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New Technology of Library and Information Service  2015, Vol. 31 Issue (12): 42-47    DOI: 10.11925/infotech.1003-3513.2015.12.07
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Implicit Feature Identification in Product Reviews
Zhang Li1, Xu Xin2
1 Department of Computer Science and Technology, Nanjing University, Nanjing 210093, China;
2 Department of Information Science, Bussiness School, East China Normal University, Shanghai 200241, China
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[Objective] Opinion mining in product areas draws more and more attention and becomes a hot research topic. The outcome of opinion mining can be used widely just like harmful information filtering, society opinion analysis, user consumption guidance and product improvement and so on. Implicit feature identification plays an important role because implicit features are common in network comments and the identification of them is difficult. [Methods] This paper uses the comments against a certain automobile brand which only have the explicit features to get refined multi-POS opinions and generate opinion clusters by using Synonyms Forests. Meanwhile identify opinions based on field common phrases. Dictionary in the form of {Feature, Opinion, Weight} is generated by using features and opinions, and the weight is calculated. Then deploy explicitly multi-strategy property extraction algorithm based on a dictionary and consider similarity of the opinions in unmatched comments including implicit features and dictionary. [Results] Implicit features can be extracted effectively and the F-value is 75.55% which reaches the good result of the identification of implicit features. [Limitations] Data labeling is a time-consuming job. [Conclusions] Experiment of the new algorithm shows positive result and has some practical value.

Received: 06 July 2015      Published: 06 April 2016
:  TP309  

Cite this article:

Zhang Li, Xu Xin. Implicit Feature Identification in Product Reviews. New Technology of Library and Information Service, 2015, 31(12): 42-47.

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