Please wait a minute...
New Technology of Library and Information Service  2016, Vol. 32 Issue (9): 51-57    DOI: 10.11925/infotech.1003-3513.2016.09.06
Orginal Article Current Issue | Archive | Adv Search |
Analyzing Travelers’ Preferences for Hotels Based on Structural Topic Model
Yang Haixia(),Wu Weifang,Sun Hanlin
Economics and Management School, Wuhan University, Wuhan 430072, China
Download: PDF(744 KB)   HTML ( 35
Export: BibTeX | EndNote (RIS)      

[Objective] This paper aims to identify various types of travelers’ preferences for hotel services. [Methods] First, we classified the hotels as luxury and budget ones, and then divided the travelers into five categories. Second, we analyzed individual traveler’s rating behaviors on the hotel review website TripAdvisor. Finally, we analyzed the latent topics of hotel reviews with the help of Structure Topic Model (STM) to identify travellers’ preferences for hotel services. [Results] We found that the average rating scores of luxury hotels were higher than the budget ones and travelers did have different preferences for hotel services. [Limitations] The dataset for our study was not large enough. We did not consider the impacts of gender and age to hotel rating and online review contents. [Conclusions] Analyzing travelers’ preferences for hotels could help both the managers and travelers make right decisions.

Key wordsOnline review      Patterns of rating      Hotel grade      Travelers’ profiles      STM      Review topics     
Received: 12 June 2016      Published: 19 October 2016

Cite this article:

Yang Haixia,Wu Weifang,Sun Hanlin. Analyzing Travelers’ Preferences for Hotels Based on Structural Topic Model. New Technology of Library and Information Service, 2016, 32(9): 51-57.

