The rapid expansion of electronic commerce has made online reviews an important source of information for consumers, vendors, and digital platforms. However, the increasing prevalence of deceptive reviews threatens the reliability of these information environments by introducing artificially generated, manipulated, or misleading opinions. Conventional fake-review detection approaches frequently depend on lexical patterns, manually engineered features, or behavioral indicators that may fail when deceptive writers modify their language or imitate authentic reviewer behavior. This research proposes a Semantic Representation Learning Approach for Identifying Deceptive Reviews, emphasizing the extraction of contextual and semantic characteristics from review text. The proposed approach integrates text preprocessing, semantic representation, contextual feature learning, deception-oriented classification, and model evaluation into a unified analytical framework. The methodological foundation is informed by prior research on sentiment analysis, linguistic patterns, online-review behavior, and fake-review detection. Particular attention is given to the transition from surface-level word matching toward representations capable of capturing relationships among words, contextual meaning, sentiment expression, and linguistic consistency. The proposed framework also considers the broader computational perspective of adaptive representation learning, where graph-based reasoning and self-adaptive mechanisms demonstrate the value of learning relationships rather than relying exclusively on isolated features (Ramamurthy et al., 2026). The resulting framework provides a theoretically grounded and technically extensible approach for detecting deceptive reviews while recognizing limitations related to dataset quality, evolving deception strategies, domain transfer, and interpretability.