Package 'quanteda'

Description

Coercion functions to and from corpus objects, including conversion to a plain character object; and checks for whether an object is a corpus.

Usage

## S3 method for class 'corpus'
as.character(x, use.names = TRUE, ...)

is.corpus(x)

as.corpus(x)

Arguments

Value

as.character() returns the corpus as a plain character vector, with or without named elements.

is.corpus returns TRUE if the object is a corpus.

as.corpus() upgrades a corpus object to the newest format. object.

Note

as.character(x) where x is a corpus is equivalent to calling the deprecated texts(x).

Description

Convert an eligible input object into a dfm, or check whether an object is a dfm. Current eligible inputs for coercion to a dfm are: matrix, (sparse) Matrix, TermDocumentMatrix and DocumentTermMatrix (from the tm package), data.frame, and other dfm objects.

Usage

as.dfm(x)

is.dfm(x)

Arguments

Value

as.dfm converts an input object into a dfm. Row names are used for docnames, and column names for featnames, of the resulting dfm.

is.dfm returns TRUE if and only if its argument is a dfm.

See Also

as.data.frame.dfm(), as.matrix.dfm(), convert()

Description

Convert a dictionary from a different format into a quanteda dictionary, or check to see if an object is a dictionary.

Usage

as.dictionary(x, ...)

## S3 method for class 'data.frame'
as.dictionary(x, format = c("tidytext"), separator = " ", tolower = FALSE, ...)

is.dictionary(x)

Arguments

Value

as.dictionary returns a quanteda dictionary object. This conversion function differs from the dictionary() constructor function in that it converts an existing object rather than creates one from components or from a file.

is.dictionary returns TRUE if an object is a quanteda dictionary.

Examples

## Not run: 
data(sentiments, package = "tidytext")
as.dictionary(subset(sentiments, lexicon == "nrc"))
as.dictionary(subset(sentiments, lexicon == "bing"))
# to convert AFINN into polarities - adjust thresholds if desired
datafinn <- subset(sentiments, lexicon == "AFINN")
datafinn[["sentiment"]] <-
    with(datafinn,
         sentiment <- ifelse(score < 0, "negative",
                             ifelse(score > 0, "positive", "netural"))
    )
with(datafinn, table(score, sentiment))
as.dictionary(datafinn)

dat <- data.frame(
    word = c("Great", "Horrible"),
    sentiment = c("positive", "negative")
    )
as.dictionary(dat)
as.dictionary(dat, tolower = FALSE)

## End(Not run)

is.dictionary(dictionary(list(key1 = c("val1", "val2"), key2 = "val3")))
# [1] TRUE
is.dictionary(list(key1 = c("val1", "val2"), key2 = "val3"))
# [1] FALSE

Description

Convert an eligible input object into a fcm, or check whether an object is a fcm. Current eligible inputs for coercion to a dfm are: matrix, (sparse) Matrix and other fcm objects.

Usage

as.fcm(x)

Arguments

Value

as.fcm converts an input object into a fcm.

Description

Coercion functions to and from tokens objects, checks for whether an object is a tokens object, and functions to combine tokens objects.

Usage

## S3 method for class 'tokens'
as.list(x, ...)

## S3 method for class 'tokens'
as.character(x, use.names = FALSE, ...)

is.tokens(x)

as.tokens(x, concatenator = "_", ...)

## S3 method for class 'spacyr_parsed'
as.tokens(
  x,
  concatenator = "/",
  include_pos = c("none", "pos", "tag"),
  use_lemma = FALSE,
  ...
)

is.tokens(x)

Arguments

Details

The concatenator is used to automatically generate dictionary values for multi-word expressions in tokens_lookup() and dfm_lookup(). The underscore character is commonly used to join elements of multi-word expressions (e.g. "piece_of_cake", "New_York"), but other characters (e.g. whitespace " " or a hyphen "-") can also be used. In those cases, users have to tell the system what is the concatenator in your tokens so that the conversion knows to treat this character as the inter-word delimiter, when reading in the elements that will become the tokens.

Value

as.list returns a simple list of characters from a tokens object.

as.character returns a character vector from a tokens object.

is.tokens returns TRUE if the object is of class tokens, FALSE otherwise.

as.tokens returns a quanteda tokens object.

is.tokens returns TRUE if the object is of class tokens, FALSE otherwise.

Examples

# create tokens object from list of characters with custom concatenator
dict <- dictionary(list(country = "United States",
                   sea = c("Atlantic Ocean", "Pacific Ocean")))
lis <- list(c("The", "United-States", "has", "the", "Atlantic-Ocean",
              "and", "the", "Pacific-Ocean", "."))
toks <- as.tokens(lis, concatenator = "-")
tokens_lookup(toks, dict)

Description

Methods for coercing a dfm object to a matrix or data.frame object.

Usage

## S3 method for class 'dfm'
as.matrix(x, ...)

Arguments

Examples

# coercion to matrix
as.matrix(data_dfm_lbgexample[, 1:10])

Description

Converts a quanteda dictionary object constructed by the dictionary function into the YAML format. The YAML files can be edited in text editors and imported into quanteda again.

Usage

as.yaml(x)

Arguments

Value

as.yaml a dictionary in the YAML format, as a character object

Examples

## Not run: 
dict <- dictionary(list(one = c("a b", "c*"), two = c("x", "y", "z??")))
cat(yaml <- as.yaml(dict))
cat(yaml, file = (yamlfile <- paste0(tempfile(), ".yml")))
dictionary(file = yamlfile)

## End(Not run)

Description

Create an array of resampled dfms.

Usage

bootstrap_dfm(x, n = 10, ..., verbose = quanteda_options("verbose"))

Arguments

Details

Function produces multiple, resampled dfm objects, based on resampling sentences (with replacement) from each document, recombining these into new "documents" and computing a dfm for each. Resampling of sentences is done strictly within document, so that every resampled document will contain at least some of its original tokens.

Value

A named list of dfm objects, where the first, dfm_0, is the dfm from the original texts, and subsequent elements are the sentence-resampled dfms.

Author(s)

Kenneth Benoit

Examples

# bootstrapping from the original text
set.seed(10)
txt <- c(textone = "This is a sentence.  Another sentence.  Yet another.",
         texttwo = "Premiere phrase.  Deuxieme phrase.")
dfmat <- dfm(tokens(txt))
bootstrap_dfm(dfmat, n = 3, verbose = TRUE)

Description

These function select or discard elements from a character object. For convenience, the functions char_remove and char_keep are defined as shortcuts for char_select(x, pattern, selection = "remove") and char_select(x, pattern, selection = "keep"), respectively.

These functions make it easy to change, for instance, stopwords based on pattern matching.

Usage

char_select(
  x,
  pattern,
  selection = c("keep", "remove"),
  valuetype = c("glob", "fixed", "regex"),
  case_insensitive = TRUE
)

char_remove(x, ...)

char_keep(x, ...)

Arguments

Value

a modified character vector

Examples

# character selection
mykeywords <- c("natural", "national", "denatured", "other")
char_select(mykeywords, "nat*", valuetype = "glob")
char_select(mykeywords, "nat", valuetype = "regex")
char_select(mykeywords, c("natur*", "other"))
char_select(mykeywords, c("natur*", "other"), selection = "remove")

# character removal
char_remove(letters[1:5], c("a", "c", "x"))
words <- c("any", "and", "Anna", "as", "announce", "but")
char_remove(words, "an*")
char_remove(words, "an*", case_insensitive = FALSE)
char_remove(words, "^.n.+$", valuetype = "regex")

# remove some of the system stopwords
stopwords("en", source = "snowball")[1:6]
stopwords("en", source = "snowball")[1:6] |>
  char_remove(c("me", "my*"))
  
# character keep
char_keep(letters[1:5], c("a", "c", "x"))

Description

char_tolower and char_toupper are replacements for base::tolower() and base::tolower() based on the stringi package. The stringi functions for case conversion are superior to the base functions because they correctly handle case conversion for Unicode. In addition, the ⁠*_tolower()⁠ functions provide an option for preserving acronyms.

Usage

char_tolower(x, keep_acronyms = FALSE)

char_toupper(x)

Arguments

Examples

txt1 <- c(txt1 = "b A A", txt2 = "C C a b B")
char_tolower(txt1)
char_toupper(txt1)

# with acronym preservation
txt2 <- c(text1 = "England and France are members of NATO and UNESCO",
          text2 = "NASA sent a rocket into space.")
char_tolower(txt2)
char_tolower(txt2, keep_acronyms = TRUE)
char_toupper(txt2)

Description

Get the concatenator character from a tokens object.

