Amazon, founded by Jeff Bezos in July 1994, is renowned for its influence in the electronic commerce industry. Amazon initially started as an online marketplace for books but quickly expanded into a multitude of product categories and is currently one of the world’s largest online retailers.

With over 300 million active customers interacting with their online platform, Amazon is trying to ensure that users have the most authentic, reliable, and trustworthy shopping experience. To safeguard their product standards, Amazon has developed a review framework in which customers can get insights and feedback on products while shopping. However, with the multitude of reviews, there is an ever-growing problem of review legitimacy.

Our Amazon Review Confidence Tool combats this legitimacy problem by conducting product review analysis to calculate review authenticity within a visually intuitive browser extension and web application.

Users have access to a browser extension that displays confidence scores for each review of a selected product. The Review Confidence Tool then calculates an adjusted total average rating after filtering reviews with low confidence scores, enabling users to get a more accurate rating without low legitimacy reviews.

The tool is also equipped with a web application that provides a visually intuitive summary of review authenticity. This solution reduces customer confusion and preserves sellers’ reputations within two easy-to-use applications.

The tool’s infrastructure is built entirely on Amazon Web Services, referencing the AWS Well-Architected Framework to create a responsive and scalable environment. The serverless web app uses AWS Amplify, Lambda, and DynamoDB to minimize unnecessary overhead, and Amazon SageMaker is the tool’s all-in-one solution to machine learning.