  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.