Usage

concat(x)

concatenator(x)

Arguments

Details

The concatenator character is a special delimiter used to link separate tokens in multi-token phrases. It is embedded in the meta-data of tokens objects and used in downstream operations, such as tokens_compound() or tokens_lookup(). It can be extracted using concat() and set using tokens(x, concatenator = ...) when x is a tokens object.

The default ⁠_⁠ is recommended since it will not be removed during normal cleaning and tokenization (while nearly all other punctuation characters, at least those in the Unicode punctuation class ⁠[P]⁠ will be removed).

Value

a character of length 1

Examples

toks <- tokens(data_corpus_inaugural[1:5])
concat(toks)

Description

Convert a quanteda dfm or corpus object to a format useable by other packages. The general function convert provides easy conversion from a dfm to the document-term representations used in all other text analysis packages for which conversions are defined. For corpus objects, convert provides an easy way to make a corpus and its document variables into a data.frame.

Usage

convert(x, to, ...)

## S3 method for class 'dfm'
convert(
  x,
  to = c("lda", "tm", "stm", "austin", "topicmodels", "lsa", "matrix", "data.frame",
    "tripletlist"),
  docvars = NULL,
  omit_empty = TRUE,
  docid_field = "doc_id",
  ...
)

## S3 method for class 'corpus'
convert(x, to = c("data.frame", "json"), pretty = FALSE, ...)

Arguments

Value

A converted object determined by the value of to (see above). See conversion target package documentation for more detailed descriptions of the return formats.

Examples

## convert a dfm

toks <- corpus_subset(data_corpus_inaugural, Year > 1970) |>
    tokens()
dfmat1 <- dfm(toks)

# austin's wfm format
identical(dim(dfmat1), dim(convert(dfmat1, to = "austin")))

# stm package format
stmmat <- convert(dfmat1, to = "stm")
str(stmmat)

# triplet
tripletmat <- convert(dfmat1, to = "tripletlist")
str(tripletmat)

## Not run: 
# tm's DocumentTermMatrix format
tmdfm <- convert(dfmat1, to = "tm")
str(tmdfm)

# topicmodels package format
str(convert(dfmat1, to = "topicmodels"))

# lda package format
str(convert(dfmat1, to = "lda"))

## End(Not run)

## convert a corpus into a data.frame

corp <- corpus(c(d1 = "Text one.", d2 = "Text two."),
               docvars = data.frame(dvar1 = 1:2, dvar2 = c("one", "two"),
                                    stringsAsFactors = FALSE))
convert(corp, to = "data.frame")
convert(corp, to = "json")

Description

Creates a corpus object from available sources. The currently available sources are:

• a character vector, consisting of one document per element; if the elements are named, these names will be used as document names.

• a data.frame (or a tibble tbl_df), whose default document id is a variable identified by docid_field; the text of the document is a variable identified by text_field; and other variables are imported as document-level meta-data. This matches the format of data.frames constructed by the the readtext package.

• a kwic object constructed by kwic().

• a tm VCorpus or SimpleCorpus class object, with the fixed metadata fields imported as docvars and corpus-level metadata imported as meta information.

• a corpus object.

Usage

corpus(x, ...)

## S3 method for class 'corpus'
corpus(
  x,
  docnames = quanteda::docnames(x),
  docvars = quanteda::docvars(x),
  meta = quanteda::meta(x),
  ...
)

## S3 method for class 'character'
corpus(
  x,
  docnames = NULL,
  docvars = NULL,
  meta = list(),
  unique_docnames = TRUE,
  ...
)

## S3 method for class 'data.frame'
corpus(
  x,
  docid_field = "doc_id",
  text_field = "text",
  meta = list(),
  unique_docnames = TRUE,
  ...
)

## S3 method for class 'kwic'
corpus(
  x,
  split_context = TRUE,
  extract_keyword = TRUE,
  meta = list(),
  concatenator = " ",
  ...
)

## S3 method for class 'Corpus'
corpus(x, ...)

Arguments

Details

The texts and document variables of corpus objects can also be accessed using index notation and the $ operator for accessing or assigning docvars. For details, see [.corpus().

Value

A corpus class object containing the original texts, document-level variables, document-level metadata, corpus-level metadata, and default settings for subsequent processing of the corpus.

For quanteda >= 2.0, this is a specially classed character vector. It has many additional attributes but you should not access these attributes directly, especially if you are another package author. Use the extractor and replacement functions instead, or else your code is not only going to be uglier, but also likely to break should the internal structure of a corpus object change. Using the accessor and replacement functions ensures that future code to manipulate corpus objects will continue to work.

See Also

corpus, docvars(), meta(), as.character.corpus(), ndoc(), docnames()

Examples

# create a corpus from texts
corpus(data_char_ukimmig2010)

# create a corpus from texts and assign meta-data and document variables
summary(corpus(data_char_ukimmig2010,
               docvars = data.frame(party = names(data_char_ukimmig2010))), 5)

# import a tm VCorpus
if (requireNamespace("tm", quietly = TRUE)) {
    data(crude, package = "tm")    # load in a tm example VCorpus
    vcorp <- corpus(crude)
    summary(vcorp)

    data(acq, package = "tm")
    summary(corpus(acq), 5)

    vcorp2 <- tm::VCorpus(tm::VectorSource(data_char_ukimmig2010))
    corp <- corpus(vcorp2)
    summary(corp)
}

# construct a corpus from a data.frame
dat <- data.frame(letter_factor = factor(rep(letters[1:3], each = 2)),
                  some_ints = 1L:6L,
                  some_text = paste0("This is text number ", 1:6, "."),
                  stringsAsFactors = FALSE,
                  row.names = paste0("fromDf_", 1:6))
dat
summary(corpus(dat, text_field = "some_text",
               meta = list(source = "From a data.frame called mydf.")))

Description

Combine documents in a corpus object by a grouping variable, by concatenating their texts in the order of the documents within each grouping variable.

Usage

corpus_group(x, groups = docid(x), fill = FALSE, concatenator = " ")

Arguments

Value

a corpus object whose documents are equal to the unique group combinations, and whose texts are the concatenations of the texts by group. Document-level variables that have no variation within groups are saved in docvars. Document-level variables that are lists are dropped from grouping, even when these exhibit no variation within groups.

Examples

corp <- corpus(c("a a b", "a b c c", "a c d d", "a c c d"),
               docvars = data.frame(grp = c("grp1", "grp1", "grp2", "grp2")))
corpus_group(corp, groups = grp)
corpus_group(corp, groups = c(1, 1, 2, 2))
corpus_group(corp, groups = factor(c(1, 1, 2, 2), levels = 1:3))

# with fill
corpus_group(corp, groups = factor(c(1, 1, 2, 2), levels = 1:3), fill = TRUE)

Description

For a corpus, reshape (or recast) the documents to a different level of aggregation. Units of aggregation can be defined as documents, paragraphs, or sentences. Because the corpus object records its current "units" status, it is possible to move from recast units back to original units, for example from documents, to sentences, and then back to documents (possibly after modifying the sentences).

Usage

corpus_reshape(
  x,
  to = c("sentences", "paragraphs", "documents"),
  use_docvars = TRUE,
  ...
)

Arguments

Value

A corpus object with the documents defined as the new units, including document-level meta-data identifying the original documents.

Examples

# simple example
corp1 <- corpus(c(textone = "This is a sentence.  Another sentence.  Yet another.",
                 textwo = "Premiere phrase.  Deuxieme phrase."),
                 docvars = data.frame(country=c("UK", "USA"), year=c(1990, 2000)))
summary(corp1)
summary(corpus_reshape(corp1, to = "sentences"))

# example with inaugural corpus speeches
(corp2 <- corpus_subset(data_corpus_inaugural, Year>2004))
corp2para <- corpus_reshape(corp2, to = "paragraphs")
corp2para
summary(corp2para, 50, showmeta = TRUE)
## Note that Bush 2005 is recorded as a single paragraph because that text
## used a single \n to mark the end of a paragraph.

Description

Take a random sample of documents of the specified size from a corpus, with or without replacement, optionally by grouping variables or with probability weights.

Usage

corpus_sample(x, size = ndoc(x), replace = FALSE, prob = NULL, by = NULL)

Arguments

Value

a corpus object (re)sampled on the documents, containing the document variables for the documents sampled.

