TOPS: A Framework for Trusted Opinion Analysis of Product Reviews Using Hybrid Deep Learning Based D2CL Filter

Abstract

The rapid growth of online product reviews has made it increasingly challenging for consumers to make informed purchase decisions. However, the abundance of reviews, including fake or augmented and sarcastic reviews, poses a challenge for consumers. To address this challenge, this paper introduces the TOPS (Trusted Opinion analysis of Product reviewS) framework, a novel approach that leverages a hybrid deep learning-based D2CL (Dual Deep leaning based cleaning) filter to enhance the reliability of online reviews. The proposed methodology employs the D2CL filter to identify and eliminate fake and sarcastic reviews, ensuring that the consolidated sentiment analysis provides users with trustworthy opinions. The framework is equipped with the R-mGRU, a hybrid deep learning model specifically designed to tackle the nuances of product reviews. This model has demonstrated impressive accuracy rates, achieving 89%, 91%, and 94% for fake, sarcasm, and sentiment analysis tasks, respectively. The TOPS framework makes a significant contribution to improving the overall quality and authenticity of product reviews, empowering consumers with more reliable information for informed decision-making in online shopping scenarios.