Machine Learning Algorithms are Used for Fake Review Detection

An internet business’s revenue can be significantly impacted by user reviews. Online users read reviews before choosing any products or services. Because of this, a company’s profitability and reputation are directly impacted by the reliability of internet reviews. Due to this, some businesses pay spammers to post false reviews. These false reviews abuse the purchase decisions of customers. The approaches for feature extraction currently in use are examined. We will see an injustice in these ratings and reviews. The text analysis method used in this study was sentiment analysis (SA); currently, the area of text analysis attracting the greatest interest. One of the main issues SA is presently experiencing is how to differentiate between negative, neutral, and positive opinion reviews. In this article, We contrast supervised and unsupervised machine learning particularly. Our research demonstrates that supervised machine learning is more accurate and effective than unsupervised learning.