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http://dx.doi.org/10.13088/jiis.2019.25.1.021

An Analytical Approach Using Topic Mining for Improving the Service Quality of Hotels  

Moon, Hyun Sil (School of Business & AI Management Research Center, KyungHee University)
Sung, David (School of Business & AI Management Research Center, KyungHee University)
Kim, Jae Kyeong (School of Business & AI Management Research Center, KyungHee University)
Publication Information
Journal of Intelligence and Information Systems / v.25, no.1, 2019 , pp. 21-41 More about this Journal
Abstract
Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.
Keywords
service quality; topic mining; decision tree; big data analysis; online review analysis;
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1 Chevalier, J. A., and D. Mayzlin, "The effect of word of mouth on sales: Online book reviews," Journal of marketing research, Vol.43, No.3(2006), 345-354.   DOI
2 Choi, H., and H. Varian, "Predicting the present with Google Trends," Economic Record, Vol.88(2012), 2-9.   DOI
3 Das, S. R., and M.Y. Chen, "Yahoo! for Amazon: Sentiment extraction from small talk on the web," Management science, Vol.53, No.9 (2007), 1375-1388.   DOI
4 Dellarocas, C., "Strategic manipulation of internet opinion forums: Implications for consumers and firms," Management science, Vol.52, No.10(2006), 1577-1593.   DOI
5 Dellarocas, C., X. Zhang, and N.F. Awad, "Exploring the value of online product reviews in forecasting sales: The case of motion pictures," Journal of Interactive marketing, Vol.21, No.4(2007), 23-45.   DOI
6 Duan, W., B. Gu, and A.B. Whinston, "Do online reviews matter?-An empirical investigation of panel data," Decision support systems, Vol.45, No.4(2008), 1007-1016.   DOI
7 Fang, B., Q. Ye, D. Kucukusta, and R. Law, "Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics," Tourism Management, Vol.52(2016), 498-506.   DOI
8 Forman, C., A. Ghose, and B. Wiesenfeld, "Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets," Information Systems Research, Vol.19, No.3(2008), 291-313.   DOI
9 Goel, S., J.M. Hofman, S. Lahaie, D.M. Pennock, and D.J. Watts, "Predicting consumer behavior with Web search," Proceedings of the National Academy of Sciences(PNAS) (2010).
10 Grewal, R., T.W. Cline, and A. Davies, "Early-entrant advantage, word‐of‐mouth communication, brand similarity, and the consumer decision‐making process," Journal of Consumer Psychology, Vol.13, No.3 (2003), 187-197.   DOI
11 Griffiths, T. L., M. Steyvers, D.M. Blei, and J.B. Tenenbaum, "Integrating topics and syntax," In Advances in neural information processing systems (2005), 537-544.
12 Hagenau, M., M. Liebmann, and D. Neumann, "Automated news reading: Stock price prediction based on financial news using context-capturing features," Decision Support Systems, Vol.55, No.3(2013), 685-697.   DOI
13 Herr, P. M., F.R. Kardes, and J. Kim, "Effects of word-of-mouth and product-attribute information on persuasion: An accessibility-diagnosticity perspective," Journal of consumer research, Vol.17, No.4(1991), 454-462.   DOI
14 Huang, J., and C.X. Ling, "Using AUC and accuracy in evaluating learning algorithms," IEEE Transactions on knowledge and Data Engineering, Vol.17, No.3(2005), 299-310.   DOI
15 Kusumasondjaja, S., T. Shanka, and C. Marchegiani, "Credibility of online reviews and initial trust: The roles of reviewer's identity and review valence," Journal of Vacation Marketing, Vol.18, No.3(2012), 185-195.   DOI
16 Lee, J., "How eWOM reduces uncertainties in decision-making process: using the concept of entropy in information theory," The Journal of Society for e-Business Studies, Vol.16, No.4(2011), 241-256.
17 Lee, M. and H.J. Kim, "Construction of Event Networks from Large News Data Using Text Mining Techniques," Journal of Intelligent Information Systems, Vol.24, No.1(2018), 183-203.
