Employing Semi-Supervised and Supervised Learning to Discover False Online Ratings (International Journal of Innovative Research in Computer Science & Technology, 2023)
Today’s modern industry and trade,  internet evaluations matter a lot. Buying web items is often  influenced by the opinions of other customers. Because of  this, unscrupulous folks or organisations attempt to rig  customer evaluations to their personal advantage. Using a  lodging rating database, this research examines the  performance of semi-supervised (SSVD) and supervised  (SVD) word extraction methods for detecting false ratings.
Keywords: Online Products; Fake Reviews; Identifica-tion; Classification; E-Commerce