Sentiment Analysis of Online Product Reviews Using Modified Deep Learning Model

These days, digital evaluations are absolutely necessary for enhancing consumer communications all around the world and for influencing the shopping behaviours of consumers.E-commerce behemoths like as Amazon, Flipkart, Snap deal, Jio, and others let users to share their experiences and provide prospective customers true information into the functioning of a product.Utilizing and analysing the sentiment of customers' product evaluations on e-commerce platforms may be beneficial to both businesses and the academics who work in those platforms.E-commerce product quality management places a significant emphasis on the practise of sentiment analysis of e-commerce reviews.The polarity of emotion expressed in customer reviews is a burgeoning field within the field of sentiment analysis.Businesses make use of consumer feedback, both positive and negative, in order to gain a better understanding of how customers feel about different products and to determine which are the most popular.This study conducts an analysis of previously published research and sets out to construct a new model known as Modified Deep Learning Neural Network Models (MDLNNM), with the goal of automatically detecting and classifying the polarity of sentiment in Product Review Dataset.This system can classify user reviews based on the features of product reviews by using the data from those reviews.According to the findings of the survey, companies have the ability to keep an eye on their customers in real time and request guidance on how to better comprehend them.They will then be able to improve their product options and provide customers with dependable after-sales support.