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Preprocessing involved Minimal Text Cleaning (MTC) and Advanced Text Normalization (ATN), followed by feature extraction from TF-IDF, Part-of-Speech distributions, Named Entity Recognition (NER), readability indices, lexical richness, [Formula: see text]-gram frequencies, sentiment polarity, punctuation usage, and syntactic complexity. Random Forest (RF) consistently achieved top performance (accuracy up to 0.98, AUC\/ROC up to 0.99), outperforming the Na\u00efve Bayes baseline. To enhance transparency, SHAP-based explainability was applied, revealing that readability metrics, lexical richness, unigrams, and linguistic structures (POS and NER) were the strongest drivers of classification across both datasets. For comparison, GPT-4o and GPT-3.5-Turbo, tested in zero-shot mode, achieved a maximum accuracy of 0.68. 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