18 Liu, Z., and S. Park, "What makes a useful online review? Implication for travel product websites," Tourism Management, Vol.47(2015), 140-151.   DOI
19 Gantz, J., and D. Reinsel, "Extracting value from chaos," IDC review, Vol.1142(2011), 1-12.
20 Liu, Y., "Word of mouth for movies: Its dynamics and impact on box office revenue," Journal of marketing, Vol.70, No.3(2006), 74-89.   DOI
21 Loh, W. Y., "Classification and regression trees," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol.1, No.1(2011), 14-23.   DOI
22 Mudambi, S. M., and D. Schuff, "Research note: What makes a helpful online review? A study of customer reviews on Amazon. com.," MIS quarterly, Vol.34, No.1(2010), 185-200.   DOI
23 Ogut, H., and B.K. Onur Tas, "The influence of internet customer reviews on the online sales and prices in hotel industry," The Service Industries Journal, Vol.32, No.2(2012), 197-214.   DOI
24 Pan, B., T. MacLaurin, and J.C. Crotts, "Travel blogs and the implications for destination marketing," Journal of Travel Research, Vol.46, No.1(2007), 35-45.   DOI
25 Park, S.M., and B.W. On, "Latent topics-based product reputation mining," Journal of Intelligent Information Systems, Vol.23, No.2 (2017), 39-70.
26 Vermeulen, I. E. and D. Seegers, "Tried and tested: The impact of online hotel reviews on consumer consideration," Tourism management, Vol.30, No.1(2009), 123-127.   DOI
27 Salehan, M., and D.J. Kim, "Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics," Decision Support Systems, Vol.81(2016), 30-40.   DOI
28 Sparks, B. A., and V. Browning, "The impact of online reviews on hotel booking intentions and perception of trust," Tourism management, Vol.32, No.6(2011), 1310-1323.   DOI
29 Sparks, B. A., H.E. Perkins, and R. Buckley, "Online travel reviews as persuasive communication: The effects of content type, source, and certification logos on consumer behavior," Tourism Management, Vol.39 (2013), 1-9.   DOI
30 Ur-Rahman, N., and J.A. Harding, "Textual data mining for industrial knowledge management and text classification: A business oriented approach," Expert Systems with Applications, Vol.39, No.5(2012), 4729-4739.   DOI
31 Werthner, H., and S. Klein, Information technology and tourism: a challenging relationship. Springer-Verlag Wien, 1999.
32 Xiang, Z., and U. Gretzel, "Role of social media in online travel information search," Tourism management, Vol.31, No.2(2010), 179-188.   DOI
33 Xie, K. L., Z. Zhang, and Z. Zhang, "The business value of online consumer reviews and management response to hotel performance," International Journal of Hospitality Management, Vol.43(2014), 1-12.   DOI
34 Zhao, F., Y. Zhu, H. Jin, and L.T. Yang, "A personalized hashtag recommendation approach using LDA-based topic model in microblog environment," Future Generation Computer Systems, Vol.65((2016), 196-206.   DOI
35 Ye, Q., R. Law, G. Gu, and W. Chen, "The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings," Computers in Human behavior, Vol.27, No.2(2011), 634-639.   DOI
36 Zhang, X., and C. Dellarocas, "The lord of the ratings: is a movie's fate is influenced by reviews?," International Conference on Information Systems 2006 proceedings, (2006), 117.
37 Zhang, Z., Q. Ye, R. Law, and Y. Li, "The impact of e-word-of-mouth on the online popularity of restaurants: A comparison of consumer reviews and editor reviews," International Journal of Hospitality Management, Vol.29, No.4(2010), 694-700.   DOI
38 Askitas, N., and K.F. Zimmermann, "Google econometrics and unemployment forecasting," Applied Economics Quarterly, Vol.55, No.2(2009), 107-120.   DOI
39 Zikopoulos, P., and C. Eaton, Understanding big data: Analytics for enterprise class hadoop and streaming data, McGraw-Hill Osborne Media, 2011.
40 Archak, N., A. Ghose, and P.G. Ipeirotis, "Deriving the pricing power of product features by mining consumer reviews," Management science, Vol.57, No.8(2011), 1485-1509.   DOI
41 Baars, H., and H.G. Kemper, "Management support with structured and unstructured data -an integrated business intelligence framework," Information Systems Management, Vol.25, No.2(2008), 132-148.   DOI
42 Blei, D. M., and J. Lafferty, Text mining: Classification, clustering, and applications, Chapman & Hall/CRC, 2009.
43 Blei, D. M., A.Y. Ng, and M.I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research, Vol.3, No.1(2003), 993-1022.
44 Breiman, L, Classification and regression trees, Routledge, New York, 1984.
45 Cambria, E., B. Schuller, Y. Xia, and C. Havasi, "New avenues in opinion mining and sentiment analysis," IEEE Intelligent Systems, Vol.28, No.2(2013), 15-21.   DOI
46 Chen, Y., and J. Xie, "Online consumer review: Word-of-mouth as a new element of marketing communication mix," Management science, Vol.54, No.3(2008), 477-491.   DOI
47 Chae, S.H., J.I. Lim, and J. Kang, "A Comparative Analysis of Social Commerce and Open Market Using User Reviews in Korean Mobile Commerce," Journal of Intelligent Information Systems, Vol.21, No.4(2015), 53-77.   DOI