基于累积相对频率分布的垃圾意见检测方法
Improving Opinion Spam Detection by Cumulative Relative Frequency Distribution
摘要：
在过去的几年里，在线评论变得非常重要，因为它们可以影响消费者的购买决定和企业的声誉，因此，撰写虚假评论的做法可能会对客户和服务提供商造成严重后果。已经提出了各种方法来检测在线评论中的意见垃圾，特别是基于监督分类器。在这篇文章中，我们从一组用于分类意见垃圾邮件的有效功能开始，通过考虑每个功能的累积相对频率分布，我们重新设计了它们。通过对来自Yelp.com的真实数据进行的实验评估，我们表明使用分布特征能够提高分类器的性能。
英文摘要：
Over the last years, online reviews became very important since they can influence the purchase decision of consumers and the reputation of businesses, therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers. In this contribution, we start from a set of effective features used for classifying opinion spam and we re-engineered them, by considering the Cumulative Relative Frequency Distribution of each feature. By an experimental evaluation carried out on real data from Yelp.com, we show that the use of the distributional features is able to improve the performances of classifiers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
