...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Engineering Applications of Artificial Intelligence >Extracting and summarizing affective features and responses from online product descriptions and reviews: A Kansei text mining approach
【24h】

Extracting and summarizing affective features and responses from online product descriptions and reviews: A Kansei text mining approach

机译：从在线产品描述和评论中提取和总结情感特征和响应：Kansei文本挖掘方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Today's product design takes into account the affective aspects of products, such as aesthetics and comfort, as much as reliability and physical quality. Manufacturers need to understand the consumers' affective preferences and responses to product features in order to improve their products. Conventional approaches use manual methods, such as questionnaires and surveys, to discover product features and affective preferences, and then correlate their relationships. This is one-time, labour-intensive, and time-consuming process. There is a need to develop an automated and unsupervised method to efficiently identify the affective information. In particular, text mining is an automatic approach to extract useful information from text, while Kansei engineering studies product affective attributes. In this paper, we propose a Kansei text mining approach which incorporates text mining and Kansei engineering approaches to automatically extract and summarize product features and their corresponding affective responses based on online product descriptions and consumer reviews. Users can efficiently and timely review the affective aspects of the products. In order to evaluate the effectiveness of the proposed approach, experiments have been conducted on the basis of public data from Amazon.com. The results showed that the proposed approach can effectively identify the affective information in terms of feature affective opinions. In addition, we have developed a prototype system that visualizes product features, affective attributes, affective keywords, and their relationships. The proposed approach not only helps consumers making purchase decisions, but also helps manufacturers understanding their products and competitors' products, which might provide insights into their product development.
机译：当今的产品设计考虑到了产品的情感方面，例如美观和舒适性以及可靠性和物理质量。制造商需要了解消费者的情感偏好和对产品功能的反应，以改善他们的产品。传统方法使用手动方法（例如问卷和调查）来发现产品特征和情感偏好，然后关联它们之间的关系。这是一次性的，劳动密集型的且耗时的过程。需要开发一种自动且无监督的方法以有效地识别情感信息。特别是，文本挖掘是一种从文本中提取有用信息的自动方法，而Kansei工程公司则研究产品的情感属性。在本文中，我们提出了一种Kansei文本挖掘方法，该方法结合了文本挖掘和Kansei工程方法，可以基于在线产品描述和消费者评论自动提取和汇总产品功能及其相应的情感响应。用户可以高效，及时地查看产品的情感方面。为了评估所提出方法的有效性，已经基于Amazon.com的公共数据进行了实验。结果表明，该方法能够有效地根据特征情感观点识别情感信息。此外，我们还开发了一个原型系统，该系统可以可视化产品功能，情感属性，情感关键字及其关系。所提出的方法不仅可以帮助消费者做出购买决定，还可以帮助制造商了解他们的产品和竞争对手的产品，从而可以提供有关其产品开发的见解。

著录项

  • 来源
  • 作者

    Wang W. M.; Li Z.; Tian Z. G.; Wang J. W.; Cheng M. N.;

  • 作者单位

    Guangdong Univ Technol, Sch Electromech Engn, Guangdong Prov Key Lab Comp Integrated Mfg Syst, Guangzhou 510006, Guangdong, Peoples R China;

    Guangdong Univ Technol, Sch Electromech Engn, Guangdong Prov Key Lab Comp Integrated Mfg Syst, Guangzhou 510006, Guangdong, Peoples R China;

    Guangdong Univ Technol, Sch Electromech Engn, Guangdong Prov Key Lab Comp Integrated Mfg Syst, Guangzhou 510006, Guangdong, Peoples R China;

    Guangdong Univ Technol, Sch Electromech Engn, Guangdong Prov Key Lab Comp Integrated Mfg Syst, Guangzhou 510006, Guangdong, Peoples R China;

    Hong Kong Polytech Univ, Knowledge Management & Innovat Res Ctr, Dept Ind & Syst Engn, Hong Kong 999077, Hong Kong, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Affective mining; Opinion mining; Customer reviews; Affective design; Kansei engineering;

    机译：情感挖掘;意见挖掘;客户评论;情感设计;关西工程;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号