quickSentiment 0.3.1

Major Changes & API Updates

pipeline() now accepts raw atomic vectors (text_vector and sentiment_vector) instead of full dataframes, drastically improving memory efficiency.
Renamed the prediction() function to predict_sentiment() to prevent namespace collisions with base R generic functions.

Removed the caret dependency entirely. Cross-validation folds and confusion matrix evaluations are now handled via lightweight custom implementations.
The vectorization pipeline now strictly utilizes dgCMatrix sparse matrices, ensuring the package scales efficiently for large text datasets.

Models now return explicit class probabilities alongside the final predicted categories.
Added automated ROC/AUC calculations for binary classification. The pipeline now returns an optimized threshold, which predict_sentiment() utilizes by default.
Users can now manually define classification thresholds during inference.
Upgraded text preprocessing capabilities with advanced arguments for custom stop words, retained words, and default corrections for quanteda stop word dictionaries.

Resolved an initialization bug that incorrectly prevented the use of the Naive Bayes (nb) model.
Fixed a compatibility error that occurred when pairing TF-IDF vectorization with Random Forest (rf) models.