  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.
