Journal of Digital Information Management Mayank Saini, Aditi Sharan 15 2 2017 http://dline.info/fpaper/jdim/v15i2/jdimv15i2_4.pdf In the current era of Internet, people are increasingly using the e-commerce websites for purchasing goods and services. Reviews and blogs have become the prime source of information for making purchasing decisions. As reviews and blogs directly affect sales and revenue, many e-commerce companies hire people for writing reviews to promote or demote target products and services. These fictitious opinions that are written to sound authentic are known as deceptive reviews. In this paper, we tried to establish a link between personality traits and deceptive/fake reviews. We analyzed personality recognition techniques and deceptive review detection from a psycholinguistic point of view. We tried to capture stable individual characteristics to predict behavioral differences between deceptive and truthful reviewer/review. This study shows that personality clues along with other review specific features can be quite successful to build automatic deceptive review classifiers. We have used various ensemble learning techniques to ensure effective use of the features and achieve good classification accuracy. Our experiments on restaurant and hotel domain have achieved up to 93 and 94 percent accuracy respectively with the final classifier.