Examples

set.seed(123)
# sampling from a corpus
summary(corpus_sample(data_corpus_inaugural, size = 5))
summary(corpus_sample(data_corpus_inaugural, size = 10, replace = TRUE))

# sampling with by
corp <- data_corpus_inaugural
corp$century <- paste(floor(corp$Year / 100) + 1)
corp$century <- paste0(corp$century, ifelse(corp$century < 21, "th", "st"))
corpus_sample(corp, size = 2, by = century) |>
    summary()
# needs drop = TRUE to avoid empty interactions
corpus_sample(corp, size = 1, by = interaction(Party, century, drop = TRUE), replace = TRUE) |>
    summary()

# sampling sentences by document
corp <- corpus(c(one = "Sentence one.  Sentence two.  Third sentence.",
                 two = "First sentence, doc2.  Second sentence, doc2."),
               docvars = data.frame(var1 = c("a", "a"), var2 = c(1, 2)))
corpus_reshape(corp, to = "sentences") %>%
    corpus_sample(replace = TRUE, by = docid(.))

# oversampling
corpus_sample(corp, size = 5, replace = TRUE)

Description

Segment corpus text(s) or a character vector, splitting on a pattern match. This is useful for breaking the texts into smaller documents based on a regular pattern (such as a speaker identifier in a transcript) or a user-supplied annotation.

Usage

corpus_segment(
  x,
  pattern = "##*",
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE,
  extract_pattern = TRUE,
  pattern_position = c("before", "after"),
  use_docvars = TRUE
)

char_segment(
  x,
  pattern = "##*",
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE,
  remove_pattern = TRUE,
  pattern_position = c("before", "after")
)

Arguments

Details

For segmentation into syntactic units defined by the locale (such as sentences), use corpus_reshape() instead. In cases where more fine-grained segmentation is needed, such as that based on commas or semi-colons (phrase delimiters within a sentence), corpus_segment() offers greater user control than corpus_reshape().

Value

corpus_segment returns a corpus of segmented texts

char_segment returns a character vector of segmented texts

Boundaries and segmentation explained

The pattern acts as a boundary delimiter that defines the segmentation points for splitting a text into new "document" units. Boundaries are always defined as the pattern matches, plus the end and beginnings of each document. The new "documents" that are created following the segmentation will then be the texts found between boundaries.

The pattern itself will be saved as a new document variable named pattern. This is most useful when segmenting a text according to tags such as names in a transcript, section titles, or user-supplied annotations. If the beginning of the file precedes a pattern match, then the extracted text will have a NA for the extracted pattern document variable (or when pattern_position = "after", this will be true for the text split between the last pattern match and the end of the document).

To extract syntactically defined sub-document units such as sentences and paragraphs, use corpus_reshape() instead.

Using patterns

One of the most common uses for corpus_segment is to partition a corpus into sub-documents using tags. The default pattern value is designed for a user-annotated tag that is a term beginning with double "hash" signs, followed by a whitespace, for instance as ⁠##INTRODUCTION The text⁠.

Glob and fixed pattern types use a whitespace character to signal the end of the pattern.

For more advanced pattern matches that could include whitespace or newlines, a regex pattern type can be used, for instance a text such as

⁠Mr. Smith: Text⁠
⁠Mrs. Jones: More text⁠

could have as pattern = "\\b[A-Z].+\\.\\s[A-Z][a-z]+:", which would catch the title, the name, and the colon.

For custom boundary delimitation using punctuation characters that come come at the end of a clause or sentence (such as ⁠,⁠ and., these can be specified manually and pattern_position set to "after". To keep the punctuation characters in the text (as with sentence segmentation), set extract_pattern = FALSE. (With most tag applications, users will want to remove the patterns from the text, as they are annotations rather than parts of the text itself.)

See Also

corpus_reshape(), for segmenting texts into pre-defined syntactic units such as sentences, paragraphs, or fixed-length chunks

Examples

## segmenting a corpus

# segmenting a corpus using tags
corp1 <- corpus(c("##INTRO This is the introduction.
                  ##DOC1 This is the first document.  Second sentence in Doc 1.
                  ##DOC3 Third document starts here.  End of third document.",
                 "##INTRO Document ##NUMBER Two starts before ##NUMBER Three."))
corpseg1 <- corpus_segment(corp1, pattern = "##*")
cbind(corpseg1, docvars(corpseg1))

# segmenting a transcript based on speaker identifiers
corp2 <- corpus("Mr. Smith: Text.\nMrs. Jones: More text.\nMr. Smith: I'm speaking, again.")
corpseg2 <- corpus_segment(corp2, pattern = "\\b[A-Z].+\\s[A-Z][a-z]+:",
                           valuetype = "regex")
cbind(corpseg2, docvars(corpseg2))

# segmenting a corpus using crude end-of-sentence segmentation
corpseg3 <- corpus_segment(corp1, pattern = ".", valuetype = "fixed",
                           pattern_position = "after", extract_pattern = FALSE)
cbind(corpseg3, docvars(corpseg3))

## segmenting a character vector

# segment into paragraphs and removing the "- " bullet points
cat(data_char_ukimmig2010[4])
char_segment(data_char_ukimmig2010[4],
             pattern = "\\n\\n(-\\s){0,1}", valuetype = "regex",
             remove_pattern = TRUE)

# segment a text into clauses
txt <- c(d1 = "This, is a sentence?  You: come here.", d2 = "Yes, yes okay.")
char_segment(txt, pattern = "\\p{P}", valuetype = "regex",
             pattern_position = "after", remove_pattern = FALSE)

Description

Returns subsets of a corpus that meet certain conditions, including direct logical operations on docvars (document-level variables). corpus_subset functions identically to subset.data.frame(), using non-standard evaluation to evaluate conditions based on the docvars in the corpus.

Usage

corpus_subset(x, subset, drop_docid = TRUE, ...)

Arguments

Value

corpus object, with a subset of documents (and docvars) selected according to arguments

See Also

subset.data.frame()

Examples

summary(corpus_subset(data_corpus_inaugural, Year > 1980))
summary(corpus_subset(data_corpus_inaugural, Year > 1930 & President == "Roosevelt"))

Description

Removes sentences from a corpus or a character vector shorter than a specified length.

Usage

corpus_trim(
  x,
  what = c("sentences", "paragraphs", "documents"),
  min_ntoken = 1,
  max_ntoken = NULL,
  exclude_pattern = NULL
)

char_trim(
  x,
  what = c("sentences", "paragraphs", "documents"),
  min_ntoken = 1,
  max_ntoken = NULL,
  exclude_pattern = NULL
)

Arguments

Value

a corpus or character vector equal in length to the input. If the input was a corpus, then the all docvars and metadata are preserved. For documents whose sentences have been removed entirely, a null string ("") will be returned.

Examples

txt <- c("PAGE 1. This is a single sentence.  Short sentence. Three word sentence.",
         "PAGE 2. Very short! Shorter.",
         "Very long sentence, with multiple parts, separated by commas.  PAGE 3.")
corp <- corpus(txt, docvars = data.frame(serial = 1:3))
corp

# exclude sentences shorter than 3 tokens
corpus_trim(corp, min_ntoken = 3)
# exclude sentences that start with "PAGE <digit(s)>"
corpus_trim(corp, exclude_pattern = "^PAGE \\d+")

# trimming character objects
char_trim(txt, "sentences", min_ntoken = 3)
char_trim(txt, "sentences", exclude_pattern = "sentence\\.")

Description

This is a long paragraph (2,914 characters) of text taken from a debate on Joe Higgins, delivered December 8, 2011.

Usage

data_char_sampletext

Format

character vector with one element

Source

Dáil Éireann Debate, Financial Resolution No. 13: General (Resumed). 7 December 2011. vol. 749, no. 1.

Examples

tokens(data_char_sampletext, remove_punct = TRUE)

Description

Extracts from the election manifestos of 9 UK political parties from 2010, related to immigration or asylum-seekers.

Usage

data_char_ukimmig2010

Format

A named character vector of plain ASCII texts

Examples

data_corpus_ukimmig2010 <- 
    corpus(data_char_ukimmig2010, 
           docvars = data.frame(party = names(data_char_ukimmig2010)))
summary(data_corpus_ukimmig2010, showmeta = TRUE)

Description

US presidential inaugural address texts, and metadata (for the corpus), from 1789 to present.

Usage

data_corpus_inaugural

Format

a corpus object with the following docvars:

• Year a four-digit integer year

• President character; President's last name

• FirstName character; President's first name (and possibly middle initial)

• Party factor; name of the President's political party

Details

data_corpus_inaugural is the quanteda-package corpus object of US presidents' inaugural addresses since 1789. Document variables contain the year of the address and the last name of the president.

Source

https://archive.org/details/Inaugural-Address-Corpus-1789-2009 and https://www.presidency.ucsb.edu/documents/presidential-documents-archive-guidebook/inaugural-addresses.

Examples

# some operations on the inaugural corpus
summary(data_corpus_inaugural)
head(docvars(data_corpus_inaugural), 10)

Description

Constructed example data to demonstrate the Wordscores algorithm, from Laver Benoit and Garry (2003), Table 1.

