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.
Highlights
The Commercial website is a main venue for individuals to articulate themselves
In order to buy the right products for the individuals and to make the business decisions for the Organization online reviews are very important
Feature selection is an essential step in the fake review detection to reduce dimensionality of the feature space and to improve the classification accuracy
Summary
The Commercial website is a main venue for individuals to articulate themselves. A portion of the main complaints regarding opinion sharing platforms are that spammers will, without much of a break, create a conversation about the actual company by writing spam comments These spam scores may expect a key activity in bringing competition up in products or services. Various spam recognition testing techniques, such as machine learning , deep learning, and Lexicon-based approaches, are implemented to determine can comments are spam This survey paper has shared views on different methodologies tailored for the identification of fraudulent reviews by using various machine learning techniques. Spammers utilize a great deal of idealistic and negative terms that probably won't be required in a practical setting They some of the time compose proclamations that depend on product titles and not on their data of using the product. They some of the time compose proclamations that depend on product titles and not on their data of using the product. [2]
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