A Hybridized Approach for Enhanced Fake Review Detection

User reviews on online consumption platforms are crucial for both consumers and merchants, serving as a reference for purchase decisions and product improvement. However, fake reviews can mislead consumers and harm merchant profits and reputation. Developing effective methods for detecting deceptive reviews is crucial to protecting the interests of both parties. In recent years, research on fake review detection has focused on improving machine learning and neural network methods to enhance the accuracy of fake review detection, neglecting the fundamental and necessary work of text feature representation for reviews. High-quality review text feature representation affects or even determines the quality and performance of fake review detection methods. The increasing prevalence of fake reviews results in a more complex distribution within the feature space of review texts, thus necessitating review embedding methods that exhibit comprehensive semantic comprehension and contextual awareness of review texts. To improve the quality of textual feature representation, we propose a review-embedding attention-based long short-term memory (A-LSTM) method that can encode the global semantics of reviews and detect the deception of the review content. A-LSTM uses attention gates to discover the importance of words, and by analyzing the importance of words, it can help distinguish the characteristics of real and fake reviews, and we propose an attention loss function to solve the problem of class imbalance. On the Yelp dataset, the accuracy of deceptive review detection has increased to 90.9%.