quickSentiment: A Fast and Flexible Pipeline for Text Classification
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.
| Version: |
0.3.1 |
| Imports: |
doParallel, foreach, glmnet, magrittr, Matrix, methods, naivebayes, pROC, quanteda, ranger, stopwords, stringr, textstem, xgboost |
| Suggests: |
knitr, rmarkdown, spelling |
| Published: |
2026-03-02 |
| 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 |
| Citation: |
quickSentiment citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
quickSentiment results |
Documentation:
Downloads:
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