Share:


Hotel selection utilizing online reviews: a novel decision support model based on sentiment analysis and DL-VIKOR method

    Xia Liang Affiliation
    ; Peide Liu Affiliation
    ; Zhihao Wang Affiliation

Abstract

With the considerable development of tourism market, as well as the expansion of the e-commerce platform scale, increasing tourists often prefer to select tourism products such as services or hotels online. Thus, it needs to provide an efficient decision support model for tourists to select tourism products. Online reviews based on the user experience would help tourists improve decision efficiency on tourism products. Therefore, in this study, a quantitative method for hotel selection with online reviews is proposed. First, with respect this problem with online reviews, by analyzing sentiment words in online reviews, tourists’ sentiment preferences are transformed into the format of distribution linguistic with respect to sentiment levels. Second, from a theoretical perspective, we proposed a method to determine the ideal solution and nadir solution for distribution linguistic evaluations. Next, based on the frequency of words for evaluating hotel and the distribution linguistic evaluations, the weight vector of the evaluation features is determined. Further, a novel DL-VIKOR method is developed to rank and then to select hotels. Finally, a realistic case from TripAdvisor.com for selecting hotel is used to demonstrate practically and feasibility of the proposed model.


First published online 19 July 2019

Keyword : decision making, quantitative method, online reviews, hotel selection, distribution linguistic

How to Cite
Liang, X., Liu, P., & Wang, Z. (2019). Hotel selection utilizing online reviews: a novel decision support model based on sentiment analysis and DL-VIKOR method. Technological and Economic Development of Economy, 25(6), 1139-1161. https://doi.org/10.3846/tede.2019.10766
Published in Issue
Jul 19, 2019
Abstract Views
3104
PDF Downloads
1587
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Alimardani, M., Hashemkhani Zolfani, S., Aghdaie, M. H., & Tamošaitienė, J. (2013). A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment. Technological and Economic Development of Economy, 19(3), 533-548. https://doi.org/10.3846/20294913.2013.814606

Amaro, S., Duarte, P., & Henriques, C. (2016). Travelers’ use of social media: A clustering approach. Annals of Tourism Research, 59, 1-15. https://doi.org/10.1016/j.annals.2016.03.007

Bi, J. W., Liu, Y., Fan, Z. P., & Zhang, J. (2019). Wisdom of crowds: Conducting importance-performance analysis (IPA) through online reviews. Tourism Management, 70, 460-478. https://doi.org/10.1016/j.tourman.2018.09.010

Chiu, S. H., & Lin, T. Y. (2018). Performance evaluation of Taiwanese international tourist hotels: Evidence from a modified NDEA model with ICA technique. Technological and Economic Development of Economy, 24(4), 1560-1580. https://doi.org/10.3846/tede.2018.3116

Daekook, K., Yongtae, P. (2014). Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Systems with Applications, 41(4), 1041-1050. https://doi.org/10.1016/j.eswa.2013.07.101

Fan, Z. P., Che, Y. J., & Chen, Z. Y. (2017). Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research, 74, 90-100. https://doi.org/10.1016/j.jbusres.2017.01.010

Fan, Z. P., Xi, Y., & Liu, Y. (2018). Supporting consumer’s purchase decision: A method for ranking products based on online multi-attribute product ratings. Soft Computing, 22(16), 5247-5261. https://doi.org/10.1007/s00500-017-2961-4

Guan, H. J., Zhao, A. W., & Du, J. G. (2017). Enterprise green technology innovation behavior. BeiJing, China: Economic Science Press, 4.

Guo, W. T., Huynh, V. N., & Sriboonchitta, S. (2017). A proportional linguistic distribution based model for multiple attribute decision making under linguistic uncertainty. Annals of Operations Research, 256(2), 305-328. https://doi.org/10.1007/s10479-016-2356-4

Herrera, F., Herrera-Viedma, E., & Verdegay, L. (1995). A sequential selection process in group decision making with linguistic assessment approach. Information Sciences, 85, 223-239. https://doi.org/10.1016/0020-0255(95)00025-K

Herrera, F., & Herrera-Viedma, E. (1996). A model of consensus in group decision making under linguistic assessments. Fuzzy Sets and System, 78, 73-87. https://doi.org/10.1016/0165-0114(95)00107-7

Jacob, C., & Guéguen, N. (2015). Does the geographic proximity of products influence a consumer’s decision? An evaluation in a restaurant. Annals of Tourism Research, 52, 169-172. https://doi.org/10.1016/j.annals.2015.03.001

Jiang, Y. P., Liang, H. M., & Sun, M. H. (2015). A method for discrete stochastic MADM problems based on the ideal and nadir solutions. Computers & Industrial Engineering, 87(1), 114-125. https://doi.org/10.1016/j.cie.2015.04.019

Ji, P., Zhang, H. Y., & Wang, J. Q. (2018). A fuzzy decision support model with sentiment analysis for items comparison in e-commerce: The case study of PConline.com. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 1-12. https://doi.org/10.1109/TSMC.2018.2875163

Liang, X., Jiang, Y. P., & Liu, P. D. (2018). Stochastic multiple criteria decision making with 2-tuple aspirations: a method based on disappointment stochastic dominance. International Transactions in Operational Research, 25(1), 913-940. https://doi.org/10.1111/itor.12430

