Optimization of Feature-Opinion Pairs in Chinese Customer Reviews

Authors:
Yongwen Huang;Zhongshi He;Haiyan Wang
Affiliations:
College of Computer Science, Chongqing University, Chongqing, China 400044;College of Computer Science, Chongqing University, Chongqing, China 400044;Art History Department, Sichuan Fine Arts Institute, Chongqing, China 400052
Venue:
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Year:
2009

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Abstract

Customer reviews mining can urge manufacturers to improve product quality and guide people a rational consumption. The commonly used mining methods are not satisfactory in precision of the features and opinions extracting. In this paper, we extracted the product features and opinion words in a unified process with semi-supervised learning algorithm, and made an adjustment of the threshold value of confidence to obtain a better mining performance, then adjusted the features sequence with big standard deviation, and maximized the harmonic-mean to raise the precision while ensured the recall. The experiment results show that our techniques are very effective.