Online reviews play an integral part for success or failure of businesses.
Prior to purchasing services or goods, customers first review the online
comments submitted by previous customers. However, it is possible to
superficially boost or hinder some businesses through posting counterfeit and
fake reviews. This paper explores a natural language processing approach to
identify fake reviews. We present a detailed analysis of linguistic features
for distinguishing fake and trustworthy online reviews. We study 15 linguistic
features and measure their significance and importance towards the
classification schemes employed in this study. Our results indicate that fake
reviews tend to include more redundant terms and pauses, and generally contain
longer sentences. The application of several machine learning classification
algorithms revealed that we were able to discriminate fake from real reviews
with high accuracy using these linguistic features.

本文探讨了一种处理虚假评论的自然语言处理方法，通过研究 15 种语言特征，证明了虚假评论往往包含更多的废话和长句子，并且应用机器学习算法可以高准确率地辨别出虚假评论。