Usage

data_dfm_lbgexample

Format

A dfm object with 6 documents and 37 features.

Details

This is the example word count data from Laver, Benoit and Garry's (2003) Table 1. Documents R1 to R5 are assumed to have known positions: -1.5, -0.75, 0, 0.75, 1.5. Document V1 is assumed unknown, and will have a raw text score of approximately -0.45 when computed as per LBG (2003).

References

Laver, M., Benoit, K.R., & Garry, J. (2003). Estimating Policy Positions from Political Text using Words as Data. American Political Science Review, 97(2), 311–331.

Description

The 2015 Lexicoder Sentiment Dictionary in quanteda dictionary format.

Usage

data_dictionary_LSD2015

Format

A dictionary of four keys containing glob-style pattern matches.

negative

2,858 word patterns indicating negative sentiment

positive

1,709 word patterns indicating positive sentiment

neg_positive

1,721 word patterns indicating a positive word preceded by a negation (used to convey negative sentiment)

neg_negative

2,860 word patterns indicating a negative word preceded by a negation (used to convey positive sentiment)

Details

The dictionary consists of 2,858 "negative" sentiment words and 1,709 "positive" sentiment words. A further set of 2,860 and 1,721 negations of negative and positive words, respectively, is also included. While many users will find the non-negation sentiment forms of the LSD adequate for sentiment analysis, Young and Soroka (2012) did find a small, but non-negligible increase in performance when accounting for negations. Users wishing to test this or include the negations are encouraged to subtract negated positive words from the count of positive words, and subtract the negated negative words from the negative count.

Young and Soroka (2012) also suggest the use of a pre-processing script to remove specific cases of some words (i.e., "good bye", or "nobody better", which should not be counted as positive). Pre-processing scripts are available at https://www.snsoroka.com/data-lexicoder/.

License and Conditions

The LSD is available for non-commercial academic purposes only. By using data_dictionary_LSD2015, you accept these terms.

Please cite the references below when using the dictionary.

References

The objectives, development and reliability of the dictionary are discussed in detail in Young and Soroka (2012). Please cite this article when using the Lexicoder Sentiment Dictionary and related resources. Young, L. & Soroka, S. (2012). Lexicoder Sentiment Dictionary. Available at https://www.snsoroka.com/data-lexicoder/.

Young, L. & Soroka, S. (2012). Affective News: The Automated Coding of Sentiment in Political Texts. doi:10.1080/10584609.2012.671234. Political Communication, 29(2), 205–231.

Examples

# simple example
txt <- "This aggressive policy will not win friends."

tokens_lookup(tokens(txt), dictionary = data_dictionary_LSD2015, exclusive = FALSE)
## tokens from 1 document.
## text1 :
## [1] "This"   "NEGATIVE"   "policy"   "will"   "NEG_POSITIVE"   "POSITIVE"   "POSITIVE" "."

# notice that double-counting of negated and non-negated terms is avoided 
# when using nested_scope = "dictionary"
tokens_lookup(tokens(txt), dictionary = data_dictionary_LSD2015, 
              exclusive = FALSE, nested_scope = "dictionary")
## tokens from 1 document.
## text1 :
## [1] "This"   "NEGATIVE"   "policy"   "will"   "NEG_POSITIVE" "POSITIVE."   

# compound neg_negative and neg_positive tokens before creating a dfm object
toks <- tokens_compound(tokens(txt), data_dictionary_LSD2015)

dfm_lookup(dfm(toks), data_dictionary_LSD2015)

Description

Construct a sparse document-feature matrix from a tokens or dfm object.

Usage

dfm(
  x,
  tolower = TRUE,
  remove_padding = FALSE,
  verbose = quanteda_options("verbose"),
  ...
)

Arguments

Value

a dfm object

Changes in version 3

In quanteda v4, many convenience functions formerly available in dfm() were removed.

See Also

as.dfm(), dfm_select(), dfm

Examples

## for a corpus
toks <- data_corpus_inaugural |>
  corpus_subset(Year > 1980) |>
  tokens()
dfm(toks)

# removal options
toks <- tokens(c("a b c", "A B C D")) |>
    tokens_remove("b", padding = TRUE)
toks
dfm(toks)
dfm(toks) |>
 dfm_remove(pattern = "") # remove "pads"

# preserving case
dfm(toks, tolower = FALSE)

Description

"Compresses" or groups a dfm or fcm whose dimension names are the same, for either documents or features. This may happen, for instance, if features are made equivalent through application of a thesaurus. It could also be needed after a cbind.dfm() or rbind.dfm() operation. In most cases, you will not need to call dfm_compress, since it is called automatically by functions that change the dimensions of the dfm, e.g. dfm_tolower().

Usage

dfm_compress(
  x,
  margin = c("both", "documents", "features"),
  verbose = quanteda_options("verbose")
)

fcm_compress(x)

Arguments

Value

dfm_compress returns a dfm whose dimensions have been recombined by summing the cells across identical dimension names (docnames or featnames). The docvars will be preserved for combining by features but not when documents are combined.

fcm_compress returns an fcm whose features have been recombined by combining counts of identical features, summing their counts.

Note

fcm_compress works only when the fcm was created with a document context.

Examples

# dfm_compress examples
dfmat <- rbind(dfm(tokens(c("b A A", "C C a b B")), tolower = FALSE),
               dfm(tokens("A C C C C C"), tolower = FALSE))
colnames(dfmat) <- char_tolower(featnames(dfmat))
dfmat
dfm_compress(dfmat, margin = "documents")
dfm_compress(dfmat, margin = "features")
dfm_compress(dfmat)

# no effect if no compression needed
dfmatsubset <- dfm(tokens(data_corpus_inaugural[1:5]))
dim(dfmatsubset)
dim(dfm_compress(dfmatsubset))

# compress an fcm
fcmat1 <- fcm(tokens("A D a C E a d F e B A C E D"),
             context = "window", window = 3)
## this will produce an error:
# fcm_compress(fcmat1)

txt <- c("The fox JUMPED over the dog.",
         "The dog jumped over the fox.")
toks <- tokens(txt, remove_punct = TRUE)
fcmat2 <- fcm(toks, context = "document")
colnames(fcmat2) <- rownames(fcmat2) <- tolower(colnames(fcmat2))
colnames(fcmat2)[5] <- rownames(fcmat2)[5] <- "fox"
fcmat2
fcm_compress(fcmat2)

Description

Combine documents in a dfm by a grouping variable, by summing the cell frequencies within group and creating new "documents" with the group labels.

Usage

dfm_group(
  x,
  groups = docid(x),
  fill = FALSE,
  force = FALSE,
  verbose = quanteda_options("verbose")
)

Arguments

Value

dfm_group returns a dfm whose documents are equal to the unique group combinations, and whose cell values are the sums of the previous values summed by group. Document-level variables that have no variation within groups are saved in docvars. Document-level variables that are lists are dropped from grouping, even when these exhibit no variation within groups.

Examples

corp <- corpus(c("a a b", "a b c c", "a c d d", "a c c d"),
               docvars = data.frame(grp = c("grp1", "grp1", "grp2", "grp2")))
dfmat <- dfm(tokens(corp))
dfm_group(dfmat, groups = grp)
dfm_group(dfmat, groups = c(1, 1, 2, 2))

# with fill = TRUE
dfm_group(dfmat, fill = TRUE,
          groups = factor(c("A", "A", "B", "C"), levels = LETTERS[1:4]))

Description

Apply a dictionary to a dfm by looking up all dfm features for matches in a a set of dictionary values, and replace those features with a count of the dictionary's keys. If exclusive = FALSE then the behaviour is to apply a "thesaurus", where each value match is replaced by the dictionary key, converted to capitals if capkeys = TRUE (so that the replacements are easily distinguished from features that were terms found originally in the document).

Usage

dfm_lookup(
  x,
  dictionary,
  levels = 1:5,
  exclusive = TRUE,
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE,
  capkeys = !exclusive,
  nomatch = NULL,
  verbose = quanteda_options("verbose")
)

Arguments

Note

If using dfm_lookup with dictionaries containing multi-word values, matches will only occur if the features themselves are multi-word or formed from n-grams. A better way to match dictionary values that include multi-word patterns is to apply tokens_lookup() to the tokens, and then construct the dfm.

