HOTEL SERVICES PREFERENCES ACROSS CULTURES: A CASE STUDY OF APPLYING OPINION MINING ON VIETNAMESE AND AMERICAN ONLINE REVIEWS

Tourism is considered as one of the most important industries in Vietnam. The Government continuously keeps managing and asking for improving all sectors related to tourism. As an important infrastructure for tourism industries, hotels for accommodation are highly considered for improving customer services. On hotel booking and reviews channels, customers express their opinions and feedback about their experienced hotels by writing online reviews, this is valuable source of information that hotel managers should utilize. In this study, we collected 37,712 online reviews about Vietnamese hotels written by domestic and foreign customers in Vietnamese and English languages. Then we developed a hybrid model to perform opinion mining on multilingual social media text and explore for customer opinion differences across cultures. The results show that there are differences in hotel service preferences between Vietnamese and American customers. Based on this research result we recommend for improving customer satisfactions via diversifying across cultures

[1]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[2]  Hsin-Chang Yang,et al.  A Multilingual Text Mining Approach Based on Self-Organizing Maps , 2004, Applied Intelligence.

[3]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[4]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[5]  Trung-Kien Nguyen,et al.  Vietnamese Word Segmentation with CRFs and SVMs: An Investigation , 2006, PACLIC.

[6]  Philipp Koehn,et al.  Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[7]  Khurshid Ahmad,et al.  Multi-lingual Sentiment Analysis of Financial News Streams , 2007 .

[8]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[9]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[10]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.

[11]  Dinh Dien,et al.  A Comparative Study on Vietnamese Text Classification Methods , 2007, 2007 IEEE International Conference on Research, Innovation and Vision for the Future.

[12]  Rada Mihalcea,et al.  Multilingual Subjectivity Analysis Using Machine Translation , 2008, EMNLP.

[13]  Ivan Titov,et al.  A Joint Model of Text and Aspect Ratings for Sentiment Summarization , 2008, ACL.

[14]  Phuong H. Nguyen,et al.  Vietnamese spelling detection and correction using Bi-gram, Minimum Edit Distance, SoundEx algorithms with some additional heuristics , 2008, 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies.

[15]  Kerstin Denecke,et al.  Using SentiWordNet for multilingual sentiment analysis , 2008, 2008 IEEE 24th International Conference on Data Engineering Workshop.

[16]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[17]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[18]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[19]  Rathany Chan Sam,et al.  Extracting Phrases in Vietnamese Document for Summary Generation , 2010, 2010 International Conference on Asian Language Processing.

[20]  Quang-Thuy Ha,et al.  An Upgrading Feature-Based Opinion Mining Model on Vietnamese Product Reviews , 2011, AMT.

[21]  Erkan Bostanci,et al.  An Evaluation of Classification Algorithms Using Mc Nemar's Test , 2012, BIC-TA.

[22]  Alexandra Balahur,et al.  Multilingual Sentiment Analysis using Machine Translation? , 2012, WASSA@ACL.

[23]  Don-Lin Yang,et al.  A semi-automatic approach to construct Vietnamese ontology from online text , 2012 .

[24]  Wu He,et al.  International Journal of Information Management Social Media Competitive Analysis and Text Mining: a Case Study in the Pizza Industry , 2022 .

[25]  Erik Cambria,et al.  Application of multi-dimensional scaling and artificial neural networks for biologically inspired opinion mining , 2013, BICA 2013.

[26]  Luis Alfonso Ureña López,et al.  Ranked WordNet graph for Sentiment Polarity Classification in Twitter , 2014, Comput. Speech Lang..

[27]  Raymond Y. K. Lau,et al.  Product aspect extraction supervised with online domain knowledge , 2014, Knowl. Based Syst..

[28]  Walaa Medhat,et al.  Sentiment analysis algorithms and applications: A survey , 2014 .

[29]  Richa Sharma,et al.  Determination of Polarity of Sentences using Sentiment Orientation System , 2014 .

[30]  Hyun-Kyu Lee,et al.  Exploring Relationship Between Social ICT Issues And Academic Research Interests Through Text Mining Analysis , 2014 .

[31]  Tran Vu Pham,et al.  Domain Specific Sentiment Dictionary for Opinion Mining of Vietnamese Text , 2014, MIWAI.

[32]  Seong-Bae Park,et al.  Building a Vietnamese SentiWordNet Using Vietnamese Electronic Dictionary and String Kernel , 2014, PKAW.

[33]  Rosa M. Carro,et al.  Sentiment analysis in Facebook and its application to e-learning , 2014, Comput. Hum. Behav..

[34]  Rekha Jain,et al.  Polarity Detection at Sentence Level , 2014 .

[35]  Lee, Jong Hwa,et al.  Purchase Process Aspect-based Opinion Mining : An Application for Online Shopping Mall , 2015 .

[36]  Jong-Hwa Lee,et al.  Design Hybrid Models for Opinion Mining on Vietnamese Social Media Text Data , 2016 .