A Fast and Flexible Pipeline for Text Classification

Documentation for package ‘quickSentiment’ version 0.3.4

BOW_test	Transform New Text into a Document-Feature Matrix
BOW_train	Train a Bag-of-Words Model
evaluate_performance	Evaluate Model Performance (ROC and Precision-Recall)
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
plot.quickSentiment_prc	Plot Precision-Recall Curve
plot.quickSentiment_roc	Plot ROC Curve
predict_sentiment	Predict Sentiment on New Data Using a Saved Pipeline Artifact
pre_process	Preprocess a Vector of Text Documents
print.quickSentiment_eval	Print quickSentiment Evaluation Results
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