A Fast and Flexible Pipeline for Text Classification

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Documentation for package ‘quickSentiment’ version 0.3.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

BOW_test	Transform New Text into a Document-Feature Matrix
BOW_train	Train a Bag-of-Words Model
evaluate_metrics	Calculate Classification Metrics
logit_model	Train a Regularized Logistic Regression Model using glmnet
nb_model	Multinomial Naive Bayes for Text Classification
pipeline	Run a Full Text Classification Pipeline on Preprocessed Text
predict_sentiment	Predict Sentiment on New Data Using a Saved Pipeline Artifact
pre_process	Preprocess a Vector of Text Documents
qs_negations	Standard Negation Words for Sentiment Analysis
rf_model	functions/random_forest_fast.R Train a Random Forest Model using Ranger
xgb_model	Train a Gradient Boosting Model using XGBoost