Article
TrustReview: Hybrid Learning Framework for Fake Online Review Detection
The exponential growth of online review platforms has been paralleled by a surge in fake, incentivised, and spam reviews, fundamentally undermining consumer trust and distorting purchase decisions. This paper presents TrustReview, a hybrid learning framework for automated detection of fake online reviews that synergises a fine-tuned RoBERTa transformer backbone with a multi-modal feature pipeline encompassing linguistic cues, sentiment analysis, reviewer behavioural metadata, and readability metrics. The system is trained on a curated, SMOTE-balanced corpus of 21,540 reviews drawn from Yelp, Amazon, and TripAdvisor, integrated with the ORCA benchmark dataset. TrustReview achieves a classification accuracy of 97.1%, precision of 96.2%, recall of 95.7%, and F1-score of 95.9%, significantly outperforming all evaluated baselines including Naive Bayes (71.4% F1), SVM (77.5%), Random Forest (81.1%), BiLSTM (85.1%), and standalone RoBERTa (89.7%). Deployed as a Flask REST API with sub-second inference latency (0.74 seconds average), TrustReview provides a scalable, real-time solution for review authenticity verification across ecommerce and hospitality platforms
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