Proceedings of International Conference on Applied Innovation in IT  ·  2026/06/12  ·  Vol. 14  ·  Issue 3  ·  pp. 371–379
A Hybrid Roberta-Large and Vader-Based Deep Learning Framework for Fake Reviews Detection in Amazon Dataset
Zaid Z. Abdulshaheed and Ahmed J. Obaid
📄 Download PDF DOI: Under indexing
Abstract
Because online shopping has grown so quickly, what people say about products in online reviews now has a huge impact on what people buy. Unfortunately, fake reviews are a big issue, as they can trick shoppers, harm a company's good name, and make the market unfairly skewed. This research presents a way to find these fake reviews in Amazon reviews specifically, and it uses both the words in the review plus how positive or negative the review feels. The system works by merging RoBERTa-Large (which understands the context of words) with VADER (which measures emotional tone), and then using a Transformer encoder, a BiGRU layer, a way of focusing on important information (an attention mechanism), and finally, layers of fully connected classifiers. Amazon reviews were used for this because of their sheer number, the fact they come from the actual world, and how often they’re used by others studying fake review spotting. Testing showed the RoBERTa-Large, VADER and BiGRU combination did very well; The proposed model achieved 95.45% accuracy and 0.9898 ROC-AUC across roughly 4 million Amazon reviews. What this shows is that combining a deep understanding of what words mean in their context with the positivity or negativity expressed in the review improves finding fake reviews, and gives a reliable method for looking at lots of reviews on big online shops.
Keywords
Fake Review Detection Amazon Reviews RoBERTa-Large VADER BiGRU Deep Learning Sentiment Analysis.
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