Detection of Fake Online Reviews Using Semi-Supervised and Supervised Learning

Social media’s growth has made online data research crucial to comprehending consumer behaviour. In order to assess consumers’ thoughts on movies, this study focuses on sentiment analysis of Twitter tweets. To find hidden trends, a lexicon that includes internet reviews and social media terms is created. As e-commerce has grown, internet reviews have a big impact on what people decide to buy. On the other hand, dishonest material has increased as a result of the financial incentives linked to false reviews. False reviews have the potential to increase or decrease sales. This study uses support vector machines, logistic regression, and the Naïve Bayes classifier to determine if reviews are authentic or fraudulent.