TY - GEN
T1 - Opinion extraction from customer reviews
AU - Loh, Han Tong
AU - Sun, Jie
AU - Wang, Jingjing
AU - Lu, Wen Feng
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77953787651&partnerID=8YFLogxK
U2 - 10.1115/DETC2009-86355
DO - 10.1115/DETC2009-86355
M3 - Conference Proceeding
AN - SCOPUS:77953787651
SN - 9780791848999
T3 - Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2009, DETC2009
SP - 753
EP - 758
BT - Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2009, DETC2009
T2 - 2009 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC2009
Y2 - 30 August 2009 through 2 September 2009
ER -