Deciphering word-of-mouth in social media: Text-based metrics of consumer reviews

Zhu Zhang, Xin Li, Yubo Chen
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引用次数: 101

Abstract

Enabled by Web 2.0 technologies, social media provide an unparalleled platform for consumers to share their product experiences and opinions through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics influence consumer purchases and product sales. By integrating marketing theories with text mining techniques, we propose a set of novel measures that focus on sentiment divergence in consumer product reviews. To test the validity of these metrics, we conduct an empirical study based on data from Amazon.com and BN.com (Barnes & Noble). The results demonstrate significant effects of our proposed measures on product sales. This effect is not fully captured by nontextual review measures such as numerical ratings. Furthermore, in capturing the sales effect of review content, our divergence metrics are shown to be superior to and more appropriate than some commonly used textual measures the literature. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, our results suggest that firms should pay special attention to textual content information when managing social media and, more importantly, focus on the right measures.
解读社交媒体中的口碑:基于文本的消费者评价指标
在Web 2.0技术的支持下,社交媒体为消费者提供了一个无与伦比的平台,通过口碑(WOM)或消费者评论来分享他们的产品体验和意见。了解口碑内容和指标如何影响消费者购买和产品销售变得越来越重要。通过将营销理论与文本挖掘技术相结合,我们提出了一套关注消费者产品评论中情绪分歧的新措施。为了检验这些指标的有效性,我们基于亚马逊和BN.com (Barnes & Noble)的数据进行了实证研究。结果表明,我们提出的措施对产品销售有显著的影响。这种影响不能被非文本的评审方法如数值评价完全捕捉到。此外,在捕捉评论内容的销售效果时,我们的发散度量被证明优于和比一些常用的文献文本度量更合适。这些发现对社交媒体和用户生成内容的商业影响提供了重要见解,这是商业智能研究中的一个新问题。从管理的角度来看,我们的研究结果表明,企业在管理社交媒体时应特别注意文本内容信息,更重要的是,关注正确的措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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