Identifying emerging hotel preferences using Emerging Pattern Mining technique

Hotel managers continue to find ways to understand traveler preferences, with the aim of improving their strategic planning, marketing, and product development. Traveler preference is unpredictable; for example, hotel guests used to prefer having a telephone in the room, but now favor fast Internet connection. Changes in preference influence the performance of hotel businesses, thus creating the need to identify and address the demands of their guests. Most existing studies focus on current demand attributes and not on emerging ones. Thus, hotel managers may find it difficult to make appropriate decisions in response to changes in travelers' concerns. To address these challenges, this paper adopts Emerging Pattern Mining technique to identify emergent hotel features of interest to international travelers. Data are derived from 118,000 records of online reviews. The methods and findings can help hotel managers gain insights into travelers' interests, enabling the former to gain a better understanding of the rapid changes in tourist preferences.

[1]  Qiang Ye,et al.  Feature extraction of travel destinations from online Chinese-language customer reviews , 2011, Int. J. Serv. Technol. Manag..

[2]  B. Stringam,et al.  An Analysis of Word-of-Mouse Ratings and Guest Comments of Online Hotel Distribution Sites , 2010 .

[3]  B. Sohrabi,et al.  An exploratory analysis of hotel selection factors: a comprehensive survey of Tehran hotels. , 2012 .

[4]  Yuen-Hsien Tseng,et al.  Text mining techniques for patent analysis , 2007, Inf. Process. Manag..

[5]  Heon Gyu Lee,et al.  Real-Time Diagnosis System Using Incremental Emerging Pattern Mining , 2010, 2010 Proceedings of the 5th International Conference on Ubiquitous Information Technologies and Applications.

[6]  D. Carson.  The `blogosphere' as a market research tool for tourism destinations: A case study of Australia's Northern Territory , 2008 .

[7]  Maria Banyai,et al.  Travel Blogs: A Reflection of Positioning Strategies? , 2012 .

[8]  Iis P. Tussyadiah,et al.  Marketing Places Through First‐Person Stories—an Analysis of Pennsylvania Roadtripper Blog , 2008 .

[9]  Jinyan Li,et al.  Strong Compound-Risk Factors: Efficient Discovery Through Emerging Patterns and Contrast Sets , 2007, IEEE Transactions on Information Technology in Biomedicine.

[10]  Mark R. Lehto,et al.  Text Analysis of Consumer Reviews: The Case of Virtual Travel Firms , 2007, HCI.

[11]  H. Wilkins,et al.  Using Importance-Performance Analysis to Appreciate Satisfaction in Hotels , 2010 .

[12]  Botao Wang,et al.  Efficiently mining local conserved clusters from gene expression data , 2010, Neurocomputing.

[13]  Clark Hu,et al.  Text mining a decade of progress in hospitality human resource management research: identifying emerging thematic development. , 2007 .

[14]  Ming-Cheng Tsou.  Geographic Information Retrieval and Text Mining on Chinese Tourism Web Pages , 2010, Int. J. Inf. Technol. Web Eng..

[15]  Jinyan Li,et al.  Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns. , 2002 .

[16]  Miguela M. Mena,et al.  Using travel blogs to examine the postconsumption behavior of tourists , 2012 .

[17]  Henry Tsai,et al.  Hotel Selection Criteria Used by Mainland Chinese and Foreign Individual Travelers to Hong Kong , 2011 .

[18]  Hee Seok Song,et al.  Detecting the change of customer behavior based on decision tree analysis , 2005, Expert Syst. J. Knowl. Eng..

[19]  A. A. M. Ariffin,et al.  A preliminary study on customer expectations of hotel hospitality: influences of personal and hotel factors. , 2012 .

[20]  Peng Shi,et al.  A novel approach of mining strong jumping emerging patterns based on BSC-tree , 2014, Int. J. Syst. Sci..

[21]  Ralph Arnote,et al.  Hong Kong (China) , 1996, OECD/G20 Base Erosion and Profit Shifting Project.

[22]  Chieh-Yuan Tsai,et al.  A change detection method for sequential patterns , 2009, Decis. Support Syst..

[23]  Edgard Monforte Merlo,et al.  Consumers attribute analysis of economic hotels: An exploratory study , 2011 .

[24]  A. Wenger.  Analysis of travel bloggers' characteristics and their communication about Austria as a tourism destination , 2008 .

