An Improvised Method on Detecting Fake Reviews Systems Through AI Analytics on Sales Data

Customers of internet enterprises are not the only ones negatively affected by fake online evaluations; the industry as a whole suffers as well. Researchers have spent a lot of time investigating bogus reviews, but no comprehensive survey has been conducted on the topic. In order to pave the way for future studies on false reviews, this one presents a conceptual framework based on causes and effects. The primary goal of this work is to treat the symptom by finding a solution to the problem at its source, which in this case is to create algorithms to detect false reviews. The strategies and reasons that contribute to the existence of false reviews are often overlooked in these kinds of actions. Even though there are algorithms that are very good at finding fake reviews, a lot of them still exist because they are always coming up with new ways to post misleading content that cannot be found.