Quantitative Analysis of Textual Data

A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and ngrams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.

See the new, upcoming major API changes, to be incorporated into the next minor version 0.9.9 and for CRAN, in a "1.0" release.

See the Getting Started Vignette.

An R package for managing and analyzing text, by Ken Benoit and Paul Nulty.

quanteda makes it easy to manage texts in the form of a corpus, defined as a collection of texts that includes document-level variables specific to each text, as well as meta-data for documents and for the collection as a whole. quanteda includes tools to make it easy and fast to manuipulate the texts in a corpus, by performing the most common natural language processing tasks simply and quickly, such as tokenizing, stemming, or forming ngrams. quanteda's functions for tokenizing texts and forming multiple tokenized documents into a document-feature matrix are both extremely fast and extremely simple to use. quanteda can segment texts easily by words, paragraphs, sentences, or even user-supplied delimiters and tags.

Built on the text processing functions in the stringi package, which is in turn built on C++ implementation of the ICU libraries for Unicode text handling, quanteda pays special attention to fast and correct implementation of Unicode and the handling of text in any character set, following conversion internally to UTF-8.

quanteda is built for efficiency and speed, through its design around three infrastructures: the stringi package for text processing, the data.table package for indexing large documents efficiently, and the Matrix package for sparse matrix objects. If you can fit it into memory, quanteda will handle it quickly. (And eventually, we will make it possible to process objects even larger than available memory.)

quanteda is principally designed to allow users a fast and convenient method to go from a corpus of texts to a selected matrix of documents by features, after defining and selecting the documents and features. The package makes it easy to redefine documents, for instance by splitting them into sentences or paragraphs, or by tags, as well as to group them into larger documents by document variables, or to subset them based on logical conditions or combinations of document variables. The package also implements common NLP feature selection functions, such as removing stopwords and stemming in numerous languages, selecting words found in dictionaries, treating words as equivalent based on a user-defined "thesaurus", and trimming and weighting features based on document frequency, feature frequency, and related measures such as tf-idf.

Once constructed, a quanteda "dfm"" can be easily analyzed using either quanteda's built-in tools for scaling document positions, or used with a number of other text analytic tools, such as:

  • topic models (including converters for direct use with the topicmodels, LDA, and stm packages)

  • document scaling (using quanteda's own functions for the "wordfish" and "Wordscores" models, direct use with the ca package for correspondence analysis, or scaling with the austin package)

  • machine learning through a variety of other packages that take matrix or matrix-like inputs.

Additional features of quanteda include:

  • the ability to explore texts using key-words-in-context;

  • fast computation of a variety of readability indexes;

  • fast computation of a variety of lexical diversity measures;

  • quick computation of word or document association measures, for clustering or to compute similarity scores for other purposes; and

  • a comprehensive suite of descriptive statistics on text such as the number of sentences, words, characters, or syllables per document.

Planned features coming soon to quanteda are:

  • bootstrapping methods for texts that makes it easy to resample texts from pre-defined units, to facilitate computation of confidence intervals on textual statistics using techniques of non-parametric bootstrapping, but applied to the original texts as data.

  • expansion of predictive and analytic methods called through the standard interface called textmodel(). Current model types include correspondence analysis, "Wordscores", "Wordfish", and Naive Bayes.

  • Addition of settings to corpus projects, that will propogate through downstream objects.

  • Addition of a history that will propogate through downstream objects.

Acknowledgements: This research was supported by the European Research Council grant ERC-2011-StG 283794-QUANTESS.

How to Install

As of version 0.8.0, the GitHub master repository will always contain the development version of quanteda, while the CRAN version will contain the latest "stable" version. You therefore have two options for installing the package:

  1. From CRAN, using your R package installer, or simply

  2. (For the development version) From GitHub, using


    Because this compiles some C++ source code, you will need a compiler installed. If you are using a Windows platform, this means you will need also to install the Rtools software available from CRAN. If you are using OS X, you will need to to install XCode, available for free from the App Store, or if you prefer a lighter footprint set of tools, just the Xcode command line tools, using the command xcode-select --install from the Terminal.

  3. (Optional) You can install some additional corpus data from quantedaData using

