Interpreting Fake Reviews Using Machine Learning and Deep Learning

The issue of fake reviews on online platforms has increasingly become a huge concern in recent years, with the potential to mislead consumers and negatively impact businesses. In this paper, we present a comprehensive approach to detecting fake reviews using both supervised and unsupervised learning techniques. Our approach includes classic machine learning algorithms, deep learning techniques such as RNN and attention networks, as well as state-of-the-art models like BERT and GPT. We leverage a labeled dataset of restaurant reviews from Yelp.com to train and evaluate our models. We also compare the performance of supervised and unsupervised learning techniques and identify the most effective and explainable models for detecting fake reviews. Our results show that our approach achieves high accuracy in detecting fake reviews, and the interpretation of our models offers valuable insights into the factors that contribute to the identification of fake reviews. We believe our work contributes to the ongoing effort of combating fake reviews and provides a practical and effective solution for businesses and consumers to identify trustworthy reviews.