Authors : Pranav Patil; Kiran Nangude; Aditya Rananavare; Rutik Pisal; Pradeep Shinde

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/5n7s5kyd

Scribd : https://tinyurl.com/5c622w2t

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR2600

Abstract : Thought mining is a widely used topic in today's world; The internet contains a lot of valuable information used by different companies for different purposes. Our goal is to create a web application with machine learning models that can identify user reviewsof specific products. It shows the advantages and disadvantages of checking reviews of products that are useful to users. In this application, when the user searches for a product, comment data is collected from the e-commerce site and transferred to the machine learning model, which is the Naive Bayes tool, to enable positive and negative emotions to be identified separately according to the extracted features. . from themodel. We show users all the positive and negative polarities of the reviews for the products they are looking for, and we also clearly show how we arrived at the results. Therefore, these results can help users make decisions about products..

Keywords : Data Analytics, Analysis, Product Sentiment, Ecommerce.