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 (may not be active yet) |
| 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:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=quickSentiment
to link to this page.