[25]  Jinyan Li,et al.  Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.

[26]  John C. Crotts,et al.  Farm tourism experiences in travel reviews: A cross-comparison of three alternative methods for data analysis , 2013 .

[27]  Betsy Bender Stringam,et al.  Assessing the Importance and Relationships of Ratings on User-Generated Traveler Reviews , 2010 .

[28]  Paul Rayson,et al.  Comparing Corpora using Frequency Profiling , 2000, Proceedings of the workshop on Comparing corpora -.

[29]  R. Law,et al.  Analyzing changes in hotel customers’ expectations by trip mode , 2013 .

[30]  Tat Y. Choi,et al.  Determinants of hotel guests’ satisfaction and repeat patronage in the Hong Kong hotel industry , 2001 .

[31]  Adrian Groza,et al.  Integrating DBpedia and SentiWordNet for a tourism recommender system , 2011, 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing.

[32]  M. T. Díaz-Delfa,et al.  Tourist preferences for rural house stays: Evidence from discrete choice modelling in Spain , 2009 .

[33]  C. Pedron,et al.  Analysing reviews in the Web 2.0: Small and medium hotels in Portugal , 2012 .

[34]  Julia E. Blose,et al.  Believe it or not: Credibility of blogs in tourism , 2008 .

[35]  Y. Reisinger,et al.  Cross-cultural differences in tourism: Indonesian tourists in Australia. , 1997 .

[36]  Hyoil Han,et al.  Approaches to text mining for clinical medical records , 2006, SAC '06.

[37]  Huiqing Liu,et al.  Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients , 2003, Bioinform..

[38]  Santiago Melián-González,et al.  Improving hotel ratings by offering free Wi‐Fi , 2011 .

[39]  Lorraine Davidson,et al.  I spy with my little eye: A comparison of manual versus computer‐aided analysis of data gathered by projective techniques , 2010 .

[40]  Huilong Duan,et al.  Mining the Changes of Medical Behaviors for Clinical Pathways , 2013, MedInfo.

[41]  Fred J. DeMicco,et al.  Marketplace Lodging Needs of Mature Travelers , 1992 .

[42]  Rob Law,et al.  Discovering the hotel selection preferences of Hong Kong inbound travelers using the Choquet Integral. , 2013 .

[43]  Kotagiri Ramamohanarao,et al.  Instance-Based Classification by Emerging Patterns , 2000, PKDD.

[44]  Ling-Feng Hsieh,et al.  A service quality measurement architecture for hot spring hotels in Taiwan , 2008 .

[45]  Rob Law,et al.  Identifying changes and trends in Hong Kong outbound tourism , 2011 .

[46]  Philip S. Yu,et al.  Mining interesting user behavior patterns in mobile commerce environments , 2012, Applied Intelligence.

[47]  Kjetil Nørvåg,et al.  A study of opinion mining and visualization of hotel reviews , 2012, IIWAS '12.

[48]  Peng Shi,et al.  Fast algorithms for mining Strong Jumping Emerging Patterns using the contrast pattern tree , 2013 .

[49]  Rob Law,et al.  Incorporating Both Positive and Negative Association Rules into the Analysis of Outbound Tourism in Hong Kong , 2010 .

[50]  Valerie J. Gillet,et al.  Automating Knowledge Discovery for Toxicity Prediction Using Jumping Emerging Pattern Mining , 2012, J. Chem. Inf. Model..

[51]  Jameel Khadaroo,et al.  The role of transport infrastructure in international tourism development: A gravity model approach , 2008 .

[52]  J. Crotts,et al.  Travel Blogs and the Implications for Destination Marketing , 2007 .

[53]  R. Law,et al.  The Impact of Culture on Hotel Ratings: Analysis of Star-Rated Hotels in China , 2011 .

[54]  René Witte,et al.  Combining Biological Databases and Text Mining to Support New Bioinformatics Applications , 2005, NLDB.

[55]  Kotagiri Ramamohanarao,et al.  A Bayesian Approach to Use Emerging Patterns for Classification , 2003, ADC.

[56]  T. Lockyer.  Understanding the dynamics of the hotel accommodation purchase decision , 2005 .

[57]  Kotagiri Ramamohanarao,et al.  Making Use of the Most Expressive Jumping Emerging Patterns for Classification , 2001, Knowledge and Information Systems.