Abstract
In e-commerce and on social media, identifying fake opinions has become a tremendous challenge. Such opinions are widely generated on the internet by fake viewers, also called fraudsters. They write deceptive reviews that purport to reflect actual user experience either to promote some products or to defame others. They also target the reputations of e-businesses. Their aim is to mislead customers to make a wrong purchase decision by selecting undesired products. Such reviewers are often paid by rival e-business companies to compose positive reviews of their products and/or negative reviews of other companies' products. The main objective of this paper is to detect, analyze and calculate the difference between fake and truthful product reviews. To do this, the methodology has planned to have seven phases: reviewing online products, analyzing features through linguistic enquiry and word count (LIWC), preprocessing the data to clean and normalize them, embedding words (Word2Vec) and analyzing performance using artificial deep-learning algorithms for classifying fake and truthful reviews. Two deep-learning neural network models have been evaluated based on standard Yelp product reviews. These models are bidirectional long-short term memory (BiLSTM) and convolutional neural network (CNN). The results from comparing the performance of the two models showed that the BiLSTM model provided higher accuracy for detecting fake reviews than the CNN model.