URL:     OR

[1] Buhalis D, Law R.Progress in Information Technology and Tourism Management: 20 Years on and 10 Years After the Internet—The State of eTourism Research[J]. Tourism Management, 2008, 29(4): 609-623.
[2] Banerjee S, Chua A Y K. In Search of Patterns Among Travellers’ Hotel Ratings in TripAdvisor[J]. Tourism Management, 2016, 53: 125-131.
[3] Brown J, Broderick A J, Lee N.Word of Mouth Communication Within Online Communities: Conceptualizing the Online Social Network[J]. Journal of Interactive Marketing, 2007, 21(3): 2-20.
[4] Goldenberg J, Libai B, Muller E.Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth[J]. Marketing Letters, 2001, 12(3): 211-223.
[5] Ye Q, Law R, Gu B.The Impact of Online User Reviews on Hotel Room Sales[J]. International Journal of Hospitality Management, 2009, 28(1): 180-182.
[6] Rivers M J, Toh R S, Alaoui M.Frequent-stayer Programs: the Demographic, Behavioral, and Attitudinal Characteristics of Hotel Steady Sleepers[J]. Journal of Travel Research, 1991, 30(2): 41-45.
[7] Lee C F, Huang H I, Chen W C.The Determinants of Honeymoon Destination Choice—The Case of Taiwan[J]. Journal of Travel & Tourism Marketing, 2010, 27(7): 676-693.
[8] Lai L, Graefe A R.Identifying Market Potential and Destination Choice Factors of Taiwanese Overseas Travelers[J]. Journal of Hospitality & Leisure Marketing, 1999, 6(4): 45-65.
[9] Poston R S.Using and Fixing Biased Rating Schemes[J]. Communications of the ACM, 2008, 51(9): 105-109.
[10] Munar A M, Jacobsen J K S. Motivations for Sharing Tourism Experiences Through Social Media[J]. Tourism Management, 2014, 43: 46-54.
[11] Mukherjee A, Liu B.Aspect Extraction Through Semi-supervised Modeling [C]. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. 2012: 339-348.
[12] Wang H, Lu Y, Zhai C.Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach [C]. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA. 2010: 783-792.
[13] Zhao Y, Dong S, Yang J.Effect Research of Aspects Extraction for Chinese Hotel Reviews Based on Machine Learning Method[J]. International Journal of Smart Home, 2015, 9(3): 23-34.
[14] Dolnicar S.Business Travellers’ Hotel Expectations and Disappointments: A Different Perspective to Hotel Attribute Importance Investigation[J]. Asia Pacific Journal of Tourism Research, 2002, 7(1): 29-35.
[15] O’Connor P. User-generated Content and Travel: A Case Study on [A]. // Information and Communication Technologies in Tourism 2008[M]. Springer Vienna, 2008.
[16] 高宝俊, 孙含琳, 王寒凝. 在线评论对酒店订满率的影响研究[J]. 旅游学刊, 2016, 31(4): 109-117.
[16] (Gao Baojun, Sun Hanlin, Wang Hanning.Influence of Online Reviews on Hotels’ Full-occupancy Rates[J]. Tourism Tribune, 2016, 31(4): 109-117.)
[17] Roberts M E, Stewart B M, Tingley D.STM: R Package for Structural Topic Models[J]. General Information, 2014, 57(1): 445-460.
[18] Roberts M E, Stewart B M, Tingley D, et al.Structural Topic Models for Open-Ended Survey Responses[J]. American Journal of Political Science, 2014, 58(4): 1064-1082.
[19] Lucas C, Nielsen R A, Roberts M E, et al.Computer-Assisted Text Analysis for Comparative Politics[J]. Political Analysis, 2015, 23(2): 254-277.
[20] Liu Y.Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue[J]. Journal of Marketing, 2006, 70(3): 74-89.
[21] Sun M.How Does the Variance of Product Ratings Matter?[J]. Management Science, 2012, 58(4): 696-707.
[22] Ariffin A A M, Maghzi A. A Preliminary Study on Customer Expectations of Hotel Hospitality: Influences of Personal and Hotel Factors[J]. International Journal of Hospitality Management, 2012, 31(1): 191-198.
[23] Liu S, Law R, Rong J, et al.Analyzing Changes in Hotel Customers’ Expectations by Trip Mode[J]. International Journal of Hospitality Management, 2013, 34(1): 359-371.
[24] 保继刚. 旅游者行为研究[J]. 社会科学家, 1987(6): 19-22.
[24] (Bao Jigang.Research the Travelers’ Behavior[J]. Social Scientist, 1987(6): 19-22.)
[25] 杨瑞. 西安市大学生旅游行为模式研究[D]. 西安: 陕西师范大学, 2006.
[25] (Yang Rui.The Study on the University Student’s Travel Behavior Pattern in Xi’an [D]. Xi’an: Shaanxi Normal University, 2006.)
[26] 岳冬菊, 杨媛. 西安市国内游客旅游偏好实证分析[J]. 西安文理学院学报: 自然科学版, 2010, 13(2): 116-119.
[26] (Yue Dongju, Yang Yuan.An Empirical Analysis of Domestic Tourists’ Traveling Preferences in Xi’an[J]. Journal of Xi’an University of Arts & Science: Natural Science Edition, 2010, 13(2): 116-119.)
