Package: quickSentiment
Title: A Fast and Flexible Pipeline for Text Classification
Version: 0.3.4
Authors@R: 
    person("Alabhya", "Dahal", email = "alabhya.dahal@gmail.com", role = c("aut", "cre"))
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: doParallel, foreach, glmnet, magrittr, Matrix, methods,
        naivebayes, quanteda, ranger, stopwords, stringr, textstem,
        xgboost
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, spelling
Language: en-US
NeedsCompilation: no
Packaged: 2026-04-17 01:50:05 UTC; meala
Author: Alabhya Dahal [aut, cre]
Maintainer: Alabhya Dahal <alabhya.dahal@gmail.com>
Repository: CRAN
Date/Publication: 2026-04-17 03:30:02 UTC
