Data from: The Valuation of User-Generated Content: A Structural, Stylistic and Semantic Analysis of Online Reviews

This record contains the underlying research data for the publication "The Valuation of User-Generated Content: A Structural, Stylistic and Semantic Analysis of Online Reviews" and the full-text is available from: https://ink.library.smu.edu.sg/etd_coll/78The ability and ease for users to create and publish content has provided vast amount of online product reviews. However, the amount of data is overwhelmingly large and unstructured, making information difficult to quantify. This creates challenge in understanding how online reviews affect consumers’ purchase decisions. In my dissertation, I explore the structural, stylistic and semantic content of online reviews. Firstly, I present a measurement that quantifies sentiments with respect to a multi-point scale and conduct a systematic study on the impact of online reviews on product sales. Using the sentiment metrics generated, I estimate the weight that customers place on each segment of the review and examine how these segments affect the sales for a given product. The results empirically verified that sentiments influence sales, of which ratings alone do not capture. Secondly, I propose a method to detect online review manipulation using writing style analysis and assess how consumers respond to such manipulation. Finally, I find that societal norms have influence on posting behavior and significant differences do exist across cultures. Users should therefore exercise care in interpreting the information from online reviews. This dissertation advances our understanding on the consumer decision making process and shed insight on the relevance of online review ratings and sentiments over a sequential decision making process. Having tapped into the abundant supply of online review data, the results in this work are based on large-scale datasets which extend beyond the scale of traditional word-of-mouth research.

本记录包含了出版作品《用户生成内容的价值：在线评论的结构、风格和语义分析》的底层研究数据，全文可从以下链接获取：https://ink.library.smu.edu.sg/etd_coll/78。用户创建和发布内容的便捷能力催生了海量的在线产品评论。然而，数据量之大、结构之混乱，使得信息的量化变得异常困难。这给理解在线评论如何影响消费者的购买决策带来了挑战。在我的论文中，我探讨了在线评论的结构、风格和语义内容。首先，我提出了一种量化情感的多点刻度测量方法，并对在线评论对产品销售的影响进行了系统性研究。利用生成的情感指标，我估计了顾客对评论各部分所赋予的权重，并考察了这些部分如何影响特定产品的销售。实证结果表明，情感确实影响了销售，而单一的评级无法完全捕捉这一点。其次，我提出了一种基于写作风格分析来检测在线评论操纵的方法，并评估消费者对此类操纵的反应。最后，我发现社会规范对发布行为有影响，且不同文化之间存在显著差异。因此，用户在解读在线评论信息时应格外谨慎。本研究深化了我们对消费者决策过程的认知，并揭示了在线评论评级和情感在连续决策过程中的相关性。鉴于本研究充分利用了丰富的在线评论数据资源，其结果基于大规模数据集，超出了传统口碑研究的规模。