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Opinion Extraction From Customer Reviews

[+] Author Affiliations
Han Tong Loh, Jie Sun, Jingjing Wang, Wen Feng Lu

National University of Singapore, Singapore

Paper No. DETC2009-86355, pp. 753-758; 6 pages
  • ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 29th Computers and Information in Engineering Conference, Parts A and B
  • San Diego, California, USA, August 30–September 2, 2009
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4899-9 | eISBN: 978-0-7918-3856-3
  • Copyright © 2009 by ASME


The internet offers a new channel for product designers to obtain valuable information about customer’s opinions which are very important to product development, especially at the product concept design stage. Due to the rapid growth of such information, it is difficult for humans to manage and analyze all these information. Therefore, an alternative choice is to perform opinion mining with automatic textual mining techniques. In this research, we propose a hybrid opinion extraction (HOE) framework that can extract features and predict semantic orientation of the expressed opinions, from the free format text. The framework is inspired by capturing the characteristics of the way people express opinions, utilizes both statistical regularities of the patterns and some prior knowledge. Compared to previous work, our opinion mining technique has demonstrated its better performance in terms of extracting features and predicting semantic orientations of opinions. Thus it has the potential to be adopted by product designers as an efficient tool for quickly obtaining customer feedback.

Copyright © 2009 by ASME



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