Abstract: Many individuals look for product reviews before making purchase decisions. They often encounter various reviews online, but it can be difficult for users to determine whether these reviews are authentic or deceptive. Certain review platforms may post favorable reviews created by the manufacturers themselves to manipulate perceptions and generate misleadingly positive feedback for their products. Consequently, users may struggle to discern the authenticity of a review. To address the issue of identifying fake reviews online, a Deep Learning Based Model for Fake Review Detection has been developed. This system aims to detect fraudulent reviews by tracking the IP addresses of users along with their purchasing behavior. Users can log into the system with their user ID and password, browse different products, and submit reviews. To assess whether a review is authentic or fake, the system checks the user's IP address. If the system detects multiple fake reviews originating from the same IP address, it will notify the admin to delete those reviews from the system. The system adopts data mining techniques. This solution assists users in finding accurate reviews about products.
To tackle this challenge, we suggest a model based on deep learning for identifying fraudulent reviews in e-commerce websites and service-based sectors. This model utilizes natural language processing (NLP) methods to examine text data and uncover patterns that suggest the presence of fake reviews. By employing sophisticated deep learning frameworks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the system is capable of discerning subtle linguistic indicators, sentiment irregularities, and behavioural trends that set genuine reviews apart from fake ones. Furthermore, we make use of pre-trained word embeddings like Word2Vec or Glove to capture the semantic connections between words, thus improving the model's capability to comprehend context and intent. The model is developed using a substantial dataset of labelled reviews, including both positive and negative feedback, to ensure its robustness. Through thorough evaluation, the deep learning model achieves a high level of accuracy in categorizing fake reviews, providing an effective means to bolster trust and reliability in online review systems. This strategy could be integrated into current platforms, enabling real-time detection of fake reviews and protecting both users and businesses from deceptive practices.
Keywords: Fake review detection Deep learning Convolutional Neural Networks (CNNs)
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