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Fraudulence Detection and Recommendation of Trusted Websites

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TLDR
This paper concentrates on analyzing the reviews and extracting the useful information to guide the customers using text mining and people will get a better idea of making a proper decision in buying the product through online.
Abstract
We live in the world of science where everything could be done possibly through online facilities. As the online services increase, the fraudulent sites also increase, however. Therefore, it enhances the difficulty of people to identify a fake or fraudulent or scam website. Fraudsters are highly intelligent in making or persuading fake products which seem exactly same as the original. Some scam websites use low prices to attract people. Most of the fraudulent sites use domain names that reference a popular brand or product name and design with mere change. Some websites will get your card details before you intend to buy a product. Fraudsters try to attract people’s attention by advertising the fake products in many social websites such as Facebook, Instagram and Twitter, and when people wishes to view about the product and its details, they are forced to install an app of that site. So, here we have a solution to find those fraudulent products and recommend some trusted products for people to buy with good quality and at a fair price. We use the reviews or feedback or comments and the rating from the users who already purchased the product and shared their experience for identifying fraudulent products and giving a caution to the remaining people. This paper concentrates on analyzing the reviews and extracting the useful information to guide the customers using text mining. As a result of this, people will get a better idea of making a proper decision in buying the product through online.

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References
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Proceedings ArticleDOI

Automatic Identification of Replicated Criminal Websites Using Combined Clustering

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TL;DR: It significantly shows that analyzing social media data by using Naïve Bayes model presented sharing positive and negative views accurately as well as reflects satisfied results.
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Proceedings ArticleDOI

Recommendation system based contextual analysis of Facebook comment

TL;DR: The novelty of the proposed approach is that posts are not simply characterized by an opinion score, as is the case with machine learning-based classifiers, but instead receive an opinion grade for each distinct notion in the post.
Trending Questions (2)
Are there web extensions that detects fraudulent, fake, counterfeit, or scam product?

Yes, the paper proposes a solution to detect fraudulent products by analyzing reviews and feedback from users.

Are there existing website extensions for detecting scam or fraudulent products in online shopping?

Yes, the paper proposes a solution to detect fraudulent products by analyzing reviews and feedback from users who have already purchased the product.