Ensemble Learning to Find Deceptive Reviews using Personality Traits and Reviews Specific Features
Mayank Saini, Aditi Sharan
Journal of Digital Information Management, Vol. 15, No. 2, 2017.
DOI: 
Link: http://dline.info/fpaper/jdim/v15i2/jdimv15i2_4.pdf
Abstract:
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.