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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Huang, Y., He, Z., & Wang, H. (2009). Optimization of feature-opinion pairs in chinese customer reviews. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5579 LNAI, pp. 747–756). https://doi.org/10.1007/978-3-642-02568-6_76
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