  A robust and reliable system of detecting spam reviews is a crying need in
todays world in order to purchase products without being cheated from online
sites. In many online sites, there are options for posting reviews, and thus
creating scopes for fake paid reviews or untruthful reviews. These concocted
reviews can mislead the general public and put them in a perplexity whether to
believe the review or not. Prominent machine learning techniques have been
introduced to solve the problem of spam review detection. The majority of
current research has concentrated on supervised learning methods, which require
labeled data - an inadequacy when it comes to online review. Our focus in this
article is to detect any deceptive text reviews. In order to achieve that we
have worked with both labeled and unlabeled data and proposed deep learning
methods for spam review detection which includes Multi-Layer Perceptron (MLP),
Convolutional Neural Network (CNN) and a variant of Recurrent Neural Network
(RNN) that is Long Short-Term Memory (LSTM). We have also applied some
traditional machine learning classifiers such as Nave Bayes (NB), K Nearest
Neighbor (KNN) and Support Vector Machine (SVM) to detect spam reviews and
finally, we have shown the performance comparison for both traditional and deep
learning classifiers.
