Title :
Will I Like It? Providing Product Overviews Based on Opinion Excerpts
Author :
Homoceanu, Silviu ; Loster, Michael ; Lofi, Christoph ; Balke, Wolf-Tilo
Author_Institution :
Inst. fur Informationssyst., Tech. Univ. Braunschweig, Braunschweig, Germany
Abstract :
With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the plethora of online offers. Thus, techniques for personalization and shopping assistance are in high demand by users, as well as by shopping platforms themselves. For a pleasant and successful shopping experience, users should be empowered to easily decide on which products to buy with high confidence. However, especially for entertainment goods like e.g. movies, books, or music, this task is very challenging. Unfortunately, to days approaches for dealing with this challenge (like e.g. recommender systems) suffer severe drawbacks: recommender systems are completely opaque, i.e. the recommendation is hard to justify semantically. User reviews could help users to form an opinion of recommended items, but with several thousand reviews available for e.g. a given popular movie, it is very challenging for users to find representative reviews. In this paper, we propose a novel technique for automatically analyzing user reviews using advanced opinion mining techniques. The results of this analysis are then used to group reviews by their semantics, i.e. by their contained opinions and point-of-views. Furthermore, the relevant paragraphs with respect to each opinion is extracted and presented to the user. These extracts can easily be digested by users to allow them a quick and diverse forming of opinion, and thus increasing their confidence in their decision, and their overall customer satisfaction.
Keywords :
customer satisfaction; data mining; electronic commerce; recommender systems; retail data processing; advanced opinion mining techniques; customer satisfaction; e-commerce platforms; opinion excerpts; personalization assistance technique; product overviews; recommender systems; shopping assistance technique; user reviews; Avatars; Business; Databases; Feature extraction; Motion pictures; Recommender systems; Semantics; E-Commerce; Human Computer Interaction; Knowledge Management; Opinion-Mining;
Conference_Titel :
Commerce and Enterprise Computing (CEC), 2011 IEEE 13th Conference on
Conference_Location :
Luxembourg
Print_ISBN :
978-1-4577-1542-6
Electronic_ISBN :
978-0-7695-4535-6
DOI :
10.1109/CEC.2011.12