Fake opinion detection in an e-commerce business based on a long-short memory algorithm

Abstract

Online fake opinions, in the form of misleading reviews, about products or services are harmful and impact consumers’ decisions to purchase. Most consumers today, who are buying their needs over the Internet, are checking online reviews first to get detailed experiences of previous customers about products or services. However, some e-businesses encourage individuals to write fake reviews about products or services, in exchange for money or free products, to compete with other competitors. These people are regarded as fraudster reviewers, and the reviews they write are known as fake reviews. In the current study, we considered the issue of fake opinion identification in e-commerce businesses based on deep learning recurrent neural network long short-term memory (RNN-LSTM). We performed our experiment using a standard Yelp product review dataset. We used a linguistic inquiry and word count dictionary to extract additional linguistic features from the review texts, which can help distinguish between real and fake reviews. Instances of these features include: the authenticity of the review’s text, the analytical thinking of the reviewer, negative words, positive words, and personal pronouns. The proposed RNN-LSTM model reports a better performance for the classification of the reviews into either fake or real categories, achieving results of 98% in terms of accuracy and F1-score.