  Online Reviews play a vital role in e commerce for decision making. Much of
the population makes the decision of which places, restaurant to visit, what to
buy and from where to buy based on the reviews posted on the respective
platforms. A fraudulent review or opinion spam is categorized as an untruthful
or deceptive review. Positive reviews of a product or a restaurant helps
attract customers and thereby lead to an increase in sales whereas negative
reviews may hamper the progress of a restaurant or sales of a product and
thereby lead to defamed reputation and loss. Fraudulent reviews are
deliberately posted on various online review platforms to trick customers to
buy, visit or distract against a product or a restaurant. They are also written
to commend or discredit the product's repute. The work aims at detecting and
classifying the reviews as deceptive or truthful. It involves use of various
deep learning techniques for classifying the reviews and an overview of
proposed approach involving Attention based Bidirectional LSTM to tackle issues
related to semantic information in reviews and a comparative study over
baseline machine learning techniques for review classification.