See Also

dfm_replace

Examples

dict <- dictionary(list(christmas = c("Christmas", "Santa", "holiday"),
                        opposition = c("Opposition", "reject", "notincorpus"),
                        taxglob = "tax*",
                        taxregex = "tax.+$",
                        country = c("United_States", "Sweden")))
dfmat <- dfm(tokens(c("My Christmas was ruined by your opposition tax plan.",
                      "Does the United_States or Sweden have more progressive taxation?")))
dfmat

# glob format
dfm_lookup(dfmat, dict, valuetype = "glob")
dfm_lookup(dfmat, dict, valuetype = "glob", case_insensitive = FALSE)

# regex v. glob format: note that "united_states" is a regex match for "tax*"
dfm_lookup(dfmat, dict, valuetype = "glob")
dfm_lookup(dfmat, dict, valuetype = "regex", case_insensitive = TRUE)

# fixed format: no pattern matching
dfm_lookup(dfmat, dict, valuetype = "fixed")
dfm_lookup(dfmat, dict, valuetype = "fixed", case_insensitive = FALSE)

# show unmatched tokens
dfm_lookup(dfmat, dict, nomatch = "_UNMATCHED")

Description

Match the feature set of a dfm to a specified vector of feature names. For existing features in x for which there is an exact match for an element of features, these will be included. Any features in x not features will be discarded, and any feature names specified in features but not found in x will be added with all zero counts.

Usage

dfm_match(x, features, verbose = quanteda_options("verbose"))

Arguments

Details

Selecting on another dfm's featnames() is useful when you have trained a model on one dfm, and need to project this onto a test set whose features must be identical. It is also used in bootstrap_dfm().

Value

A dfm whose features are identical to those specified in features.

Note

Unlike dfm_select(), this function will add feature names not already present in x. It also provides only fixed, case-sensitive matches. For more flexible feature selection, see dfm_select().

See Also

dfm_select()

Examples

# matching a dfm to a feature vector
dfm_match(dfm(tokens("")), letters[1:5])
dfm_match(data_dfm_lbgexample, c("A", "B", "Z"))
dfm_match(data_dfm_lbgexample, c("B", "newfeat1", "A", "newfeat2"))

# matching one dfm to another
txt <- c("This is text one", "The second text", "This is text three")
(dfmat1 <- dfm(tokens(txt[1:2])))
(dfmat2 <- dfm(tokens(txt[2:3])))
(dfmat3 <- dfm_match(dfmat1, featnames(dfmat2)))
setequal(featnames(dfmat2), featnames(dfmat3))

Description

Substitute features based on vectorized one-to-one matching for lemmatization or user-defined stemming.

Usage

dfm_replace(
  x,
  pattern,
  replacement,
  case_insensitive = TRUE,
  verbose = quanteda_options("verbose")
)

Arguments

Examples

dfmat1 <- dfm(tokens(data_corpus_inaugural))

# lemmatization
taxwords <- c("tax", "taxing", "taxed", "taxed", "taxation")
lemma <- rep("TAX", length(taxwords))
featnames(dfm_select(dfmat1, pattern = taxwords))
dfmat2 <- dfm_replace(dfmat1, pattern = taxwords, replacement = lemma)
featnames(dfm_select(dfmat2, pattern = taxwords))

# stemming
feat <- featnames(dfmat1)
featstem <- char_wordstem(feat, "porter")
dfmat3 <- dfm_replace(dfmat1, pattern = feat, replacement = featstem, case_insensitive = FALSE)
identical(dfmat3, dfm_wordstem(dfmat1, "porter"))

Description

Take a random sample of documents of the specified size from a dfm, with or without replacement, optionally by grouping variables or with probability weights.

Usage

dfm_sample(
  x,
  size = NULL,
  replace = FALSE,
  prob = NULL,
  by = NULL,
  verbose = quanteda_options("verbose")
)

Arguments

Value

a dfm object (re)sampled on the documents, containing the document variables for the documents sampled.

See Also

sample

Examples

set.seed(10)
dfmat <- dfm(tokens(c("a b c c d", "a a c c d d d", "a b b c")))
dfmat
dfm_sample(dfmat)
dfm_sample(dfmat, replace = TRUE)

# by groups
dfmat <- dfm(tokens(data_corpus_inaugural[50:58]))
dfm_sample(dfmat, by = Party, size = 2)

Description

This function selects or removes features from a dfm or fcm, based on feature name matches with pattern. The most common usages are to eliminate features from a dfm already constructed, such as stopwords, or to select only terms of interest from a dictionary.

Usage

dfm_select(
  x,
  pattern = NULL,
  selection = c("keep", "remove"),
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE,
  min_nchar = NULL,
  max_nchar = NULL,
  padding = FALSE,
  verbose = quanteda_options("verbose")
)

dfm_remove(x, ...)

dfm_keep(x, ...)

fcm_select(
  x,
  pattern = NULL,
  selection = c("keep", "remove"),
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE,
  verbose = quanteda_options("verbose"),
  ...
)

fcm_remove(x, ...)

fcm_keep(x, ...)

Arguments

Details

dfm_remove and fcm_remove are simply a convenience wrappers to calling dfm_select and fcm_select with selection = "remove".

dfm_keep and fcm_keep are simply a convenience wrappers to calling dfm_select and fcm_select with selection = "keep".

Value

A dfm or fcm object, after the feature selection has been applied.

For compatibility with earlier versions, when pattern is a dfm object and selection = "keep", then this will be equivalent to calling dfm_match(). In this case, the following settings are always used: case_insensitive = FALSE, and valuetype = "fixed". This functionality is deprecated, however, and you should use dfm_match() instead.

Note

This function selects features based on their labels. To select features based on the values of the document-feature matrix, use dfm_trim().

See Also

dfm_match()

Examples

dfmat <- tokens(c("My Christmas was ruined by your opposition tax plan.",
               "Does the United_States or Sweden have more progressive taxation?")) |>
    dfm(tolower = FALSE)
dict <- dictionary(list(countries = c("United_States", "Sweden", "France"),
                        wordsEndingInY = c("by", "my"),
                        notintext = "blahblah"))
dfm_select(dfmat, pattern = dict)
dfm_select(dfmat, pattern = dict, case_insensitive = FALSE)
dfm_select(dfmat, pattern = c("s$", ".y"), selection = "keep", valuetype = "regex")
dfm_select(dfmat, pattern = c("s$", ".y"), selection = "remove", valuetype = "regex")
dfm_select(dfmat, pattern = stopwords("english"), selection = "keep", valuetype = "fixed")
dfm_select(dfmat, pattern = stopwords("english"), selection = "remove", valuetype = "fixed")

# select based on character length
dfm_select(dfmat, min_nchar = 5)

dfmat <- dfm(tokens(c("This is a document with lots of stopwords.",
                      "No if, and, or but about it: lots of stopwords.")))
dfmat
dfm_remove(dfmat, stopwords("english"))
toks <- tokens(c("this contains lots of stopwords",
                 "no if, and, or but about it: lots"),
               remove_punct = TRUE)
fcmat <- fcm(toks)
fcmat
fcm_remove(fcmat, stopwords("english"))

Description

Sorts a dfm by descending frequency of total features, total features in documents, or both.

Usage

dfm_sort(x, decreasing = TRUE, margin = c("features", "documents", "both"))

Arguments

Value

A sorted dfm matrix object

Author(s)

Ken Benoit

Examples

dfmat <- dfm(tokens(data_corpus_inaugural))
head(dfmat)
head(dfm_sort(dfmat))
head(dfm_sort(dfmat, decreasing = FALSE, "both"))

Description

Returns document subsets of a dfm that meet certain conditions, including direct logical operations on docvars (document-level variables). dfm_subset functions identically to subset.data.frame(), using non-standard evaluation to evaluate conditions based on the docvars in the dfm.

Usage

dfm_subset(
  x,
  subset,
  min_ntoken = NULL,
  max_ntoken = NULL,
  drop_docid = TRUE,
  verbose = quanteda_options("verbose"),
  ...
)

Arguments

Details

To select or subset features, see dfm_select() instead.

Value

dfm object, with a subset of documents (and docvars) selected according to arguments

Examples

corp <- corpus(c(d1 = "a b c d", d2 = "a a b e",
                 d3 = "b b c e", d4 = "e e f a b"),
               docvars = data.frame(grp = c(1, 1, 2, 3)))
dfmat <- dfm(tokens(corp))
# selecting on a docvars condition
dfm_subset(dfmat, grp > 1)
# selecting on a supplied vector
dfm_subset(dfmat, c(TRUE, FALSE, TRUE, FALSE))

Description

Weight a dfm by term frequency-inverse document frequency (tf-idf), with full control over options. Uses fully sparse methods for efficiency.

Usage

dfm_tfidf(
  x,
  scheme_tf = "count",
  scheme_df = "inverse",
  base = 10,
  force = FALSE,
  ...
)

Arguments

Details

dfm_tfidf computes term frequency-inverse document frequency weighting. The default is to use counts instead of normalized term frequency (the relative term frequency within document), but this can be overridden using scheme_tf = "prop".

