Enhancing Fake Review Detection Using Linguistic Exaggeration, BERT Embeddings, and Fuzzy Logic

The rapid proliferation of online reviews has made them a crucial factor in consumer decision-making. However, the presence of fake reviews threatens the credibility of review platforms, which requires advanced detection mechanisms. The core objective of this work is to develop a hybrid model that combines interpretable handcrafted linguistic cues with deep semantic features for more accurate, robust, and accurate fake review detection. In this study, we propose a novel hybrid approach to fake review detection that integrates fuzzy logic, BERT embeddings, attention-based feature fusion, and an enhanced multicentroid K-means classifier. Our method first preprocesses reviews and extracts key linguistic features based on extravagant words using a fuzzy logic-based membership function that highlights extreme linguistic patterns. Simultaneously, BERT embeddings capture the deep semantic meaning of the text. These features are then combined using a softmax-based attention mechanism to optimize the contribution of each component. To classify the reviews, we introduce a personalized multicentroid K-means algorithm, which improves traditional K-means by allowing a more precise clustering of false and genuine reviews. We evaluated our approach against traditional classifiers such as Support Vector Machines (SVM), Logistic Regression. Our final model demonstrated statistically significant improvements across key metrics, supported by cross-validation and hypothesis testing (p ¡ 0.01). The confidence intervals were calculated to assess the reliability of the observed performance gains. To validate the effectiveness of our proposed method, we conducted extensive evaluations on two real-world datasets. The results confirm that our approach effectively balances semantic understanding, linguistic heuristics, and clustering efficiency, leading to enhanced detection performance. This research provides a scalable and interpretable solution to the problem of detecting fake reviews, with potential applications in e-commerce platforms, online marketplaces, and digital content verification systems.