://doi.org/10.1016/j.dcm.2014.08.004
• 58. Zhao, P., & Yu, B. (2006). On model selection consistency of Lasso. The Journal of Machine Learning Research, 7, 2541-2563.
• 59. Zhu, J.N., Lam, L.W., & Lai, J.Y. (2019). Returning good for evil: A study of customer incivility and extra-role customer service. International Journal of Hospitality Management, 81, 65-72. doi: https://doi.org/10.1016/j.ijhm.2019.03.004

Identyfikatory

DOI

10.7172/2449-6634.jmcbem.2023.1.3