A Sentiment Analysis-based Rating System for Mobile Devices Using Customer Reviews

Abstract

"As e-commerce expands rapidly, consumers face the challenge of navigating through a vast array of smartphone options, with the key concern being the authenticity and quality of the devices. Our project aims to tackle this issue by optimizing the process of analyzing and evaluating customer reviews using Natural Language Processing (NLP) techniques. By summarizing the experiences of other users, the application helps consumers make more informed decisions.

Leveraging pre-rated reviews as training data, the model extracts sentiments and identifies key features from the feedback to create a powerful assessment tool. Integrated into a web application, this model efficiently evaluates and rates new reviews, providing users with concise summaries of smartphone experiences. Early results indicate strong performance in both regression and classification tasks, validating the model's ability to extract meaningful insights from large volumes of customer feedback.

Users have praised the application's ability to effectively condense information, significantly improving the decision-making process. Ongoing enhancements to the model aim to extend its applicability beyond smartphones and increase its efficiency. By delivering richer, more actionable insights, the project is expected to further facilitate seamless online shopping and more informed purchasing decisions."