Li, G., Law, R., Vu, H. Q., & Rong, J. (2013). Discovering the hotel selection preferences of Hong Kong inbound travelers using the Choquet Integral. Tourism Management, 36(3), 321-330. https://doi.org/10.1016/j.tourman.2012.10.017

Li, H., Ye, Q., & Law, R. (2013). Determinants of customer satisfaction in the hotel industry: An application of online review analysis. Asia Pacific Journal of Tourism Research, 18(7), 784-802. https://doi.org/10.1080/10941665.2012.708351

Li, J., & Wang, J. (2018). Multi-criteria decision-making with probabilistic hesitant fuzzy information based on expected multiplicative consistency. Neural Computing and Applications, 2018, 1-19. https://doi.org/10.1007/s00521-018-3753-1

Li, Y. M., & Lai, C. Y. (2014). A social appraisal mechanism for online purchase decision support in the micro-blogosphere. Decision Support Systems, 59(1), 190-205. https://doi.org/10.1016/j.dss.2013.11.007

Liu, Y., Bi, J. W., & Fan, Z. P. (2017a). Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Information Fusion, 36, 149-161. https://doi.org/10.1016/j.inffus.2016.11.012

Liu, Y., Bi, J. W., & Fan, Z. P. (2017b). A method for ranking products through online reviews based on sentiment classification and interval-valued intuitionistic fuzzy TOPSIS. International Journal of Information Technology & Decision Making, 16(06), 1497-1522. https://doi.org/10.1142/S021962201750033X

Luo, S., Zhang, H., Wang, J., & Li, L. (2019). Group decision-making approach for evaluating the sustainability of constructed wetlands with probabilistic linguistic preference relations, Journal of the Operational Research Society, 2019, 1-17. https://doi.org/10.1080/01605682.2018.1510806

Opricovic, S. (1998). Multi-criteria optimization of civil engineering systems (PhD Thesis). Faculty of Civil Engineering, Belgrade.

Opricovic, S., & Tzeng, G. H. (2002). Multicriteria planning of post-earthquake sustainable reconstruction. Computer-Aided Civil and Infrastructure Engineering, 17(3), 211-220. https://doi.org/10.1111/1467-8667.00269

Sabokbar, H. F., Ayashi, A., Hosseini, A., Banaitis, A., Banaitienė, N., & Ayashi, R. (2016). Risk assessment in tourism system using a fuzzy set and dominance-based rough set. Technological and Economic Development of Economy, 22(4), 554-573. https://doi.org/10.3846/20294913.2016.1198840

Song, H. (2015). The structure of customer satisfaction with cruise-line services: An empirical investigation based on online word of mouth. Current Issues in Tourism, 18(5), 450-464. https://doi.org/10.1080/13683500.2013.776020

Sparks, B. A., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310-1323. https://doi.org/10.1016/j.tourman.2010.12.011

Sparks, B. A., So, K. K. F. K., & Bradley, G. L. (2016). Responding to negative online reviews: The effects of hotel responses on customer inferences of trust and concern. Tourism Management, 53, 74-85. https://doi.org/10.1016/j.tourman.2015.09.011

Sun, Y., Hrušovský, M., Zhang, C., & Lang, M. X. (2018). A time-dependent fuzzy programming approach for the green multimodal routing problem with rail service capacity uncertainty and road traffic congestion. Complexity, 2018, 8645793. https://doi.org/10.1155/2018/8645793

Sun, Y., Li, X., Liang, X., & Zhang, C. (2019). A bi-objective fuzzy credibilistic chance-constrained programming approach for the hazardous materials road-rail multimodal routing problem under uncertainty and sustainability. Sustainability, 11(9), 2577. https://doi.org/10.3390/su11092577

Vermeulen, I. E., & Seegers, D. (2009). Tried and tested: The impact of online hotel reviews on consumer consideration. Tourism Management, 30(1), 123-127. https://doi.org/10.1016/j.tourman.2008.04.008

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 Management, 58, 51-65. https://doi.org/10.1016/j.tourman.2016.10.001

Ye, Q., Li, H. Y., & Wang, Z. S. (2012). The influence of hotel price on perceived service quality and value in e-tourism: An empirical investigation based on online traveler reviews. Journal of Hospitality & Tourism Research, 38(1), 23-39. https://doi.org/10.1177/1096348012442540

Yu, S., Wang, J., Wang, J. Q., & Li, L. (2018). A multi-criteria decision-making model for hotel selection with linguistic distribution assessments. Applied Soft Computing, 67, 741-755. https://doi.org/10.1016/j.asoc.2017.08.009

Zhang, C. B., Zhang, H. Y., & Wang, J. Q. (2018). Personalized restaurant recommendation method combining group correlations and customer preferences. Information Sciences, 454-455, 128-143. https://doi.org/10.1016/j.ins.2018.04.061

Zhang, G., Dong, Y., & Xu, Y. (2014). Consistency and consensus measures for linguistic preference relations based on distribution assessments. Information Fusion, 17(1), 46-55. https://doi.org/10.1016/j.inffus.2012.01.006

Zhang, X. L., & Xing, X. M. (2017). Probabilistic linguistic VIKOR method to evaluate green supply chain initiatives. Sustainability, 9(7), 12-31. https://doi.org/10.3390/su9071231