Mining Context Information from Consumer ’ s Reviews

Consumer reviews, opinions and shared experiences are popular ways to express preferences and interest of tourists in most of the popular tourism sites inside of the typical valuation of product using any valuation scale. The most critical issue on opinion mining is how to extract information that can be understood and utilized by computers from written text by users/consumers in natural language. Several approaches using artificial intelligence have been used to deal with this problem even so; problem that has been less addressed but not less important is the identification of context information embedded in consumer's opinions due the arduous task of processing natural language in which reviews have been expressed. This paper addresses this problem based on classification text mining techniques to identify review's sentences containing contextual information to be then processing and incorporated in a recommender system. This approach was exemplified by a case study using reviews from www.tripadvisor.com.

[1]  S. Sénécal,et al.  The influence of online product recommendations on consumers' online choices , 2004 .

[2]  Pasi Fränti,et al.  Web Data Mining , 2009, Encyclopedia of Database Systems.

[3]  Nicolas Curien,et al.  Internet and Digital Economics: Online consumer communities: escaping the tragedy of the digital commons , 2007 .

[4]  Tong Zhang,et al.  Text Mining: Predictive Methods for Analyzing Unstructured Information , 2004 .

[5]  Ronen Feldman,et al.  Book Reviews: The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data by Ronen Feldman and James Sanger , 2008, CL.

[6]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

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

[8]  Lixuan Zhang,et al.  An Exploratory Study of Travelers' Use of Online Reviews and Recommendations , 2009, J. Inf. Technol. Tour..

[9]  Chrysanthos Dellarocas,et al.  The Digitization of Word-of-Mouth: Promise and Challenges of Online Feedback Mechanisms , 2003, Manag. Sci..

[10]  Janyce Wiebe,et al.  Development and Use of a Gold-Standard Data Set for Subjectivity Classifications , 1999, ACL.

[11]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[12]  Chih-Ping Wei,et al.  Turning Online Product Reviews to Customer Knowledge: A Semantic-based Sentiment Classification Approach , 2006, PACIS.

[13]  John K. Debenham,et al.  Recommender System Based on Consumer Product Reviews , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[14]  Chrysanthos Dellarocas,et al.  Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms , 2004, Manag. Sci..

[15]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[16]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[17]  Chih-Ping Wei,et al.  Classifying web review opinions for consumer product analysis , 2009, ICEC.