CROSS PLATFORM REPUTATION GENERATION SYSTEM BASED ON ASPECT-BASED SENTIMENT ANALYSIS

Abstract

The rapid expansion of Internet-driven platforms like social media and online marketplaces has led to an explosion of user-generated content, particularly in the form of product reviews and opinions. Consequently, there is a pressing need for automated systems to process this vast amount of data efficiently. While existing systems have made strides in generating and visualizing reputation scores from reviews, they often overlook the presence of fraudulent or biased reviews that can skew the perception of a product's reputation. Moreover, these systems typically offer a single, overarching reputation score for a product or service, failing to provide a nuanced assessment of different aspects of the entity.To address these shortcomings, we have developed a novel system that integrates multiple factors, including spam detection, review popularity, posting timing, and aspect-based sentiment analysis, to produce accurate and trustworthy reputation values. Unlike conventional approaches, our model calculates reputation scores not only for the overall entity but also for individual aspects of the product or service under review. By leveraging opinions gathered from diverse platforms, our system generates comprehensive numerical reputation values that offer insights into different facets of the entity's reputation.