Mining Product Reviews for Spam Detection Using Supervised Technique

By the introduction of web 2.0 and cheap accessibility of web, many merchant sites are operating and providing space for their users to share their experiences in form of customer reviews. Such reviews contain precious knowledge useful for both customers as well as manufacturers. E-customer accesses these reviews to know opinion expressed by existing users on a product before making purchase decision. Further, such reviews are used by manufactures to know shortcoming in their existing products as well as to know strength of a competitor products for making business plans. Since Internet has no quality control, anyone can write anything which results in low quality reviews that contain biased information known as spam, and may mislead the customer affecting his buying decisions. Thus, it is very essential to have a mechanism which is capable of assessing the trustworthiness of reviews for proper decision making or for marketing intelligence. In this paper we propose a supervised method for spam detection. Dataset are taken from merchant sites like amazon.com. However our experimental results show our proposed method is very effective over the existing method. Keywords-Text Mining, Feature Extraction, opinion Mining