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quickSentiment  

A Fast and Flexible Pipeline for Text Classification
View on CRAN: Click here


Download and install quickSentiment package within the R console
Install from CRAN:
install.packages("quickSentiment")

Install from Github:
library("remotes")
install_github("cran/quickSentiment")

Install by package version:
library("remotes")
install_version("quickSentiment", "0.1.0")



Attach the package and use:
library("quickSentiment")
Maintained by
Alabhya Dahal
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-02-06
Latest Update: 2026-02-06
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.
How to cite:
Alabhya Dahal (2026). quickSentiment: A Fast and Flexible Pipeline for Text Classification. R package version 0.1.0, https://cran.r-project.org/web/packages/quickSentiment. Accessed 06 Aug. 2026.
Previous versions and publish date:
(2026-07-13 07:30), 0.1.0 (2026-02-06 14:30), 0.2.0 (2026-02-15 06:10), 0.3.1 (2026-03-02 04:20), 0.3.2 (2026-03-20 07:20), 0.3.3 (2026-04-01 07:20), 0.3.4 (2026-04-17 05:30)
Other packages that cited quickSentiment R package
View quickSentiment citation profile
Other R packages that quickSentiment depends, imports, suggests or enhances
Complete documentation for quickSentiment
Functions, R codes and Examples using the quickSentiment R package
Full quickSentiment package functions and examples
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