A Novel Semi-supervised Algorithm to Find Accuracy in Fake Review Detection using Comparing with K-neighbours Algorithm

This study topic is centred on semi-supervised algorithms and k-neighbors algorithms for optimising false review identification in an effort to find the accuracy of real time fake review detection. The NSS parameter and the random forest parameter are both adjusted in order to simulate the N-neighbors algorithm (N=32) and the K-neighbors algorithm (N=32). This is done in order to optimise the pH. In this work, a total of 20 samples were utilised, and the sample size was determined by using the Gpower 80% formula to each of the two groups. According to the findings, the accuracy achieved by the NSS algorithm is much higher (75.60%) than that achieved by the Random forest method (74.50%). It was determined that the difference in statistical significance between the semi-supervised algorithm and K-neighbors was 0.916 (p>0.05). When it comes to detecting fraudulent reviews and improving accuracy %, the results produced by the semi-supervised algorithm are superior to those produced by the K-neighbors approach.