A Novel Metric for Enhancing Online Review Relevance in E-commerce

In the realm of e-commerce, online reviews are a crucial resource for consumers, yet their usefulness is often hindered by the overwhelming quantity and variability of information. This study proposes an innovative approach to balancing numerical ratings with the sentiment extracted from review texts, leveraging the VADER (Valence Aware Dictionary and sEntiment Reasoner) model. The proposed metric identifies atypical and incongruent reviews by evaluating the consistency between numerical ratings and the sentiment conveyed in textual content. Through the analysis of real-world review datasets, we demonstrate how this system enhances the relevance of information for consumers, enabling them to navigate reviews with greater ease. Tested on datasets comprising 3 million reviews, the results show that integrating this metric into e-commerce platforms can not only optimize the shopping experience but also provide businesses with an opportunity to increase transparency and foster customer loyalty. This work contributes to the ongoing discourse on the importance of AI-driven tools in supporting informed decision-making within digital marketing.