A Survey on Feature Selection Based Spam Review Detection using Deep Learning Techniques
| Autor: | Rajamohana Sp Dr, Sophia S Ms |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Deep learning 05 social sciences Feature selection 02 engineering and technology Machine learning computer.software_genre Hardware and Architecture 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer 050203 business & management Software |
| Zdroj: | International Journal of Advanced Information and Communication Technology. :102-108 |
| ISSN: | 2348-9928 2349-6339 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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