Detection of Fake Online Reviews using Semi-supervised and Supervised learning

Abstract

Abstract: Nowadays, when somebody wants to make some decisions about a product or a service everyone goes with the reviews as it has become an essential part of decision making. When a customer wants to order a product on an e commerce website firstly everyone checks the review section in detail and further proceeds for decision making about the product. If the reviews posted were satisfactory for the customer he may order the product thus reviews become a reputed parameter for the businesses and companies and also a great source of information for the customers. Every customer thinks that the reviews he/she is seeing is authentic and any manipulation in that from any individuals or any rival companies which may lead to fake data which will be labeled as fake reviews. This type of attempt if not noticed may let us think about the gen-unity of the data. So these reviews are the most important parameter for the businesses and companies. There exist some groups or persons who make use of these reviews to forge customers for their own interest or damage their competitors reputation. In order to solve this problem we uses Machine learning techniques(Supervised and semi-supervised) to detect whether the given review is fake or not with high accuracy. Along with this objective we also focus on developing models which need less data to train.Since we can’t always be able to get labeled data we use semi-supervised machine learning to make use of unlabeled data.It is understandable our model should be capable of giving results in reasonably less time. .In this paper we proposed many classification algorithm like Support Vector Machine algorithm (SVM) , Random Forest algorithm (RF) and deep neural network