Man pages for quickSentiment
A Fast and Flexible Pipeline for Text Classification
| 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 (Chunked, With... |
| 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... |
| xgb_model | Train a Gradient Boosting Model using XGBoost |
