Consumer Sentiment is Extracted from the e-Commerce Website Evaluates Dataset using an Assembly Model

Abstract

Globe is getting more digitalized in the modern-day. E-commerce is gaining traction in today's digitalized world since it enables customers to purchase products without leaving their homes. As more customers rely on online purchases, the value of a review grows. For a product to be selected, a consumer has to examine thousands to grasp the product. However, in this age of burgeoning machine learning (ML), sorting by hundreds of assessments will be much easier if the model were employed to polarize and learn from those evaluations. In this study, the Python language is used to apply the classification algorithm for the obtained data. The accomplishment of a voting classifier consisting of other three classifiers which are SVC, XGB, and XtraTree demonstrates greater precision than other previous research done. The dataset used here is Amazon product Review and work is implemented on Python 3.1.