Consumer-generated reviews have become increasingly important in decision-making processes for customers. Meanwhile, the overwhelming quantity of review data makes it extremely difficult to find useful information from it. A considerable amount of studies have attempted to address this problem by selecting reviews that might be helpful for and preferred by users. However, the performance of existing methods is far from ideal. One reason is because of lacking effective criteria to assess the quality of reviews. In this paper, we propose two novel measures, i.e. feature relevance and feature comprehensiveness, to assess the quality of reviews in terms of review content. A review selection approach is presented to select a set of reviews with high quality based on the two measures. Experiments on real-world review datasets show that our proposed method can assess the review quality effectively to improve the performance of review selection.

中文翻译：

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根据内容质量审核选择

消费者生成的评论在客户的决策过程中变得越来越重要。同时，大量的评论数据使从中查找有用的信息变得极为困难。大量研究试图通过选择可能对用户有帮助并受到用户偏爱的评论来解决此问题。但是，现有方法的性能远非理想。原因之一是缺乏评估评论质量的有效标准。在本文中，我们提出两种新颖的措施，即特征相关性和特征全面性，以根据评论内容评估评论的质量。提出了一种评论选择方法，可以基于这两种方法来选择一组高质量的评论。