Package 'textclassificationtutorial'

Calculate binary classification metrics

Description

Calculate binary classification metrics

Usage

classification_metrics(truth, estimate, positive)

Arguments

truth	Vector of true classes.
estimate	Vector of predicted classes.
positive	Value identifying the positive class.

Value

A one-row data frame containing confusion counts, accuracy, balanced accuracy, precision, recall, specificity, and F1.

Examples

classification_metrics(c("task", "task", "other"), c("task", "other", "other"),
                       positive = "task")

---

Compute cosine similarity

Description

Compute cosine similarity

Usage

cosine_similarity(x, y = NULL)

Arguments

x	Numeric matrix whose rows are observations.
y	Optional numeric matrix with the same columns as x. When omitted, computes all pairwise similarities among rows of x.

Value

A numeric similarity matrix. Similarities involving zero vectors are returned as NA.

Examples

x <- rbind(a = c(1, 1, 0), b = c(1, 0, 0), c = c(0, 0, 1))
cosine_similarity(x)

---

Construct a document-term matrix

Description

Construct a document-term matrix

Usage

document_term_matrix(
  text,
  document_ids = NULL,
  binary = FALSE,
  min_doc_freq = 1L,
  max_doc_prop = 1
)

Arguments

text	Character vector containing one preprocessed document per item.
document_ids	Optional unique document identifiers.
binary	Logical; store term presence instead of term frequency?
min_doc_freq	Minimum number of documents in which a term must occur.
max_doc_prop	Maximum proportion of documents in which a term may occur.

Value

A numeric matrix with class text_dtm.

Examples

docs <- preprocess_text(c("data science", "data analysis", "science"))
document_term_matrix(docs)

---

Extract text from a directory of HTML files

Description

Extract text from a directory of HTML files

Usage

extract_html_dir(path, pattern = "\\.html?$", recursive = FALSE, ...)

Arguments

path	Directory containing HTML files.
pattern	File-name regular expression.
recursive	Logical; search recursively?
...	Passed to extract_html_text().

Value

A data frame with document_id, path, and text.

---

Extract readable text from HTML

Description

Extracts text from an HTML file or character string. When the suggested xml2 package is installed, simple CSS selectors (tag, .class, ⁠#id⁠, or tag.class) or XPath can target a specific part of the page. A dependency-free fallback strips markup from the full document.

Usage

extract_html_text(
  x,
  selector = NULL,
  xpath = NULL,
  collapse = "\n",
  trim = TRUE
)

Arguments

x	Path to an HTML file or a length-one HTML character string.
selector	Optional CSS selector.
xpath	Optional XPath expression. Supply at most one of selector and xpath.
collapse	Character used to join matched nodes.
trim	Logical; normalize whitespace and trim the result?

Value

A length-one character vector containing extracted text.

Examples

html <- "<html><body><h1>Analyst</h1><p>Analyze data.</p></body></html>"
extract_html_text(html)

---

Extract top TF-IDF keywords

Description

Extract top TF-IDF keywords

Usage

extract_keywords(x, n = 1L, already_tfidf = FALSE)

Arguments

x	A document-term matrix or TF-IDF matrix.
n	Number of keywords per document.
already_tfidf	Logical; is x already weighted?

Value

A data frame with document, rank, term, and weight.

---

F-measure

Description

F-measure

Usage

f_measure(precision, recall, beta = 1)

Arguments

precision	Numeric precision.
recall	Numeric recall.
beta	Relative weight assigned to recall.

Value

Numeric F-measure.

---

Fit a multinomial Naive Bayes text classifier

Description

Fit a multinomial Naive Bayes text classifier

Usage

fit_naive_bayes(x, y, laplace = 1, prior = NULL)

Arguments

x	Nonnegative numeric document-term matrix.
y	Class labels with one value per row of x.
laplace	Nonnegative additive smoothing parameter.
prior	Optional named class probabilities.

Value

An object of class text_nb.

Examples

x <- rbind(c(3, 0), c(2, 0), c(0, 3), c(0, 2))
colnames(x) <- c("analysis", "care")
model <- fit_naive_bayes(x, c("data", "data", "health", "health"))
predict(model, x)

---

Normalize text for document-term analysis

Description

Normalize text for document-term analysis

Usage

preprocess_text(
  text,
  lowercase = TRUE,
  remove_punctuation = TRUE,
  remove_numbers = TRUE,
  stopwords = character(),
  min_token_length = 1L
)

Arguments

text	Character vector.
lowercase	Logical; convert text to lowercase?
remove_punctuation	Logical; replace punctuation with spaces?
remove_numbers	Logical; replace digits with spaces?
stopwords	Optional character vector of words to remove.
min_token_length	Minimum number of characters per token.

Value

A character vector of normalized documents.

Examples

preprocess_text(
  c("Analyze the data!", "Present 2 reports."),
  stopwords = c("the")
)

---

Read a one-label-per-line file

Description

Read a one-label-per-line file

Usage

read_label_file(path, type = c("character", "integer", "numeric", "factor"))

Arguments

path	Path to a text file.
type	Return labels as character, integer, numeric, or factor.

Value

A vector of labels.

---

Split text into sentences

Description

A lightweight sentence segmenter suitable for tutorials and clean prose. It splits at terminal punctuation followed by whitespace, and optionally at line breaks and vertical bars. For production multilingual segmentation, use a dedicated NLP tokenizer.

Usage

split_sentences(
  text,
  split_lines = TRUE,
  keep_punctuation = TRUE,
  drop_empty = TRUE
)

Arguments

text	Character vector.
split_lines	Logical; treat line breaks and | as boundaries?
keep_punctuation	Logical; retain terminal punctuation?
drop_empty	Logical; remove empty results?

Value

A character vector of sentences.

Examples

split_sentences("Analyze data. Present results! Work with teams?")

---

Create stratified cross-validation folds

Description

Create stratified cross-validation folds

Usage

stratified_folds(y, k = 5L, repeats = 1L, seed = NULL)

Arguments

y	Class labels.
k	Number of folds.
repeats	Number of repeated fold sets.
seed	Optional random seed. The caller's random-number state is restored.

Value

A list of integer test-set indices with class text_folds.

---

Calculate TF-IDF weights

Description

Calculate TF-IDF weights

Usage

tf_idf(dtm, normalize = c("length", "max", "none"), smooth_idf = TRUE)

Arguments

dtm	Numeric document-term matrix.
normalize	Term-frequency normalization: document length, maximum frequency, or none.
smooth_idf	Logical; use smoothed inverse document frequency?

Value

A numeric matrix of TF-IDF weights.

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