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首页> 外文期刊>Decision support systems >Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach
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Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach

机译：抓住口碑传播的社会商务本质：通过半监督方法评估在线电子商务评论的质量

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摘要

In e-commerce, online product reviews significantly influence the purchase decisions of buyers and the marketing strategies employed by vendors. However, the abundance of reviews and their uneven quality make distinguishing between useful and useless reviews difficult for potential customers, thereby diminishing the benefits of online review systems. To address this problem, we develop a semi-supervised system called Online Review Quality Mining (ORQM). Embedded with independent component analysis and semi-supervised ensemble learning, ORQM exploits two opportunities: the improvement of classification performance through the use of a few labeled instances and numerous unlabeled instances, and the effectiveness of the social characteristics of e-commerce communities as identifiers of influential reviewers who write high-quality reviews. Three complementary experiments on datasets from Amazon.com show that ORQM exhibits remarkably higher performance in classifying reviews of different quality levels than do other well-accepted state-of-the-art text mining methods. The high performance of ORQM is also consistent and stable even under limited availability of labeled instances, thereby outperforming other baseline methods. The experiments also reveal that (1) the social features of reviewers are important in deriving better classification results; (2) classification results are affected by product type given the different purchase habits of consumers; and (3) reviews are contingent on the inherent nature of products, such as whether they are search goods or experience goods, and digital products or physical products, through which purchase decisions are influenced.
机译：在电子商务中，在线产品评论会显着影响买方的购买决策和卖方所采用的营销策略。但是，评论的丰富性和质量参差不齐，使潜在客户很难区分有用评论和无用评论，从而削弱了在线评论系统的优势。为了解决此问题，我们开发了一种称为“在线评论质量挖掘”（ORQM）的半监督系统。嵌入独立成分分析和半监督集成学习的ORQM抓住了两个机会：通过使用一些标记实例和许多未标记实例来提高分类性能，以及电子商务社区的社会特征作为标识符的有效性。撰写高质量评论的有影响力的评论者。对Amazon.com数据集的三个补充实验表明，与其他公认的最新文本挖掘方法相比，ORQM在对不同质量级别的评论进行分类时表现出显着更高的性能。即使在标记实例的可用性有限的情况下，ORQM的高性能也是一致且稳定的，从而胜过其他基准方法。实验还表明：（1）审稿人的社会特征对于获得更好的分类结果很重要； （2）由于消费者的购买习惯不同，分类结果受产品类型的影响； （3）评估取决于产品的固有性质，例如，它们是搜索商品还是体验商品，以及数字产品或实物产品，它们会影响购买决策。

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  • 来源
    《Decision support systems》 |2013年第12期|211-222|共12页
  • 作者

    Xiaolin Zheng; Shuai Zhu; Zhangxi Lin;

  • 作者单位

    College of Computer Science,Zhejiang University,No.38, Zheda Rood, Hangzhou 310027, China;

    College of Computer Science, Zhejiang University, No. 38, Zheda Rood, Hangzhou 310027, China;

    Center for Advanced Analytics and Business Intelligence, Texas Tech University, Lubbock, TX 79409-2101, USA,Key Lab of Financial Intelligence and Financial Engineering, Southwestern University of Finance and Economics, Chengdu, Sichuan, 611130, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Online review; Review quality; Review mining; Semi-supervised learning; Social network;

    机译：在线审查;评审质量;审查采矿;半监督学习;社交网络;

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