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Aspect-level opinion mining of online customer reviews

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5 Author(s)
Xu Xueke ; Key Lab. of Web Data Sci. & Technol., Inst. of Comput. Technol., Beijing, China ; Cheng Xueqi ; Tan Songbo ; Liu Yue
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This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspect-dependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspect- dependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.

Published in:

Communications, China  (Volume:10 ,  Issue: 3 )