E-Commerce Authenticity: A Deep Learning and Aspect-based Fusion Approach for Fake Review Detection

Trust in online shopping suffers when false feedback appears. Although influencing customer choices, such content introduces misleading paths through altered opinions. Detection systems respond by identifying mixed forms of deceptive input via thorough examination methods. A structure combining various streams of written traits operates with advanced onedimensional convolution layers followed by two-way long short-term memory units tailored specifically. Focus increases on key elements thanks to an intelligent marking method. Examine the units of text closely. Preparation occurs first, unfolding in stages without immediate visibility. A mix of Doc2Vec, GloVe, and TF-IDF weighted n-grams forms the base data. Once compressed via SVD, meaning shifts are tracked by grammatical dependencies rather than deep neural stacks. Built upon SBERT representations, emotional context enters by mapping subtle bonds among moods and judgments. Despite differing routes, sequence inputs merge with auxiliary signals, refining awareness of setting during processing. Notably influenced by emotion, interpretation of complex cues grows fivefold in impact. Layered checks operate within the framework, complementing a collective structure shaped by diverse predictors. Once evaluation ends, evidence emerges: the XGBoost fusion stage lifts precision and resilience. In trials, results stand out clearly - truthful, deceitful, favorable, and unfavorable cases are consistently separated. A single investigation examined responses. Following that work, scalability methods entered the design of deep learning systems aimed at improving validation processes within online trade settings.