References

Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge: Cambridge University Press. https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf

Examples

dfmat1 <- as.dfm(data_dfm_lbgexample)
head(dfmat1[, 5:10])
head(dfm_tfidf(dfmat1)[, 5:10])
docfreq(dfmat1)[5:15]
head(dfm_weight(dfmat1)[, 5:10])

# replication of worked example from
# https://en.wikipedia.org/wiki/Tf-idf#Example_of_tf.E2.80.93idf
dfmat2 <-
    matrix(c(1,1,2,1,0,0, 1,1,0,0,2,3),
           byrow = TRUE, nrow = 2,
           dimnames = list(docs = c("document1", "document2"),
                           features = c("this", "is", "a", "sample",
                                        "another", "example"))) |>
    as.dfm()
dfmat2
docfreq(dfmat2)
dfm_tfidf(dfmat2, scheme_tf = "prop") |> round(digits = 2)

## Not run: 
# comparison with tm
if (requireNamespace("tm")) {
    convert(dfmat2, to = "tm") |> tm::weightTfIdf() |> as.matrix()
    # same as:
    dfm_tfidf(dfmat2, base = 2, scheme_tf = "prop")
}

## End(Not run)

Description

dfm_tolower() and dfm_toupper() convert the features of the dfm or fcm to lower and upper case, respectively, and then recombine the counts.

Usage

dfm_tolower(x, keep_acronyms = FALSE, verbose = quanteda_options("verbose"))

dfm_toupper(x, verbose = quanteda_options("verbose"))

fcm_tolower(x, keep_acronyms = FALSE, verbose = quanteda_options("verbose"))

fcm_toupper(x, verbose = quanteda_options("verbose"))

Arguments

Details

fcm_tolower() and fcm_toupper() convert both dimensions of the fcm to lower and upper case, respectively, and then recombine the counts. This works only on fcm objects created with context = "document".

Examples

# for a document-feature matrix
dfmat <- dfm(tokens(c("b A A", "C C a b B")), tolower = FALSE)
dfmat
dfm_tolower(dfmat)
dfm_toupper(dfmat)

# for a feature co-occurrence matrix
fcmat <- fcm(tokens(c("b A A d", "C C a b B e")),
             context = "document")
fcmat
fcm_tolower(fcmat)
fcm_toupper(fcmat)

Description

Returns a document by feature matrix reduced in size based on document and term frequency, usually in terms of a minimum frequency, but may also be in terms of maximum frequencies. Setting a combination of minimum and maximum frequencies will select features based on a range.

Feature selection is implemented by considering features across all documents, by summing them for term frequency, or counting the documents in which they occur for document frequency. Rank and quantile versions of these are also implemented, for taking the first $n$ features in terms of descending order of overall global counts or document frequencies, or as a quantile of all frequencies.

Usage

dfm_trim(
  x,
  min_termfreq = NULL,
  max_termfreq = NULL,
  termfreq_type = c("count", "prop", "rank", "quantile"),
  min_docfreq = NULL,
  max_docfreq = NULL,
  docfreq_type = c("count", "prop", "rank", "quantile"),
  sparsity = NULL,
  verbose = quanteda_options("verbose")
)

Arguments

Value

A dfm reduced in features (with the same number of documents)

Note

Trimming a dfm object is an operation based on the values in the document-feature matrix. To select subsets of a dfm based on the features themselves (meaning the feature labels from featnames()) – such as those matching a regular expression, or removing features matching a stopword list, use dfm_select().

See Also

dfm_select(), dfm_sample()

Examples

dfmat <- dfm(tokens(data_corpus_inaugural))

# keep only words occurring >= 10 times and in >= 2 documents
dfm_trim(dfmat, min_termfreq = 10, min_docfreq = 2)

# keep only words occurring >= 10 times and in at least 0.4 of the documents
dfm_trim(dfmat, min_termfreq = 10, min_docfreq = 0.4, docfreq_type = "prop")

# keep only words occurring <= 10 times and in <=2 documents
dfm_trim(dfmat, max_termfreq = 10, max_docfreq = 2)

# keep only words occurring <= 10 times and in at most 3/4 of the documents
dfm_trim(dfmat, max_termfreq = 10, max_docfreq = 0.75, docfreq_type = "prop")

# keep only words occurring 5 times in 1000, and in 2 of 5 of documents
dfm_trim(dfmat, min_docfreq = 0.4, min_termfreq = 0.005, termfreq_type = "prop")

## Not run: 
# compare to removeSparseTerms from the tm package
(dfmattm <- convert(dfmat, "tm"))
tm::removeSparseTerms(dfmattm, 0.7)
dfm_trim(dfmat, min_docfreq = 0.3)
dfm_trim(dfmat, sparsity = 0.7)

## End(Not run)

Description

Weight the feature frequencies in a dfm

Usage

dfm_weight(
  x,
  scheme = c("count", "prop", "propmax", "logcount", "boolean", "augmented", "logave"),
  weights = NULL,
  base = 10,
  k = 0.5,
  smoothing = 0.5,
  force = FALSE
)

dfm_smooth(x, smoothing = 1)

Arguments

Value

dfm_weight returns the dfm with weighted values. Note the because the default weighting scheme is "count", simply calling this function on an unweighted dfm will return the same object. Many users will want the normalized dfm consisting of the proportions of the feature counts within each document, which requires setting scheme = "prop".

dfm_smooth returns a dfm whose values have been smoothed by adding the smoothing amount. Note that this effectively converts a matrix from sparse to dense format, so may exceed memory requirements depending on the size of your input matrix.

References

Manning, C.D., Raghavan, P., & Schütze, H. (2008). An Introduction to Information Retrieval. Cambridge: Cambridge University Press. https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf

See Also

docfreq()

Examples

dfmat1 <- dfm(tokens(data_corpus_inaugural))

dfmat2 <- dfm_weight(dfmat1, scheme = "prop")
topfeatures(dfmat2)
dfmat3 <- dfm_weight(dfmat1)
topfeatures(dfmat3)
dfmat4 <- dfm_weight(dfmat1, scheme = "logcount")
topfeatures(dfmat4)
dfmat5 <- dfm_weight(dfmat1, scheme = "logave")
topfeatures(dfmat5)

# combine these methods for more complex dfm_weightings, e.g. as in Section 6.4
# of Introduction to Information Retrieval
head(dfm_tfidf(dfmat1, scheme_tf = "logcount"))

# smooth the dfm
dfmat <- dfm(tokens(data_corpus_inaugural))
dfm_smooth(dfmat, 0.5)

Description

Create a quanteda dictionary class object, either from a list or by importing from a foreign format. Currently supported input file formats are the WordStat, LIWC, Lexicoder v2 and v3, and Yoshikoder formats. The import using the LIWC format works with all currently available dictionary files supplied as part of the LIWC 2001, 2007, and 2015 software (see References).

Usage

dictionary(
  x,
  file = NULL,
  format = NULL,
  separator = " ",
  tolower = TRUE,
  encoding = "utf-8"
)

Arguments

Details

Dictionaries can be subsetted using [ and [[, operating the same as the equivalent list operators.

Dictionaries can be coerced from lists using as.dictionary(), coerced to named lists of characters using as.list(), and checked using is.dictionary().

Value

A dictionary class object, essentially a specially classed named list of characters.

References

WordStat dictionaries page, from Provalis Research https://provalisresearch.com/products/content-analysis-software/wordstat-dictionary/.

Pennebaker, J.W., Chung, C.K., Ireland, M., Gonzales, A., & Booth, R.J. (2007). The development and psychometric properties of LIWC2007. [Software manual]. Austin, TX (https://www.liwc.app/).

Yoshikoder page, from Will Lowe https://conjugateprior.org/software/yoshikoder/.

Lexicoder format, https://www.snsoroka.com/data-lexicoder/

See Also

as.dictionary(), as.list(), is.dictionary()

Examples

corp <- corpus_subset(data_corpus_inaugural, Year>1900)
dict <- dictionary(list(christmas = c("Christmas", "Santa", "holiday"),
                          opposition = c("Opposition", "reject", "notincorpus"),
                          taxing = "taxing",
                          taxation = "taxation",
                          taxregex = "tax*",
                          country = "america"))
tokens(corp) |>
    tokens_lookup(dictionary = dict) |>
    dfm()

# subset a dictionary
dict[1:2]
dict[c("christmas", "opposition")]
dict[["opposition"]]

# combine dictionaries
c(dict["christmas"], dict["country"])

## Not run: 
dfmat <- dfm(tokens(data_corpus_inaugural))

# import the Laver-Garry dictionary from Provalis Research
dictfile <- tempfile()
download.file("https://provalisresearch.com/Download/LaverGarry.zip",
              dictfile, mode = "wb")
unzip(dictfile, exdir = (td <- tempdir()))
dictlg <- dictionary(file = paste(td, "LaverGarry.cat", sep = "/"))
dfm_lookup(dfmat, dictlg)

# import a LIWC formatted dictionary from http://www.moralfoundations.org
download.file("http://bit.ly/37cV95h", tf <- tempfile())
dictliwc <- dictionary(file = tf, format = "LIWC")
dfm_lookup(dfmat, dictliwc)

## End(Not run)

Description

For a dfm object, returns a (weighted) document frequency for each term. The default is a simple count of the number of documents in which a feature occurs more than a given frequency threshold. (The default threshold is zero, meaning that any feature occurring at least once in a document will be counted.)

