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Online reviews became a very important supply of knowledge for users before creating Associate in Nursing knowing purchase call. Early reviews of a product tend to own a high impact on the following product sales. during this paper, we have a tendency to take the initiative to check the behavior characteristics of early reviewers through their denote reviews on 2 real-world giant e-commerce platforms, i.e., Amazon and Yelp. In specific, we have a tendency to divide product life into 3 consecutive stages, particularly early, majority and laggards. A user World Health Organization has de note a review within the early stage is taken into account as Associate in Nursing early reviewer. we have a tendency to quantitatively characterize early reviewers supported their rating behaviors, the helpfulness scores received from others and also the correlation of their reviews with product quality. we've got found that (1) Associate in Nursing early reviewer tends to assign the next average rating score; Associate in Nursing (2) an early reviewer tends to post a lot of useful reviews. Our analysis of product reviews conjointly indicates that early reviewers’ ratings and their received helpfulness scores area unit possible to influence product quality. By viewing review posting method as a multiplayer competition game, we have a tendency to propose a completely unique margin-based embedding model for early reviewer prediction. in depth experiments on 2 completely different e-commerce datasets have shown that our planned approach outperforms variety of competitive baseline.

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Mrs. S Maheshwari, Nandhini S, Nithya G, & Yugesh J. (2021). Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites. International Journal of Intellectual Advancements and Research in Engineering Computations, 7(2), 2406–2419. Retrieved from