[27] 雷俐丽. 大连国内游客旅游偏好及行为特征研究[D]. 大连: 东北财经大学, 2011.
[27] (Lei Lili.Research on Tourism Preference and Behavior Characteristics of Domestic Tourist in Dalian [D]. Dalian: Dongbei University of Finance & Economics, 2011.)
[28] 顾秀玲. 环太湖度假酒店四类顾客消费行为实证研究[J]. 牡丹江师范学院学报: 哲学社会科学版, 2014(5): 35-38.
[28] (Gu Xiuling.An Empirical Research of Consumers’ Behavior[J]. Journal of Mudanjiang Normal University: Philosophy Social Sciences Edition, 2014(5): 35-38.)
[29] Zhao W X, Jiang J, Yan H, et al.Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid [C]. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. 2010: 56-65.
[30] Titov I, McDonald R. Modeling Online Reviews with Multi-grain Topic Models [C]. In: Proceedings of the 17th International Conference on World Wide Web. ACM, 2008: 111-120.
[31] Kasper W, Vela M.Sentiment Analysis for Hotel Reviews[C]. In: Proceedings of Computational Linguistics Applications Conference. 2012.
[32] Chen Y S, Chen L H, Takama Y.Proposal of LDA-Based Sentiment Visualization of Hotel Reviews [C]. In: Proceedings of the 2015 IEEE International Conference on Data Mining. 2015: 687-693.
[33] Blei D M.Probabilistic Topic Models[J]. Communications of the ACM, 2012, 55(4): 77-84.
[34] Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[35] Yoo K H, Gretzel U.What Motivates Consumers to Write Online Travel Reviews?[J]. Information Technology & Tourism, 2008, 10(4): 283-295.
[36] Duan H, Zirn C.Can We Identify Manipulative Behavior and the Corresponding Suspects on Review Websites Using Supervised Learning? [A]. // Secure IT Systems[M]. Springer Berlin Heidelberg, 2012: 215-230.
[37] Wu G, Greene D, Cunningham P.Merging Multiple Criteria to Identify Suspicious Reviews [C]. In: Proceedings of the 4th ACM Conference on Recommender Systems. ACM, 2010: 241-244.
[38] Blanke J, Chiesa T.The Travel & Tourism Competitiveness Index 2011: Assessing Industry Drivers in the Wake of the Crisis [R]. World Economic Forum, 2011.
[1] Xiaoxiao Zhu,Zunqi Yang,Jing Liu. Construction of an Adverse Drug Reaction Extraction Model Based on Bi-LSTM and CRF[J]. 数据分析与知识发现, 2019, 3(2): 90-97.
[2] Li Yu,Li Qian,Changlei Fu,Huaming Zhao. Extracting Fine-grained Knowledge Units from Texts with Deep Learning[J]. 数据分析与知识发现, 2019, 3(1): 38-45.
[3] Bengong Yu,Peihang Zhang,Qingtang Xu. Selecting Products Based on F-BiGRU Sentiment Analysis[J]. 数据分析与知识发现, 2018, 2(9): 22-30.
[4] Guang Zhu,Mining Feng,Weiwei Zhang. Incentive Investments on Information Security for Libraries: An Evolutionary Game-theory Approach[J]. 数据分析与知识发现, 2018, 2(6): 13-24.
[5] Guoming Feng,Xiaodong Zhang,Suhui Liu. DBLC Model for Word Segmentation Based on Autonomous Learning[J]. 数据分析与知识发现, 2018, 2(5): 40-47.
[6] Jiang Wu,Wanwan Liu. Identifying Reviews with More Positive Votes——Case Study of[J]. 数据分析与知识发现, 2017, 1(9): 16-27.
[7] Hui Li,Yunfeng Hu. Analyzing Online Reviews with Dynamic Sentiment Topic Model[J]. 数据分析与知识发现, 2017, 1(9): 74-82.
[8] Sanhong Deng,Yuyangzi Fu,Hao Wang. Multi-Label Classification of Chinese Books with LSTM Model[J]. 数据分析与知识发现, 2017, 1(7): 52-60.
[9] Yanfeng Zhang,He Li,Lihui Peng,Litie Hou. Identifying Useful Online Reviews with Semantic Feature Extraction[J]. 数据分析与知识发现, 2017, 1(12): 74-83.
[10] Zhang Yanfeng,Li He,Peng Lihui. Research on the Brand Switching Intention of Online Product Reviews Based on the Fuzzy Sentiment Calculation[J]. 现代图书情报技术, 2016, 32(5): 64-71.
[11] Qian Gao, Yang Yang, Guangwei Hu, Chao Xu, Gaofeng Shen, Jian Zhao. Analyzing Return of Investment for New Energy Project with Big Data: Case Study of SG-ERP System in Y City[J]. 数据分析与知识发现, 2016, 32(12): 57-65.
[12] Yang Yang,Lin Hui,Hu Guangwei. Detecting Investment Risks of Photovoltaic Projects with Big Data: Case Study of[J]. 现代图书情报技术, 2016, 32(11): 11-19.
[13] Gao Song,Wang Hongwei,Feng Gang,Wang Wei. Review of Comparative Opinions Mining Studies of Online Comments[J]. 现代图书情报技术, 2016, 32(10): 1-12.
[14] Du Jiazhong, Xu Jian, Liu Ying. Research on Construction of Feature-Sentiment Ontology and Sentiment Analysis[J]. 现代图书情报技术, 2014, 30(5): 74-82.
[15] Sun Xiaoling, Zhao Yuxiang, Zhu Qinghua. Analyzing the Demand of Online Product Review System’ s Features Using Kano Model: An Empirical Study of Chinese Online Shops[J]. 现代图书情报技术, 2013, (6): 76-84.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938