A Comprehensive Product Review System for Improved Customer Satisfaction

Digital reviews now have a significant impact on how consumers communicate globally and how they make purchases. When a buyer looks at the product’s ratings and reviews, they are frequently confused by the sheer volume of them. In the proposed study, product reviews are classified into positive and negative sentiments using the VADER (Valence Aware Dictionary for Sentiment Reasoning), a machine learning model that classifies reviews into positive and negative sentiments based on attributes discovered by the model that is used in the proposed work to categories product reviews into positive and negative categories. We provide the consumer with a graph of the number of good and negative reviews for the product they are interested in, as well as the total positive and negative review polarity for the item. To save clients’ time, a graphical representation of the analysis is also given.