Advancing E-Commerce Authenticity: A Novel Fusion Approach Based on Deep Learning and Aspect Features for Detecting False Reviews

In the contemporary digital marketplace, the proliferation of online consumer reviews has a pivotal influence on purchasing decisions. Concurrently, the prevalence of spurious reviews poses a substantial risk to the integrity of e-commerce, misleading consumers, and detrimentally impacting businesses. This paper delineates a pioneering methodology for the identification of counterfeit reviews, which is based on the combination of deep learning attributes and aspect-based analytical features. The main contribution of this research is (1) proposing an aspect fusion network based on the hierarchical attention mechanism to address the problem of multiple aspects of representing review content. The aspect fusion network can help select important aspect words and fuse aspect dictionaries with word-level attention weights. (2) We build a cardinality fusion model so that the heuristic can mitigate the negative impact of random weights and intervals on the auxiliary model. The methodology integrates advanced deep learning paradigms with aspect-based sentiment analysis to detect fraudulent reviews. Specifically, the approach encompasses a dual-method strategy: initially utilizing a Convolutional Neural Network (CNN) for the extraction of profound characteristics from review texts, followed by employing aspect-based sentiment analysis tools, including Part-of-Speech (PoS) tagging and GloVe embedding, for the distillation of aspectual features. Subsequently, these split sets of features are synergized and applied in the training of various classifier layers. Eextensive experiments have been conducted on six public review datasets contrasting the previous work on authenticity and aspect analysis. The effectiveness and performance of the proposed authenticity fusion model have been verified by the detailed analyses. The proposed model outperforms the competitors with remarkable improvement on both review authenticity and aspect analysis. This innovative approach was rigorously evaluated using a dataset of Amazon reviews that encompassed both authentic and counterfeit reviews. The empirical results demonstrate that our proposed method attains a remarkable accuracy rate of 97.73%, substantially surpassing existing state-of-the-art methodologies. The study posits that the strategic fusion of deep learning attributes and aspect-based features significantly enhances the efficacy of counterfeit review detection systems, presenting a formidable tool in the arsenal against e-commerce fraud.