Usage

docfreq(
  x,
  scheme = c("count", "inverse", "inversemax", "inverseprob", "unary"),
  base = 10,
  smoothing = 0,
  k = 0,
  threshold = 0
)

Arguments

Value

a numeric vector of document frequencies for each feature

References

Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge: Cambridge University Press. https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf

Examples

dfmat1 <- dfm(tokens(data_corpus_inaugural))
docfreq(dfmat1[, 1:20])

# replication of worked example from
# https://en.wikipedia.org/wiki/Tf-idf#Example_of_tf.E2.80.93idf
dfmat2 <-
    matrix(c(1,1,2,1,0,0, 1,1,0,0,2,3),
           byrow = TRUE, nrow = 2,
           dimnames = list(docs = c("document1", "document2"),
                           features = c("this", "is", "a", "sample",
                                        "another", "example"))) |>
    as.dfm()
dfmat2
docfreq(dfmat2)
docfreq(dfmat2, scheme = "inverse")
docfreq(dfmat2, scheme = "inverse", k = 1, smoothing = 1)
docfreq(dfmat2, scheme = "unary")
docfreq(dfmat2, scheme = "inversemax")
docfreq(dfmat2, scheme = "inverseprob")

Description

Get or set the document names of a corpus, tokens, or dfm object.

Usage

docnames(x)

docnames(x) <- value

docid(x)

segid(x)

Arguments

Value

docnames returns a character vector of the document names

⁠docnames <-⁠ assigns new values to the document names of an object. docnames can only be character, so any non-character value assigned to be a docname will be coerced to mode character.

docid returns an internal variable denoting the original "docname" from which a document came. If an object has been reshaped (e.g. corpus_reshape() or segmented (e.g. corpus_segment()), docid(x) returns the original docnames but segid(x) does the serial number of those segments within the original document.

Note

docid and segid are designed primarily for developers, not for end users. In most cases, you will want docnames instead. It is, however, the default for groups, so that documents that have been previously reshaped (e.g. corpus_reshape() or segmented (e.g. corpus_segment()) will be regrouped into their original docnames when groups = docid(x).

See Also

featnames()

Examples

# get and set doument names to a corpus
corp <- data_corpus_inaugural
docnames(corp) <- char_tolower(docnames(corp))

# get and set doument names to a tokens
toks <- tokens(corp)
docnames(toks) <- char_tolower(docnames(toks))

# get and set doument names to a dfm
dfmat <- dfm(tokens(corp))
docnames(dfmat) <- char_tolower(docnames(dfmat))

# reassign the document names of the inaugural speech corpus
corp <- data_corpus_inaugural
docnames(corp) <- paste0("Speech", seq_len(ndoc(corp)))


corp <- corpus(c(textone = "This is a sentence.  Another sentence.  Yet another.",
                 textwo = "Sentence 1. Sentence 2."))
corp_sent <- corp |>
    corpus_reshape(to = "sentences")
docnames(corp_sent)

# docid
docid(corp_sent)
docid(tokens(corp_sent))
docid(dfm(tokens(corp_sent)))

# segid
segid(corp_sent)
segid(tokens(corp_sent))
segid(dfm(tokens(corp_sent)))

Description

Get or set variables associated with a document in a corpus, tokens or dfm object.

Usage

docvars(x, field = NULL)

docvars(x, field = NULL) <- value

## S3 method for class 'corpus'
x$name

## S3 replacement method for class 'corpus'
x$name <- value

## S3 method for class 'tokens'
x$name

## S3 replacement method for class 'tokens'
x$name <- value

## S3 method for class 'dfm'
x$name

## S3 replacement method for class 'dfm'
x$name <- value

Arguments

Value

docvars returns a data.frame of the document-level variables, dropping the second dimension to form a vector if a single docvar is returned.

⁠docvars<-⁠ assigns value to the named field

Accessing or assigning docvars using the $ operator

As of quanteda v2, it is possible to access and assign a docvar using the $ operator. See Examples.

Note

Reassigning document variables for a tokens or dfm object is allowed, but discouraged. A better, more reproducible workflow is to create your docvars as desired in the corpus, and let these continue to be attached "downstream" after tokenization and forming a document-feature matrix. Recognizing that in some cases, you may need to modify or add document variables to downstream objects, the assignment operator is defined for tokens or dfm objects as well. Use with caution.

Examples

# retrieving docvars from a corpus
head(docvars(data_corpus_inaugural))
tail(docvars(data_corpus_inaugural, "President"), 10)
head(data_corpus_inaugural$President)

# assigning document variables to a corpus
corp <- data_corpus_inaugural
docvars(corp, "President") <- paste("prez", 1:ndoc(corp), sep = "")
head(docvars(corp))
corp$fullname <- paste(data_corpus_inaugural$FirstName,
                       data_corpus_inaugural$President)
tail(corp$fullname)


# accessing or assigning docvars for a corpus using "$"
data_corpus_inaugural$Year
data_corpus_inaugural$century <- floor(data_corpus_inaugural$Year / 100)
data_corpus_inaugural$century

# accessing or assigning docvars for tokens using "$"
toks <- tokens(corpus_subset(data_corpus_inaugural, Year <= 1805))
toks$Year
toks$Year <- 1991:1995
toks$Year
toks$nonexistent <- TRUE
docvars(toks)

# accessing or assigning docvars for a dfm using "$"
dfmat <- dfm(toks)
dfmat$Year
dfmat$Year <- 1991:1995
dfmat$Year
dfmat$nonexistent <- TRUE
docvars(dfmat)

Description

Create a sparse feature co-occurrence matrix, measuring co-occurrences of features within a user-defined context. The context can be defined as a document or a window within a collection of documents, with an optional vector of weights applied to the co-occurrence counts.

Usage

fcm(
  x,
  context = c("document", "window"),
  count = c("frequency", "boolean", "weighted"),
  window = 5L,
  weights = NULL,
  ordered = FALSE,
  tri = TRUE,
  ...
)

Arguments

Details

The function fcm() provides a very general implementation of a "context-feature" matrix, consisting of a count of feature co-occurrence within a defined context. This context, following Momtazi et. al. (2010), can be defined as the document, sentences within documents, syntactic relationships between features (nouns within a sentence, for instance), or according to a window. When the context is a window, a weighting function is typically applied that is a function of distance from the target word (see Jurafsky and Martin 2015, Ch. 16) and ordered co-occurrence of the two features is considered (see Church & Hanks 1990).

fcm provides all of this functionality, returning a $V * V$ matrix (where $V$ is the vocabulary size, returned by nfeat()). The tri = TRUE option will only return the upper part of the matrix.

Unlike some implementations of co-occurrences, fcm counts feature co-occurrences with themselves, meaning that the diagonal will not be zero.

fcm also provides "boolean" counting within the context of "window", which differs from the counting within "document".

is.fcm(x) returns TRUE if and only if its x is an object of type fcm.

Author(s)

Kenneth Benoit (R), Haiyan Wang (R, C++), Kohei Watanabe (C++)

References

Momtazi, S., Khudanpur, S., & Klakow, D. (2010). "A comparative study of word co-occurrence for term clustering in language model-based sentence retrieval. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, Los Angeles, California, June 2010, 325-328. https://aclanthology.org/N10-1046/

Jurafsky, D. & Martin, J.H. (2018). From Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Draft of September 23, 2018 (Chapter 6, Vector Semantics). Available at https://web.stanford.edu/~jurafsky/slp3/.

Church, K. W. & P. Hanks (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22-29.

