Using Boosting Approaches to Detect Spam Reviews

When it comes down to buying products from online shops, one of the key factor that influences a buyer are the reviews associated with a product. While buying people try to understand the quality and authenticity of the product by reading the previous user feedback. And sellers have started taking advantage of it. Putting fake and spam reviews to deceive the buyers is a common strategy mostly used by newcomers. But these reviews are important when it comes to deciding whether to buy a product or not. We propose a method to detect these fake reviews from Amazon Review Dataset. Rather than using traditional machine learning classifiers we have used boosting algorithms to improve the accuracy of the traditional approach. In this approach, a significant increase in accuracy has been achieved by boosting weak learners. Up to 93% accuracy has been achieved when tried to detect fake reviews where traditional machine learning algorithms achieve an accuracy of up to 89%.