Fake Review Detection using Data Mining

Online spam reviews are deceptive evaluations of products and services. They are often carried out as a deliberate manipulation strategy to deceive the readers. Recognizing such reviews is an important but challenging problem. In this work, I try to solve this problem by using different data mining techniques. I explore the strength and weakness of those data mining techniques in detecting fake review. I start with different supervised techniques such as Support Vector Ma- chine (SVM), Multinomial Naive Bayes (MNB), and Multilayer Perceptron. The results attest that all the above mentioned supervised techniques can successfully detect fake review with more than 86% accuracy. Then, I work on a semi-supervised technique which reduces the dimension- ality of the input features vector but offers similar performance to existing approaches. I use a combination of topic modeling and SVM for the implementation of the semi-supervised tech- nique. I also compare the results with other approaches that consider all the words of a dataset as input features. I found that topic words are enough as input features to get similar accuracy compared to other approaches where researchers consider all the words as input features. At the end, I propose an unsupervised learning approach named as Words Basket Analysis for fake re- view detection. I utilize five Amazon products review dataset for an experiment and report the performance of the proposed on these datasets