• B.L.N.R.VARA PRASAD
• P.RAMESH BABU

In the dynamic landscape of e-commerce, user-generated reviews play a pivotal role in influencing consumer purchasing decisions. Identifying early reviewers, who post reviews shortly after a product's launch, holds strategic significance for marketers seeking to shape product perception and adoption. This paper presents a comprehensive study that characterizes and predicts early reviewers on e-commerce websites. Leveraging machine learning techniques and data mining, we analyze factors such as review timing, content sentiment, reviewer demographics, and product attributes to understand the distinctive characteristics of early reviewers. Furthermore, we propose a predictive model that anticipates potential early reviewers, enabling marketers to tailor their strategies effectively. Through an empirical evaluation using real-world e-commerce data, we demonstrate the model's accuracy in early reviewer prediction. This study contributes to a deeper understanding of the role of early reviewers in product marketing and offers practical insights for marketers aiming to harness the power of early adopters

E-commerce, early reviewers, user-generated reviews, product marketing, machine learning, predictive modeling

"Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-commerce Websites", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.e277-e282, September-2023, Available :http://www.jetir.org/papers/JETIR2309432.pdf

"Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-commerce Websites", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppe277-e282, September-2023, Available at : http://www.jetir.org/papers/JETIR2309432.pdf