BOW_train

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_train function

<dl><dt>doc</dt>
<dd>A character vector of documents to be processed.</dd>
<dt>weighting_scheme</dt>
<dd>A string specifying the weighting to apply. Must be one
of <code>"bow"</code>, <code>"binary"</code>, <code>"tf"</code>, or <code>"tfidf"</code>.
Defaults to <code>"bow"</code>.</dd>
<dt>ngram_size</dt>
<dd>An integer specifying the maximum n-gram size. For example,
`ngram_size = 1` will create unigrams only; `ngram_size = 2` will create unigrams and bigrams. Defaults to 1.</dd></dl>

Arguments

Train a Bag-of-Words Model — BOW_train

<dl>

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


<dt>weighting_scheme</dt>
<dd>A string specifying the weighting to apply. Must be one
of <code>"bow"</code>, <code>"binary"</code>, <code>"tf"</code>, or <code>"tfidf"</code>.
Defaults to <code>"bow"</code>.</dd>


<dt>ngram_size</dt>
<dd>An integer specifying the maximum n-gram size. For example,
`ngram_size = 1` will create unigrams only; `ngram_size = 2` will create unigrams and bigrams. Defaults to 1.</dd>

</dl>

BOW_train: Train a Bag-of-Words Model

Description

Usage

Value

Arguments

Examples