AdaptiveNet: A Novel Architecture for Reducing Computation Complexity to Fake Review Classification

The exponential rise of e-commerce platforms has resulted in a dramatic increase in online reviews, which creates a challenge in distinguishing fake reviews that erode consumer confidence and harm commerce ecosystems. Traditional approaches for fake review detection employ computationally expensive deep learning networks which are resource-intensive and difficult to use in practice. In this paper, we describe AdaptiveNet, a new lightweight neural architecture that achieves fake review detection with much lower computational resources while maintaining a higher detection and classification precision. The model proposed in this paper is based on three original innovations: a Multi-Scale Semantic Fusion (MSSF) layer for hierarchical feature extraction, Dynamic Attention Scaling (DAS) with complexity measure attention, and Adaptive Parameter Sharing (APS) context-gated networks. With thorough evaluation on Amazon, Yelp, and TripAdvisor datasets of reviews totalling 1.2 million reviews, AdaptiveNet attains 94.8% accuracy while achieving 65% computational overhead in comparison to traditional models. The architecture outperformed all other state-of-the-art models, BERT-base (92.1%), RoBERTa (91.8%), and other more recent efficient models, requiring 70% lower parameters and 60% lower energy consumption. This work markedly advances the other efficient deep learning architectures for text classification and allows for the practical implementation of fake review detection systems in resource-limited settings as process innovation.