Package: quickSentiment
Title: A Fast and Flexible Pipeline for Text Classification
Version: 0.2.0
Authors@R: 
    person("Alabhya", "Dahal", email = "alabhya.dahal@gmail.com", role = c("aut", "cre"))
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
Imports: quanteda, stopwords, foreach, stringr, textstem, glmnet,
        ranger, xgboost, naivebayes, caret, Matrix, magrittr,
        doParallel
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, spelling
Language: en-US
NeedsCompilation: no
Packaged: 2026-02-15 03:53:52 UTC; meala
Author: Alabhya Dahal [aut, cre]
Maintainer: Alabhya Dahal <alabhya.dahal@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-15 05:10:02 UTC
Built: R 4.6.0; ; 2026-02-26 05:32:05 UTC; windows
