BOW_train: Train a Bag-of-Words Model

In quickSentiment: A Fast and Flexible Pipeline for Text Classification

Train a Bag-of-Words Model

Description

Train a Bag-of-Words Model

Usage

BOW_train(doc, weighting_scheme = "bow", ngram_size = 1)

Arguments

doc	A character vector of documents to be processed.
weighting_scheme	A string specifying the weighting to apply. Defaults to "bag_of_words". "bag_of_words" (Alias: "bow") - Standard count of words. "term_frequency" (Alias: "tf") - Normalized counts (frequency relative to document length). "tfidf" (Alias: "tf-idf") - Term Frequency-Inverse Document Frequency. "binary" - Presence/Absence (1/0).
ngram_size	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.

Value

An object of class "qs_bow_fit" containing:

• dfm_template: a quanteda dfm template

• weighting_scheme: the weighting used

• ngram_size: the n-gram size used

#'

Examples

txt <- c("text one", "text two text")
fit <- BOW_train(txt, weighting_scheme = "bow")
fit$dfm_template

quickSentiment documentation built on Aug. 29, 2026, 1:07 a.m.