A high-level wrapper that simplifies text classification into three streamlined steps: preprocessing, model training, and prediction. It unifies the interface for multiple algorithms (including 'glmnet', 'ranger', and 'xgboost') and vectorization methods (Bag-of-Words, Term Frequency-Inverse Document Frequency (TF-IDF)), allowing users to go from raw text to a trained sentiment model in two function calls. The resulting model artifact automatically handles preprocessing for new datasets in the third step, ensuring consistent prediction pipelines.

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