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New Technology of Library and Information Service  2016, Vol. 32 Issue (2): 16-24    DOI: 10.11925/infotech.1003-3513.2016.02.03
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Extracting Product Feature and User Opinion from Chinese Reviews
Yuan Meng(),Hongwei Wang
School of Economics and Management, Tongji University, Shanghai 210000, China
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[Objective] This study proposed a confidence ranking model to extract product feature and user opinion from the Chinese online reviews. [Methods] Examining the semantic and association relations between candidate words, we built the confidence ranking model based on the improved HITS algorithm, and then retrieved the feature and opinion words. [Results] Compared with the reference model, our method showed better recall and precision rates while extracting the feature and opinion words from the Chinese corpus. [Limitations] Only extracted the explicit feature and opinion words, and did not try to identify and extract the implicit ones. [Conclusions] We could effectively extract the feature and opinion words using their mutual reinforcement and semantic relations. Filtering method of the semantic polarity could also improve the precision of the extracted opinion words.

Key wordsConfidence ranking      HITS      Association relation      Semantic relation      Mutual reinforcement      Feature opinion extraction     
Received: 28 August 2015      Published: 08 March 2016

Cite this article:

Yuan Meng, Hongwei Wang. Extracting Product Feature and User Opinion from Chinese Reviews. New Technology of Library and Information Service, 2016, 32(2): 16-24.

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