quickSentiment: A Fast and Flexible Pipeline for Text Classification

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.

Version:	0.1.0
Imports:	quanteda, stopwords, foreach, stringr, textstem, glmnet, ranger, xgboost, caret, Matrix, magrittr, doParallel
Suggests:	knitr, rmarkdown, spelling
Published:	2026-02-06
DOI:	10.32614/CRAN.package.quickSentiment
Author:	Alabhya Dahal [aut, cre]
Maintainer:	Alabhya Dahal <alabhya.dahal at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
Language:	en-US
Materials:	README
CRAN checks:	quickSentiment results

Documentation:

Reference manual:	quickSentiment.html , quickSentiment.pdf
Vignettes:	Introduction to quickSentiment (source, R code)

Downloads:

Package source:	quickSentiment_0.1.0.tar.gz
Windows binaries:	r-devel: quickSentiment_0.1.0.zip, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): quickSentiment_0.1.0.tgz, r-oldrel (arm64): quickSentiment_0.1.0.tgz, r-release (x86_64): quickSentiment_0.1.0.tgz, r-oldrel (x86_64): quickSentiment_0.1.0.tgz

Linking:

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