Package: quickSentiment 0.3.4

--- 1. SETUP: LOAD LIBRARIES --- | ------------------------------------------------------------------- | --- 2. LOAD AND PREPARE TRAINING DATA --- | --- 3. PREPROCESS THE TEXT --- | Use the pre_process() function from our package to clean the raw text. | This step is done externally to the main pipeline, allowing you to reuse | the same cleaned text for multiple different models or analyses in the future. | --- 4. RUN THE MAIN TRAINING PIPELINE --- | This is the core of the package. We call the main pipeline() function | to handle the train/test split, vectorization, model training, and evaluation. | =================================================================== | --- 5. EVALUATE THE RESULTS | Get the AUC, ROC and Accuracy at Decile Threshold | --- 6. PREDICTION ON NEW, UNSEEN DATA --- | The training is complete. The 'result' object now contains our trained | model and all the necessary "artifacts" for prediction.