BOW_test

This function takes a character vector of new documents and transforms it
into a DFM that has the exact same features as a pre-fitted training DFM,
ensuring consistency for prediction.

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.

Alabhya Dahal

quickSentiment

A Fast and Flexible Pipeline for Text Classification

BOW_test function

<dl><dt>doc</dt>
<dd>A character vector of new documents to be processed.</dd>
<dt>fit</dt>
<dd>A fitted BoW object returned by <code>BOW_train()</code>.</dd></dl>

Arguments

Transform New Text into a Document-Feature Matrix — BOW_test

<dl>

<dt>doc</dt>
<dd>A character vector of new documents to be processed.</dd>


<dt>fit</dt>
<dd>A fitted BoW object returned by <code>BOW_train()</code>.</dd>

</dl>

BOW_test: Transform New Text into a Document-Feature Matrix

Description

Usage

Value

Arguments

Examples