Applying Deep Learning Methods on Spam Review Detection

In today's environment, a reliable and effective technique for identifying spam reviews is essential if you want to purchase things online without being taken advantage of. There are possibilities for publishing reviews in many internet locations, which opens the door for sponsored or deceptive fake reviews. These fabricated evaluations may mislead the general audience and leave them unsure of whether or not to believe them. The issue of spam review finding has been solved by the introduction of prominent deep literacy methods. The focus of recent research has been on supervised literacy practices that contain labelled data, which is inadequate for online review. This initiative aims to expose any dishonest textbook reviews. To do this, we've used both labelled and unlabeled data and suggested deep learning techniques for spam review detection, including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and a Long Short-Term Memory (LSTM) variation of Recurrent Neural Networks (RNN). We also used standard machine learning classifiers to identify spam reviews, including Naive Bayes (NB), K Nearest Neighbor (KNN), and Support Vector Machine (SVM). Finally, we compared the effectiveness of traditional and deep literacy classifiers. We'll use deep literacy classifiers to boost the finesse and efficiency.