S. Sophia, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
SP. Rajamohana, PSG College of Technology, Coimbatore, Tamil Nadu, India.
International Journal of Advanced Information and Communication Technology
Received On : March 10, 2020
Revised On :March 10, 2020
Accepted On : March 10, 2020
Published On : July 05, 2020
Volume 07, Issue 07
Pages : 102-108
Abstract
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.
Keywords
Fake review detection; Machine learning; Feature selection and classification; Deep learning.
Cite this article
S. Sophia and SP. Rajamohana, “ A Survey on Feature Selection Based Spam Review Detection using Deep Learning Techniques, ” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp. 102–108, July. 2020.
Copyright
© 2020 S. Sophia and SP. Rajamohana. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.