A Statistical NLP Approach for Feature and Sentiment Identification from Chinese Reviews

نویسندگان

  • Zhen Hai
  • Kuiyu Chang
  • Qinbao Song
  • Jung-jae Kim
چکیده

Existing methods for extracting features from Chinese reviews only use simplistic syntactic knowledge, while those for identifying sentiments rely heavily on a semantic dictionary. In this paper, we present a systematic technique for identifying features and sentiments, using both syntactic and statistical analysis. We firstly identify candidate features using a proposed set of common syntactic rules. We then prune irrelevant candidates with topical relevance scores below a cut-off point. We also propose an association analysis method based on likelihood ratio test to infer the polarity of opinion word. The sentiment of a feature is finally adjusted by analyzing the negative modifiers in the local context of the opinion word. Experimental results show that our system performs significantly better than a well-known opinion mining system.

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تاریخ انتشار 2010