FUZZYFAKEROBERTA: FAKE REVIEW IDENTIFICATION IN E-COMMERCE PLATFORM

Fake reviews on e-commerce platforms pose a substantial risk to the integrity of online reviews and can significantly mislead consumers, leading to unfavorable buying choices. These fake reviews can distort consumer perceptions, potentially resulting in financial losses and decreased trust in online shopping. This study investigates the integration of fuzzy logic with the fine-tuned RoBERTa model, resulting in the "fuzzyfakeRoBERTa" model, designed to detect fake reviews on e-commerce platforms. The fuzzyfakeRoBERTa model enhances the accuracy and precision of fake review identification by effectively addressing the inherent imprecision and uncertainty often present in data. The research methodology included replicating the original fakeRoBERTa model, incorporating fuzzy logic to handle ambiguous data better, and thoroughly evaluating the model's performance using key metrics such as accuracy, precision, recall, and F1-score. The fuzzyfakeRoBERTa model achieved a notable accuracy rate of 97.59%, indicating a significant improvement over the original model's performance. This enhanced accuracy demonstrates the model's superior robustness and effectiveness in identifying fake reviews. The findings suggest that integrating fuzzy logic into deep learning models can substantially improve their performance in tasks that involve complex and nuanced data. This research enhances the reliability and credibility of e-commerce platforms by offering a more precise and effective tool for detecting fraudulent reviews, thereby helping maintain consumer trust and ensure fair competition in the digital marketplace.