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: 2026-06-16 09:20:15 UTC; root
Author: Alabhya Dahal [aut, cre]
Maintainer: Alabhya Dahal <alabhya.dahal@gmail.com>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-04-17 06:17:01 UTC
RemoteUrl: https://github.com/cran/quickSentiment
RemoteRef: HEAD
RemoteSha: 1c621de51fa914282a57addc9a6deeb6b614130a