International Journal of Advanced Networking and Applications

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Classification of Positive and Negative Fake Online Reviews using Machine Learning Techniques


Affiliations
1 Department of Computer Science and Engineering, Delhi Technological University, New Delhi-110042, India
2 Department of CSE & IT, Jaypee Institute of Information Technology, Noida-201309, India
 

E-Commerce is one of the most flourishing businesses in today’s world. A large part of the population, especially in urban areas, is switching towards e-commerce websites to fulfill all of their shopping requirements, whether groceries, electrical appliances, clothing, etc. In an online purchase, product review is considered a significant factor in deciding the right choice of product. Therefore, e-commerce businesses are primarily dependent on product reviews. Due to the lack of authenticity of the reviewer information, while posting a review of any product or service online, the presence of fake reviews is increasing day by day. The presence of these fake reviews of various products or services impacts the customers and the sellers. The customers might choose the wrong brand of a product or service, while the sellers might face low sales of their high-quality products because of these fake reviews. This paper used different machine learning approaches to detect fake reviews of services on e-commerce sites. We have further categorized the fake reviews into positive and negative based on the reviewer’s rating.

Keywords

Logistic regression, Support Vector Machine, Decision Tree, Random Forest, Neural Networks
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  • Classification of Positive and Negative Fake Online Reviews using Machine Learning Techniques

Abstract Views: 268  |  PDF Views: 1

Authors

Anurag Goel
Department of Computer Science and Engineering, Delhi Technological University, New Delhi-110042, India
Tanmay Nigam
Department of CSE & IT, Jaypee Institute of Information Technology, Noida-201309, India
Tarundeep Singh
Department of CSE & IT, Jaypee Institute of Information Technology, Noida-201309, India
Harsh Agrawal
Department of CSE & IT, Jaypee Institute of Information Technology, Noida-201309, India

Abstract


E-Commerce is one of the most flourishing businesses in today’s world. A large part of the population, especially in urban areas, is switching towards e-commerce websites to fulfill all of their shopping requirements, whether groceries, electrical appliances, clothing, etc. In an online purchase, product review is considered a significant factor in deciding the right choice of product. Therefore, e-commerce businesses are primarily dependent on product reviews. Due to the lack of authenticity of the reviewer information, while posting a review of any product or service online, the presence of fake reviews is increasing day by day. The presence of these fake reviews of various products or services impacts the customers and the sellers. The customers might choose the wrong brand of a product or service, while the sellers might face low sales of their high-quality products because of these fake reviews. This paper used different machine learning approaches to detect fake reviews of services on e-commerce sites. We have further categorized the fake reviews into positive and negative based on the reviewer’s rating.

Keywords


Logistic regression, Support Vector Machine, Decision Tree, Random Forest, Neural Networks

References