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.Furthermore, our proposed system 
includes an advanced visualization tool that presents detailed information about the 
generated reputation scores, enhancing user understanding and decision-making. 
Through extensive experimentation conducted on datasets sourced from various 
platforms such as Twitter, Facebook, and Amazon, we have demonstrated the 
effectiveness and superiority of our approach compared to state-of-the-art reputation 
generation systems. Overall, our system represents a significant advancement in the field of reputation analysis, offering robust and 
insightful evaluations of entities and their associated aspects in the digital landscape.