DETECTION OF FAKE REVIEWS USING MACHINE LEARNING

The rapid growth of online reviews has revolutionized how consumers make purchasing decisions. However, this digital landscape has also birthed a pervasive problem. It will have the challenges associated with counterfeit online reviews. It is often generated by individuals with vested interests, who aim to manipulate public perception, boost sales or train the competitors/ reputations. Their prevalence is alarming with countless businesses resorting to this deceptive tactic. It will explore the multifaceted impact of fake reviews on consumers, businesses, and online platforms. Online platforms grapple with the task of policy and removing fake online reviews from their platforms. There are many varieties of online review fraud one of the most prevalent, which comes with the highest risk to your business, is playing for disingenuous and fraudulent customers feedback it can encompass any feedback that is not a genuine opinion or experience related to a product, service or business they can either positive, neutral or negative depending on whether the intent is to help a business or hurt a competitor. There will be a lot of legal consequences will happen. There are several algorithms used in this abstract are text generation, sentiment analysis, context duplication, review posting timing, and user profiting algorithms. It also summarizes and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods.