A Supervised Machine Learning Approach to Detect Fake Online Reviews
Rakibul Hassan, Md. Rabiul Islam
Abstract
With increasing use of internet, online business platforms are becoming the largest market place of the world. Purchase of online product is heavily dependent on user reviews. Some dishonest groups of people misuse this fact by posting fake reviews to promote their own products or demote their competitors. Detection of fake online reviews can be considered as a binary classification task that models a classifier to tell whether a review is fake or true. In this paper, we have developed an effective supervised machine learning approach to classify fake online reviews using a dataset that contains hotel reviews from online websites.
Topics & Concepts
Competitor analysisComputer scienceBinary classificationThe InternetMachine learningFake newsArtificial intelligenceProduct (mathematics)Supervised learningClassifier (UML)Task (project management)World Wide WebData scienceInternet privacySupport vector machineEngineeringArtificial neural networkMarketingMathematicsGeometryBusinessSystems engineeringSpam and Phishing DetectionAdvanced Malware Detection TechniquesMisinformation and Its Impacts