In recent times, online shoppers are technically
knowledgeable and open to product reviews. They usually read the
buyer reviews and ratings before purchasing any product from ecommerce
website. For the better understanding of products or
services, reviews provided by the customers gives the vital source
of information. In order to buy the right products for the
individuals and to make the business decisions for the
Organization online reviews are very important. These reviews or
opinions in turn, allow us to find out the strength and weakness of
the products. Spam reviews are written in order to falsely promote
or demote a few target products or services. Also, detecting the
spam reviews has also become more critical issue for the customer
to make good decision during the purchase of the product. A
major problem in identifying the fake review detection is high
dimensionality of the feature space. Therefore, feature selection is
an essential step in the fake review detection to reduce
dimensionality of the feature space and to improve the
classification accuracy. Hence it is important to detect the spam
reviews but the major issues in spam review detection are the high
dimensionality of feature space which contains redundant, noisy
and irrelevant features. To resolve this, Deep Learning Techniques
for selecting features is necessary. To classify the features,
classifiers such as Naive Bayes, K Nearest Neighbor are used. An
analysis of the various techniques employed to identify false and
genuine reviews has been surveyed.