Product Rating using Opinion Mining
Main Article Content
Abstract
Amazon.com is one of the largest electronic commerce websites in the world, allowing users to purchase various products and submit reviews for each one of them. These reviews serve as valuable insights for first-time buyers, helping them understand the product's quality and make informed purchase decisions. However, the abundance of reviews generates unstructured big data, which can be harnessed for product recommendations on the website. The challenge lies in identifying and filtering out fake reviews that may be used to either promote or defame a particular brand. The accurate rating of a product depends on the authenticity of the reviews. Moreover, efficient analysis of big data is essential due to the slow processing speed of conventional systems. To address these challenges, we propose a novel approach that leverages the Apache Spark framework for accelerating the processing of Amazon reviews. Our system entails detecting fake reviews, efficiently processing genuine reviews using Apache Spark, and ultimately providing accurate product ratings. By implementing this approach, we aim to enhance the credibility of Amazon's review system and offer faster, more effective big data analysis for improved product recommendations.
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