Summarization and Prioritization of Amazon Reviews based on multi-level credibility attributes

N. Aishwarya, Bhuvana L S, K. N.
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引用次数: 2

Abstract

It is common for most people to check Amazon reviews of competing products before a purchase decision. However, the ratings and reviews could be from a customer who has not actually purchased the product on Amazon. There is also a chance that the reviews are doctored by paid reviewers either to enhance a particular product's appeal or lower that of a competitor's product. So, the ratings and reviews do not always reflect the reality. We provide a mechanism to eliminate un-authenticated reviews prioritizing them based on their credibility score and summarize both positive and negative keywords. In this paper, we present a methodology to eliminate un-authenticated reviews based on three levels of pruning. The final rating of the review is adjusted using helpfulness ratio of the review, how old the review is and the helpfulness and experience of the reviewer. We also summarize the positive and negative keywords using ‘term frequency-inverse document frequency’ and ‘Long Short-Term Memory networks’. The reviews are prioritized the based on their credibility score. We were able to summarize and prioritize the reviews for easy analysis by the user. We also did an empirical validation of our proposed solution and found that the overall helpfulness factors improved.
基于多层次可信度属性的亚马逊评论的汇总和优先排序
对于大多数人来说,在购买决定之前查看亚马逊对竞争产品的评论是很常见的。然而,评级和评论可能来自没有在亚马逊上实际购买该产品的客户。还有一种可能是,评论被付费的评论者篡改,以增强某一特定产品的吸引力,或降低竞争对手产品的吸引力。因此,评级和评论并不总是反映现实。我们提供了一种机制来消除未经认证的评论,根据它们的可信度评分对它们进行优先级排序,并总结正面和负面关键字。在本文中,我们提出了一种基于三个级别的修剪来消除未经认证的评论的方法。评审的最终评级是根据评审的有用性比率、评审的年龄以及审稿人的有用性和经验来调整的。我们还使用“词频-逆文档频率”和“长短期记忆网络”来总结正负关键词。这些评论是根据它们的可信度评分进行优先排序的。我们能够对评论进行总结和排序,以便于用户进行分析。我们还对我们提出的解决方案进行了实证验证,发现整体的帮助因素有所改善。
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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