Mining comparative opinions from customer reviews for Competitive Intelligence
Mining comparative opinions from customer reviews for Competitive Intelligence
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DOI：
10.1016/j.dss.2010.08.021
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发表时间：
2011-03
期刊：
影响因子：
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通讯作者：
Kaiquan Xu;S. Liao;Jiexun Li;Yuxia Song
中科院分区：
文献类型：
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作者：
Kaiquan Xu;S. Liao;Jiexun Li;Yuxia Song
竞争情报是企业风险管理和决策支持的关键因素之一。然而，竞争情报的功能往往因缺乏足够的竞争对手信息来源而受到很大限制。随着Web 2.0的出现，大量客户生成的产品评论往往包含竞争对手的信息，成为挖掘竞争情报的新来源。在本研究中，我们提出了一种新颖的图形模型，从客户评论中提取和可视化产品之间的比较关系，并考虑关系之间的相互依赖关系，帮助企业发现潜在风险并进一步设计新产品和营销策略。我们对亚马逊客户评论语料库的实验表明，我们提出的方法可以比基准方法更准确地提取比较关系。此外，这项研究为分析消费者生成的丰富数据以进行企业风险管理打开了大门。
Competitive Intelligence is one of the key factors for enterprise risk management and decision support. However, the functions of Competitive Intelligence are often greatly restricted by the lack of sufficient information sources about the competitors. With the emergence of Web 2.0, the large numbers of customer-generated product reviews often contain information about competitors and have become a new source of mining Competitive Intelligence. In this study, we proposed a novel graphical model to extract and visualize comparative relations between products from customer reviews, with the interdependencies among relations taken into consideration, to help enterprises discover potential risks and further design new products and marketing strategies. Our experiments on a corpus of Amazon customer reviews show that our proposed method can extract comparative relations more accurately than the benchmark methods. Furthermore, this study opens a door to analyzing the rich consumer-generated data for enterprise risk management.