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Identifying helpful online reviews: A product designer's perspective

机译：识别有用的在线评论：产品设计师的观点

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摘要

Large amounts of online reviews, the valuable voice of the customer, benefit consumers and product designers. Identifying and analyzing helpful reviews efficiently and accurately to satisfy both current and potential customers' needs have become a critical challenge for market-driven product design. Existing evaluation methods only use the review voting ratios given by customers to measure helpfulness. Due to the issues such as viewpoints of interest, technical proficiency and domain knowledge involved, it may mislead designers in identifying those truly valuable and insightful opinions from designers' perspective. Thus, in this study, we initiate our work to explore a possible approach that bridges the opinions expressed by consumers and the understanding gathered by designers in terms of how helpful these opinions are. Our ultimate research focus is on how to automatically evaluate the helpfulness of an online review from a designer's viewpoint entirely based on the review content itself. We start our work by first conducting an exploratory study to understand the fundamental question of what makes an online customer review helpful from product designers' viewpoint. Through our study, we propose four categories of features that reflect designers' concerns in judging review helpfulness. Based on our experiments, it reveals that discrepancy does exist between both online customer voting and designers' rating. Furthermore, for the cases where review ratings are not steadily available, we have proposed to use regression to predict and interpret review helpfulness with the help of the aforementioned four categories of features that are entirely extracted from review content. Finally, using review data crawled from Amazon.com and real ratings given by design personnel, it demonstrates the effectiveness of our proposal and it also suggests that helpful product reviews can be identified from a designer's angle by automatically analyzing the review content. We argue that the study reported is able to improve designer's ability in business intelligence processing by offering more targeted customer opinions. It highlights the urgency to uncover sensible user requirements from such quality opinions in our future research.
机译：大量的在线评论，客户的宝贵声音使消费者和产品设计师受益。高效，准确地识别和分析有用的评论，以满足当前和潜在客户的需求，已成为市场驱动型产品设计的关键挑战。现有的评估方法仅使用客户给出的评论投票率来衡量有用性。由于所涉及的兴趣，技术水平和领域知识等问题，可能会误导设计师，从设计师的角度识别那些真正有价值的见解的意见。因此，在这项研究中，我们开始工作以探索一种可能的方法，这些方法可以将消费者表达的观点与设计师在这些观点的帮助性方面收集的理解联系起来。我们的最终研究重点在于，如何完全基于评论内容本身，从设计师的角度自动评估在线评论的有用性。我们首先通过进行探索性研究来开始我们的工作，以从产品设计师的角度了解使在线客户评论有用的基本问题。通过我们的研究，我们提出了四类功能，这些功能反映了设计师在评估审阅有用性时所关注的问题。根据我们的实验，它表明在线客户投票与设计师评级之间确实存在差异。此外，对于无法持续获得评论评分的情况，我们建议借助回归来完全从评论内容中提取的上述四类功能来使用回归来预测和解释评论帮助。最后，使用从Amazon.com爬取的评论数据以及设计人员给出的真实评分，它证明了我们建议的有效性，并且还建议可以通过自动分析评论内容从设计师的角度识别有用的产品评论。我们认为，所报告的研究能够通过提供更具针对性的客户意见来提高设计人员在商业智能处理中的能力。它强调了在我们未来的研究中，从此类质量意见中发现合理的用户需求的紧迫性。

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