A Multi-Embedding Convergence Network on Siamese Architecture for Fake Reviews

TL;DR

This paper introduces a Siamese network-based approach utilizing multi-embedding techniques to detect fake reviews with high accuracy, combining contextual and semantic analysis for improved verification.

Contribution

It presents a novel multi-embedding Siamese network architecture integrating MiniLM BERT and Word2Vec for effective fake review detection.

Findings

High accuracy in fake review detection

Effective combination of contextual and semantic embeddings

Robust performance on a 40K review dataset

Abstract

In this new digital era, accessibility to real-world events is moving towards web-based modules. This is mostly visible on e-commerce websites where there is limited availability of physical verification. With this unforeseen development, we depend on the verification in the virtual world to influence our decisions. One of the decision making process is deeply based on review reading. Reviews play an important part in this transactional process. And seeking a real review can be very tenuous work for the user. On the other hand, fake review heavily impacts these transaction records of a product. The article presents an implementation of a Siamese network for detecting fake reviews. The fake reviews dataset, consisting of 40K reviews, preprocessed with different techniques. The cleaned data is passed through embeddings generated by MiniLM BERT for contextual relationship and Word2Vec for semantic relationship to form vectors. Further, the embeddings are trained in a Siamese network with LSTM layers connected to fuzzy logic for decision-making. The results show that fake reviews can be detected with high accuracy on a siamese network for prediction and verification.