A Fast and Flexible Pipeline for Text Classification R package quickSentiment version 0.3.5 A high-level pipeline that simplifies text classification into three streamlined steps: preprocessing, model training, and standardized prediction. It unifies the interface for multiple algorithms (including 'glmnet', 'ranger', 'xgboost', and 'naivebayes') and memory-efficient sparse matrix vectorization methods (Bag-of-Words, Term Frequency, TF-IDF, and Binary). Users can go from raw text to a fully evaluated sentiment model, complete with ROC-optimized thresholds, in just a few function calls. The resulting model artifact automatically aligns the vocabulary of new datasets during the prediction phase, safely appending predicted classes and probability matrices directly to the user's original dataframe to preserve metadata. Software Imports: doParallel, foreach, glmnet, magrittr, Matrix, methods, naivebayes, quanteda, ranger, stopwords, stringr, textstem, xgboost Suggests: knitr, rmarkdown, spelling Alabhya Dahal <alabhya.dahal@gmail.com> Comprehensive R Archive Network (CRAN) Alabhya Dahal [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=quickSentiment/LICENSE) 2026-07-13 application/tgz https://CRAN.R-project.org/package=quickSentiment doi:10.32614/CRAN.package.quickSentiment en-US