Abstract
A customer’s decision to purchase a product or service are primarily influenced by online reviews. Customers use online reviews, which are valuable sources of information to understand the public opinion on products and/or services. Dependability on online reviews can give rise to the potential concern that violator could give deceitful reviews in order to synthetically promote or decry products and services. This practice is known as Opinion Spam, where spammers manipulate reviews by making fake, untruthful, or deceptive reviews to get profit and boost their products, and devalue a competitor’s products. In order to tackle this issue, we propose to build a fraud risk management system and removal model. This captures fraudulent transactions based on user behaviors and network, analyses them in real-time using Data Mining, and accurately predicts the suspicious users and transactions. In this system, we use two algorithms NLP and TF-IDF to differentiate between fake and genuine reviews or feedback received by the customers
Highlights
The internet is continuing to grow in size and importance, and the quantity and impact of online reviews is increasing continuously as well
He and his team explore the issue on fake review reduce online opinion spam
To spot the fake reviews as soon as possible, we develop supervised solutions and a threshold-based solution
Summary
The internet is continuing to grow in size and importance, and the quantity and impact of online reviews is increasing continuously as well. That is not to say that online reviews are not helpful, online reviews can be helpful by helping customers understand if he should or should not buy a product, but blind trust on these reviews is treacherous for both the seller and buyer. Business owners might allure people to writes good reviews about them and hire someone to write awful reviews about their competitor's products or services. These fake reviews are considered review spam and can have a great impact in the online marketplace
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