REVIEW DRIVEN CUSTOMER SEGMENTATION FOR IMPROVED E-SHOPPING EXPERIENCE

Despite intriguing commercial possibility, product search on the Web and e-shopping applications still strive to offer satisfying customer experience. The major challenge probably is to harness the power of user generated content in the form of reviews. Using the example of cell phones this paper demonstrates that user reviews, opinions, and product ratings may actually severely differ with respect to the intended product usage of individual customers or groups. Investigating individual task-based rating behavior, we show that customer segmentation paired with intuitive interface paradigms like faceted search, promises to significantly enhance user experience by combating the information flood.

[1]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[2]  Lanny W. Martin,et al.  A Robust Transformation Procedure for Interpreting Political Text , 2007, Political Analysis.

[3]  M. Laver,et al.  Extracting Policy Positions from Political Texts Using Words as Data , 2003, American Political Science Review.

[4]  Wolf-Tilo Balke,et al.  Conceptual views for entity-centric search: turning data into meaningful concepts , 2012, Computer Science - Research and Development.

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

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

[7]  Hua Xu,et al.  Clustering product features for opinion mining , 2011, WSDM '11.

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

[9]  Bing Liu,et al.  Mining Opinion Features in Customer Reviews , 2004, AAAI.

[10]  Masatoshi Yoshikawa,et al.  Topic and Viewpoint Extraction for Diversity and Bias Analysis of News Contents , 2009, APWeb/WAIM.

[11]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[12]  Eleanor Rosch,et al.  Principles of Categorization , 1978 .

[13]  Ravi Kumar,et al.  A Characterization of Online Search Behavior , 2009, IEEE Data Eng. Bull..

[14]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.