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EXPRS: An extended pagerank method for product feature extraction from online consumer reviews

  • Zhijun Yan
  • , Meiming Xing
  • , Dongsong Zhang*
  • , Baizhang Ma
  • *此作品的通讯作者
    • Beijing Institute of Technology
    • University of Maryland, Baltimore County

    科研成果: 期刊稿件文章同行评审

    摘要

    Online consumer product reviews are a main source for consumers to obtain product information and reduce product uncertainty before making a purchase decision. However, the great volume of product reviews makes it tedious and ineffective for consumers to peruse individual reviews one by one and search for comments on specific product features of their interest. This study proposes a novel method called EXPRS that integrates an extended PageRank algorithm, synonym expansion, and implicit feature inference to extract product features automatically. The empirical evaluation using consumer reviews on three different products shows that EXPRS is more effective than two baseline methods.

    源语言英语
    页(从-至)850-858
    页数9
    期刊Information and Management
    52
    7
    DOI
    出版状态已出版 - 1 11月 2015

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