Abstract: As we can see, the trend of online shopping has evolved over the past two decades into a practical choice for everyone of us. Due to the expanding demand and desire for online shopping and online enterprises, Businesspeople must constantly rely on computer science and related technology to understand what the customer wants. Yet, because the number of consumers is expanding quickly and on a larger scale online, it is challenging for interested parties to fully comprehend the reviews they require to evaluate a product. Moreover, some product reviews are fake. Consumers and product providers that seek to meet the demands of the client encounter obstacles as a result of this.
The quantity of client reviews for the goods increases quickly as e-commerce expands and gains popularity every day. A well-liked product may have hundreds of reviews, perhaps thousands. Because of this, it is challenging for a potential customer to read them and decide whether or not to acquire the items. In this research, a method is implemented that makes use of data mining to analyze real product evaluations submitted by real consumers, notifies the creators and other consumers of the positive or negative review, and blacklists bogus accounts.
Moreover, there are a few bogus product evaluations from time to time. Consumers and product manufacturers that make an effort to understand the demands of the customer both encounter difficulties as a result. Finally, after they have reviewed the product themselves, the comment will be automatically checked to see if it is positive or negative. Fake accounts will also be blocked and prevented from accessing further. Based on experimental research and surveys, this review monitoring technique was found to be successful and efficient.
Keywords: online shopping, client needs, product reviews, false reviews, e-commerce, data mining, authentication, summary of reviews, positive/negative comments, fake accounts, review monitoring, successful and efficient.
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DOI:
10.17148/IJARCCE.2023.124187
