- In the dynamic realm of enterprise marketing, the digital landscape plays a pivotal role, with online data serving as a cornerstone. However, a pressing predicament emerges in the form of counterfeit product reviews, casting shadows on the credibility of decision-making and data analysis. These deceptive reviews, often propagated by unverified sources, wield detrimental influence, wreaking havoc on both customers and vendors. The repercussions are twofold: customers find themselves unable to make informed purchases, while vendors suffer from diminished sales due to the adverse impact of false narratives. Enter the imperativeness of our contemporary age — the dire need to identify and combat fake reviews. We present a pioneering solution that empowers users with the ability to discern the trustworthiness of reviews, thereby revolutionizing the realm of decision-making. Our innovative approach, rooted in the application of supervised machine learning, stands as a bulwark against the proliferation of falsehoods. Contrasting the limitations of conventional fake review detection methods, which often rely on categorical datasets or sentiment polarity ratings, our methodology bridges the gap. By seamlessly integrating polarity ratings and advanced classifiers, we unveil a comprehensive framework for pinpointing false reviews. This holistic strategy is the outcome of meticulous research, bolstered by an exhaustive survey of precedent literature. The outcome speaks volumes about our commitment to authenticity — with a remarkable accuracy rate of 88% achieved through the deployment of the cutting-edge Support Vector Machine (SVM) machine learning technique. In a world besieged by deceptive narratives, our system emerges as a beacon of accuracy and trustworthiness, redefining the landscape of enterprise marketing with each identified fake review.

Enterprise Marketing, KNN, Fake Reviews, Decision Making, Data Analysis, Unverified, Supervised Machine Learning.