Examples

# see http://bit.ly/29b2zOA
toks1 <- tokens(c("A D A C E A D F E B A C E D"))
fcm(toks1, context = "window", window = 2)
fcm(toks1, context = "window", count = "weighted", window = 3)
fcm(toks1, context = "window", count = "weighted", window = 3,
    weights = c(3, 2, 1), ordered = TRUE, tri = FALSE)

# with multiple documents
toks2 <- tokens(c("a a a b b c", "a a c e", "a c e f g"))
fcm(toks2, context = "document", count = "frequency")
fcm(toks2, context = "document", count = "boolean")
fcm(toks2, context = "window", window = 2)

txt3 <- c("The quick brown fox jumped over the lazy dog.",
         "The dog jumped and ate the fox.")
toks3 <- tokens(char_tolower(txt3), remove_punct = TRUE)
fcm(toks3, context = "document")
fcm(toks3, context = "window", window = 3)

Description

Sorts an fcm in alphabetical order of the features.

Usage

fcm_sort(x)

Arguments

Value

A fcm object whose features have been alphabetically sorted. Differs from fcm_sort() in that this function sorts the fcm by the feature labels, not the counts of the features.

Author(s)

Kenneth Benoit

Examples

# with tri = FALSE
fcmat1 <- fcm(tokens(c("A X Y C B A", "X Y C A B B")), tri = FALSE)
rownames(fcmat1)[3] <- colnames(fcmat1)[3] <- "Z"
fcmat1
fcm_sort(fcmat1)

# with tri = TRUE
fcmat2 <- fcm(tokens(c("A X Y C B A", "X Y C A B B")), tri = TRUE)
rownames(fcmat2)[3] <- colnames(fcmat2)[3] <- "Z"
fcmat2
fcm_sort(fcmat2)

Description

For a dfm object, returns a frequency for each feature, computed across all documents in the dfm. This is equivalent to colSums(x).

Usage

featfreq(x)

Arguments

Value

a (named) numeric vector of feature frequencies

See Also

dfm_tfidf(), dfm_weight()

Examples

dfmat <- dfm(tokens(data_char_sampletext))
featfreq(dfmat)

Description

Get the features from a document-feature matrix, which are stored as the column names of the dfm object.

Usage

featnames(x)

Arguments

Value

character vector of the feature labels

Examples

dfmat <- dfm(tokens(data_corpus_inaugural))

# first 50 features (in original text order)
head(featnames(dfmat), 50)

# first 50 features alphabetically
head(sort(featnames(dfmat)), 50)

# contrast with descending total frequency order from topfeatures()
names(topfeatures(dfmat, 50))

Description

Locates a pattern within a tokens object, returning the index positions of the beginning and ending tokens in the pattern.

Usage

index(
  x,
  pattern,
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE
)

is.index(x)

Arguments

Value

a data.frame consisting of one row per pattern match, with columns for the document name, index positions from and to, and the pattern matched.

is.index returns TRUE if the object was created by index(); FALSE otherwise.

Examples

toks <- tokens(data_corpus_inaugural[1:8])
index(toks, pattern = "secure*")
index(toks, pattern = c("secure*", phrase("united states"))) |> head()

Description

Function to check if an object is a collocations object, created by quanteda.textstats::textstat_collocations().

Usage

is.collocations(x)

Arguments

Value

TRUE if the object is of class collocations, FALSE otherwise

Description

For a text or a collection of texts (in a quanteda corpus object), return a list of a keyword supplied by the user in its immediate context, identifying the source text and the word index number within the source text. (Not the line number, since the text may or may not be segmented using end-of-line delimiters.)

Usage

kwic(
  x,
  pattern,
  window = 5,
  valuetype = c("glob", "regex", "fixed"),
  separator = " ",
  case_insensitive = TRUE,
  index = NULL,
  ...
)

is.kwic(x)

## S3 method for class 'kwic'
as.data.frame(x, ...)

Arguments

Value

A kwic classed data.frame, with the document name (docname) and the token index positions (from and to, which will be the same for single-word patterns, or a sequence equal in length to the number of elements for multi-word phrases).

Note

pattern will be a keyword pattern or phrase, possibly multiple patterns, that may include punctuation. If a pattern contains whitespace, it is best to wrap it in phrase() to make this explicit. However if pattern is a collocations (see quanteda.textstats or dictionary object, then the collocations or multi-word dictionary keys will automatically be considered phrases where each whitespace-separated element matches a token in sequence.

See Also

print-methods

Examples

# single token matching
toks <- tokens(data_corpus_inaugural[1:8])
kwic(toks, pattern = "secure*", valuetype = "glob", window = 3)
kwic(toks, pattern = "secur", valuetype = "regex", window = 3)
kwic(toks, pattern = "security", valuetype = "fixed", window = 3)

# phrase matching
kwic(toks, pattern = phrase("secur* against"), window = 2)
kwic(toks, pattern = phrase("war against"), valuetype = "regex", window = 2)

# use index
idx <- index(toks, phrase("secur* against"))
kwic(toks, index = idx, window = 2)
kw <- kwic(tokens(data_corpus_inaugural[1:20]), "provident*")
is.kwic(kw)
is.kwic("Not a kwic")
is.kwic(kw[, c("pre", "post")])

toks <- tokens(data_corpus_inaugural[1:8])
kw <- kwic(toks, pattern = "secure*", valuetype = "glob", window = 3)
as.data.frame(kw)

Description

Get or set the object metadata in a corpus, tokens, dfm, or dictionary object. With the exception of dictionaries, this will be corpus-level metadata.

Usage

meta(x, field = NULL, type = c("user", "object", "system", "all"))

meta(x, field = NULL) <- value

Arguments

Value

For meta, a named list of the metadata fields in the corpus.

For ⁠meta <-⁠, the corpus with the updated user-level metadata. Only user-level metadata may be assigned.

Examples

meta(data_corpus_inaugural)
meta(data_corpus_inaugural, "source")
meta(data_corpus_inaugural, "citation") <- "Presidential Speeches Online Project (2014)."
meta(data_corpus_inaugural, "citation")

Description

Get the number of documents or features in an object.

Usage

ndoc(x)

nfeat(x)

Arguments

Value

ndoc() returns an integer count of the number of documents in an object whose texts are organized as "documents" (a corpus, dfm, or tokens/tokens_xptr object.

nfeat() returns an integer count of the number of features. It is an alias for ntype() for a dfm. This function is only defined for dfm objects because only these have "features".

See Also

ntoken(), ntype()

Examples

# number of documents
ndoc(data_corpus_inaugural)
ndoc(corpus_subset(data_corpus_inaugural, Year > 1980))
ndoc(tokens(data_corpus_inaugural))
ndoc(dfm(tokens(corpus_subset(data_corpus_inaugural, Year > 1980))))

# number of features
toks1 <- tokens(corpus_subset(data_corpus_inaugural, Year > 1980), remove_punct = FALSE)
toks2 <- tokens(corpus_subset(data_corpus_inaugural, Year > 1980), remove_punct = TRUE)
nfeat(dfm(toks1))
nfeat(dfm(toks2))

Description

Return the count of sentences in a corpus or character object.

Usage

nsentence(x)

Arguments

Value

count(s) of the total sentences per text

Note

nsentence() is now deprecated for all usages except tokens objects that have already been tokenised with tokens(x, what = "sentence"). Using it on character or corpus objects will now generate a warning.

nsentence() relies on the boundaries definitions in the stringi package (see stri_opts_brkiter). It does not count sentences correctly if the text has been transformed to lower case, and for this reason nsentence() will issue a warning if it detects all lower-cased text.

Examples

# simple example
txt <- c(text1 = "This is a sentence: second part of first sentence.",
         text2 = "A word. Repeated repeated.",
         text3 = "Mr. Jones has a PhD from the LSE.  Second sentence.")
tokens(txt, what = "sentence") |>
    nsentence()

Description

Get the count of tokens (total features) or types (unique tokens).

Usage

ntoken(x, ...)

ntype(x, ...)

Arguments

Value

ntoken() returns a named integer vector of the counts of the total tokens

ntypes() returns a named integer vector of the counts of the types (unique tokens) per document. For dfm objects, ntype() will only return the count of features that occur more than zero times in the dfm.

Examples

# simple example
txt <- c(text1 = "This is a sentence, this.", text2 = "A word. Repeated repeated.")
toks <- tokens(txt)
ntoken(toks)
ntype(toks)
ntoken(tokens_tolower(toks))  # same
ntype(tokens_tolower(toks))   # fewer types

# with some real texts
toks <- tokens(corpus_subset(data_corpus_inaugural, Year < 1806))
ntoken(tokens(toks, remove_punct = TRUE))
ntype(tokens(toks, remove_punct = TRUE))
ntoken(dfm(toks))
ntype(dfm(toks))

Description

Declares that a character expression consists of multiple patterns, separated by an element such as whitespace. This is typically used as a wrapper around pattern() to make it explicit that the pattern elements are to be used for matches to multi-word sequences, rather than individual, unordered matches to single words.

Usage

phrase(x, separator = " ")

as.phrase(x)

is.phrase(x)