Spam Detection in Online Comments Based on Feature Weight Breakdown

Abstract

The user reviews posted online by the Internet users about a product plays a vital role in determining its success in the market. The reviews also influence the purchase decision of the consumers. The chances of getting cheated by fake reviews are very high because detecting spams in reviews is not an easy task either manually or automatically. Hence there is a need to evolve new techniques and methods to outperform the smartness of spammers. In this paper, we propose a Heterogeneous Feature Weight Analysis framework for extracting various features related to the review and certain parameters are calculated from these features to form a pattern for deceptive reviews. The features associated with the review are review content, review rating and user centric characteristics which are pulled out from the dataset retrieved from Amazon. This analysis has helped us to categorize reviews into normal and suspicious reviews. We have executed our algorithm in Python software and were able to achieve an accuracy of 71.6% inprediction.