Detecting Fake Reviews in E-Commerce using Deep Learning Techniques

Users may now create and use large amounts of data saved online thanks to e-commerce systems. Modern shoppers examine online reviews before making purchases. Evaluations are essential for both people and organizations. People and organizations need evaluations. Customers use this information to buy. Spammers produce fake reviews to mislead consumers about internet products and services. No system controls reviews, thus anybody may post anonymously. This causes fake reviews. Consumers need ways to differentiate fake reviews from real ones. Long Short-Term Memory (LSTM) network and Convolutional Neural Network (CNN) are used alongside Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) in this study. We use the Deceptive Opinion Spam Corpus and Yelp Labeled datasets to do this. Experimental results showed that the Deceptive Opinion Spam Corpus LSTM classifier beat earlier approaches by 1.14% to 3.49%. The Yelp-annotated dataset showed 44.83% maximum recall.