A Scalable Artificial Intelligence–Driven Sentiment Analysis System for Large-Scale Product Review Analytics Using NLP and Deep Learning Models

Abstract

This project outlines a scalable framework based on AI for the analysis of product reviews. It leverages the power of NLP in a web-based framework to perform review analysis at an unprecedented scale. The framework is “constructed using user reviews and comments and product-related opinions. Several preprocessing techniques have been employed to improve the framework’s analysis consistency and accuracy. These include text cleaning and normalization. The framework seeks to partition reviews based on the sentiment class, which may be positive, negative, or neutral. This is achieved using sentiment classification techniques based on polarity analysis. The framework also leverages opinion mining to find product-related sentiments, including but not limited to price, quality, and delivery. The application is built on a Django framework and allows users to create an account, upload a dataset, predict sentiment in real time, and analyze the results in a visual the dashboard. The framework also offers an optimal solution to large data analysis and the generation of graphical summaries. The performance of the system is based on the consistency and reliability of the opinion classification system. Overall, the framework provides an optimal solution to large-scale, fully automated sentiment analysis and opinion classification for product reviews. This allows companies to improve the product based on user reviews and opinions, and to improve the customer based on their reviews.