Opinion mining using ontological spam detection

Abstract

In the current scenario, the data on the web is growing exponentially. Social media is generating a large amount of data such as reviews, comments, and customers' opinions on a daily basis. This huge amount of user generated data is worthless unless some mining operations are applied to it. As there are a number of fake reviews so opinion mining technique should incorporate Spam detection to produce a genuine opinion. Nowadays, there are a number of people using social media opinions to create their call on shopping for product or service. Opinion Spam detection is an exhausting and hard problem as there are many faux or fake reviews that have been created by organizations or by the people for various purposes. They write fake reviews to mislead readers or automated detection system by promoting or demoting target products to promote them or to or to degrade their reputations. In this paper, the proposed technique includes ontology, Geo location and IP address tracking, Spam words Dictionary using Naive Bayes, Brand only review detection and tracking account used. Experimental results dictate its better results over its nearest competitors.