Feature taxonomy learning from user generated content and application in review selection

This thesis developed new methods to find useful information from massive customer generated product review data in order to assist customers in decision making. It first examines the distinct features of review text to find useful information about the reviewed product using a number of existing techniques. Then, based upon derived product information, this thesis developed novel methods to assess the review quality in order to find most useful or helpful product reviews for customers