Sentiment Classification of Consumer-Generated Online Reviews Using Topic Modeling

ABSTRACT The development of the Internet and mobile devices enabled the emergence of travel and hospitality review sites, leading to a large number of customer opinion posts. While such comments may influence future demand of the targeted hotels, they can also be used by hotel managers to improve customer experience. In this article, sentiment classification of an eco-hotel is assessed through a text mining approach using several different sources of customer reviews. The latent Dirichlet allocation modeling algorithm is applied to gather relevant topics that characterize a given hospitality issue by a sentiment. Several findings were unveiled including that hotel food generates ordinary positive sentiments, while hospitality generates both ordinary and strong positive feelings. Such results are valuable for hospitality management, validating the proposed approach.

[1]  Kurt Hornik,et al.  Text Mining Infrastructure in R , 2008 .

[2]  Gadadhar Sahoo,et al.  A charged system search approach for data clustering , 2014, Progress in Artificial Intelligence.

[3]  Leigh Jin,et al.  A Text Mining and Multidimensional Sentiment Analysis of Online Restaurant Reviews , 2017 .

[4]  C. Flavián,et al.  Avoiding the dark side of positive online consumer reviews: Enhancing reviews' usefulness for high risk-averse travelers , 2015 .

[5]  Shanshan Gao,et al.  The application and comparison of web services for sentiment analysis in tourism , 2015, 2015 12th International Conference on Service Systems and Service Management (ICSSSM).

[6]  Xiaojun Li,et al.  A sentiment analysis model for hotel reviews based on supervised learning , 2011, 2011 International Conference on Machine Learning and Cybernetics.

[7]  Huong Ha,et al.  Empowerment in the Hospitality Industry in the United States , 2016 .

[8]  Daniel S. Soper,et al.  An n-gram analysis of Communications 2000--2010 , 2012, Commun. ACM.

[9]  Björn W. Schuller,et al.  New Avenues in Opinion Mining and Sentiment Analysis , 2013, IEEE Intelligent Systems.

[10]  A. Tjoa,et al.  Information and Communication Technologies in Tourism , 1996, Springer Vienna.

[11]  Weiguo Fan,et al.  Tapping the power of text mining , 2006, CACM.

[12]  Kin-Nam Lau,et al.  Text Mining for the Hotel Industry , 2005 .

[13]  Paulo Cortez,et al.  Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation , 2015, Expert Syst. Appl..

[14]  Hadyn Ingram,et al.  Dictionary of Travel, Tourism & Hospitality (3rd ed.) , 2003 .

[15]  Mehran Sahami,et al.  Text Mining: Classification, Clustering, and Applications , 2009 .

[16]  Kurt Hornik,et al.  topicmodels : An R Package for Fitting Topic Models , 2016 .

[17]  Rob Law,et al.  Personality Differences and Hotel Web Design Study Using Targeted Positive and Negative Association Rule Mining , 2013 .

[18]  Joan Francesc Fondevila i Gascón,et al.  Sentiment analysis as a qualitative methodology to analyze social media: study case of tourism , 2016 .

[19]  James Allan,et al.  A Comparative Study of Utilizing Topic Models for Information Retrieval , 2009, ECIR.

[20]  Viktor Pekar,et al.  Discovery of subjective evaluations of product features in hotel reviews , 2008 .

[21]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[22]  Ling Liu,et al.  Manipulation of online reviews: An analysis of ratings, readability, and sentiments , 2012, Decis. Support Syst..

[23]  Astrid Dickinger,et al.  Analyzing destination branding and image from online sources: A web content mining approach , 2015 .

[24]  Peter O'Connor,et al.  User-Generated Content and Travel: A Case Study on Tripadvisor.Com , 2008, ENTER.

[25]  Bill Karakostas,et al.  The state of CRM adoption by the financial services in the UK: an empirical investigation , 2005, Inf. Manag..

[26]  Raffaele Filieri.  What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM , 2015 .

[27]  Sophia Ananiadou,et al.  Applications of text mining within systematic reviews , 2011, Research synthesis methods.

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

[29]  Z. Schwartz,et al.  What can big data and text analytics tell us about hotel guest experience and satisfaction , 2015 .

[30]  M. Ruiz-Molina,et al.  International Journal of Hospitality Management How Can Integrated Marketing Communications and Advanced Technology Influence the Creation of Customer-based Brand Equity? Evidence from the Hospitality Industry , 2022 .

[31]  A. Mattila,et al.  Restaurant Servicescape, Service Encounter, and Perceived Congruency on Customers' Emotions and Satisfaction , 2010 .

[32]  Karen L. Xie,et al.  The business value of online consumer reviews and management response to hotel performance. , 2014 .

[33]  Thomas A. Maier,et al.  Managing Customer Retention in Private Clubs Using Churn Analysis: Some Empirical Findings , 2016 .

[34]  J. Kandampully,et al.  Customer loyalty in the hotel industry: the role of customer satisfaction and image , 2000 .

[35]  Paulo Rita,et al.  Forecasting tomorrow’s tourist , 2016 .