#> quanteda version
#> Attaching package: 'quanteda'
#> The following object is masked from 'package:base':
#>     sample
# create a corpus from the immigration texts from UK party platforms
uk2010immigCorpus <- corpus(ukimmigTexts,
                            notes="Immigration-related sections of 2010 UK party manifestos",
#> Warning in corpus.character(ukimmigTexts, docvars = data.frame(party =
#> names(ukimmigTexts)), : Argument enc not used.
#> Corpus consisting of 9 documents and 1 docvar.
summary(uk2010immigCorpus, showmeta=TRUE)
#> Corpus consisting of 9 documents.
#>          Text Types Tokens Sentences        party
#>           BNP  1126   3330        88          BNP
#>     Coalition   144    268         4    Coalition
#>  Conservative   252    503        15 Conservative
#>        Greens   325    687        21       Greens
#>        Labour   296    703        29       Labour
#>        LibDem   257    499        14       LibDem
#>            PC    80    118         5           PC
#>           SNP    90    136         4          SNP
#>          UKIP   346    739        27         UKIP
#> Source:  /Users/kbenoit/Dropbox (Personal)/GitHub/quanteda/* on x86_64 by kbenoit
#> Created: Wed Oct 19 19:51:22 2016
#> Notes:   Immigration-related sections of 2010 UK party manifestos
# key words in context for "deport", 3 words of context
kwic(uk2010immigCorpus, "deport", 3)
#>                        contextPre keyword                contextPost
#>  [BNP, 159]        The BNP will [  deport ] all foreigners convicted
#> [BNP, 1970]                . 2. [  Deport ] all illegal immigrants  
#> [BNP, 1976] immigrants We shall [  deport ] all illegal immigrants  
#> [BNP, 2621]  Criminals We shall [  deport ] all criminal entrants
# create a dfm, removing stopwords
mydfm <- dfm(uk2010immigCorpus, ignoredFeatures=c("will", stopwords("english")))
#> Creating a dfm from a corpus ...
#>    ... lowercasing
#>    ... tokenizing
#>    ... indexing documents: 9 documents
#>    ... indexing features:
#> 1,585 feature types
#> ...
#> removed 97 features, from 175 supplied (glob) feature types
#>    ... created a 9 x 1489 sparse dfm
#>    ... complete. 
#> Elapsed time: 0.03 seconds.
dim(mydfm)              # basic dimensions of the dfm
#> [1]    9 1489
topfeatures(mydfm, 20)  # 20 top words
#> immigration     british      people      asylum     britain          uk 
#>          66          37          35          29          28          27 
#>      system  population     country         new  immigrants      ensure 
#>          27          21          20          19          17          17 
#>       shall citizenship      social    national         bnp     illegal 
#>          17          16          14          14          13          13 
#>        work     percent 
#>          13          12
plot(mydfm, min.freq = 6, random.order = FALSE)             # word cloud     

In-depth tutorials in the form of a gitbook will be available here here.

Examples for any function can also be seen using (for instance, for corpus()):


There are also some demo functions that show off some of the package capabilities, such as demo(quanteda).


quanteda 0.9.8

  • Improved the performance of selectFeatures.tokenizedTexts().
  • Improved the performance of rbind.dfm().
  • Added support for different docvars when importing multiple files using textfile(). (#147)
  • Added support for comparison dispersion plots in plot.kwic(). (#146)
  • Added a corpus constructor method for kwic objects.
  • Substantially improved the performance of convert(x, to = "stm") for dfm export, including adding an argument for meta-data (docvars, in quanteda parlance). (#209)
  • Internal rewrite of textfile(), now supports more file types, more wildcard patterns, and is far more robust generally.
  • Add support for loading external dictionary formats:
  • yoshikoder,
  • lexicoder v2 and v3 (#228)
  • Autodetect dictionary file format from file extension, so no longer require format keyword for loading dictionaries (#227)
  • Improved compatibility with rOpenSci guidelines (#218):
    • Use httr to get remote files
    • Use messages() to display messages rather than print or cat
    • Reorganise sections in README file
  • Added new punctuation argument to collocations() to provide new options for handling collocations separated by punctuation characters (#220).
  • ( Fixed an incompatibility in sequences.cpp with Solaris x86 (#257)
  • ( Fix bug in verbose output of dfm that causes misreporting of number of features (#250)
  • ( Fix a bug in selectFeatures.dfm() that ignored case_insensitive = TRUE settings (#251) correct the documentation for this function.
  • ( Fix a bug in tf(x, scheme = "propmax") that returned a wrong computation; correct the documentation for this function.
  • ( Fixed a bug in textfile() causing all texts to have the same name, for types using the "textField" argument (a single file containing multiple documents).
  • Fixed bug in phrasetotoken() where if pattern included a + for valuetype = c("glob", "fixed") it threw a regex error. #239
  • Fixed bug in textfile() where source is a remote .zip set. (#172)
  • Fixed bug in wordstem.dfm() that caused an error if supplied a dfm with a feature whose total frequency count was zero, or with a feature whose total docfreq was zero. Fixes #181.
  • Fix #214 "mysterious stemmed token" bug in wordstem.dfm(), introduced in fixing #181.
  • Fixed previously non-functional toLower = argument in dfm.tokenizedTexts().
  • Fixed some errors in the computation of a few readability formulas (#215).
  • Added filenames names to text vectors returned by textfile (#221).
  • dictionary() now works correctly when reading LIWC dictionaries where all terms belong to one key (#229).
  • `convert(x, to = "stm") now indexes the dfm components from 1, not 0 (#222).
  • Remove temporary stemmed token (#214).
  • Fixed bug in textmodel_NB() for non-"uniform" priors (#241)
  • Added warn = FALSE to the readLines() calls in textfile(), so that no warnings are issued when files are read that are missing a final EOL or that contain embedded nuls.
  • trim() now prints an output message even when no features are removed (#223)
  • We now skip some platform-dependent tests on CRAN, travis-ci and Windows.

quanteda 0.9.6

  • Improved Naive Bayes model and prediction, textmodel(x, y, method = "NB"), now works correctly on k > 2.

  • Improved tag handling for segment(x, what = "tags")

  • Added valuetype argument to segment() methods, which allows faster and more robust segmentation on large texts.

  • corpus() now converts all hyphen-like characters to simple hyphen

  • segment.corpus() now preserves all existing docvars.

  • corpus documentation now removes the description of the corpus object's structure since too many users were accessing these internal elements directly, which is strongly discouraged, as we are likely to change the corpus internals (soon and often). Repeat after me: "encapsulation".

  • Improve robustness of corpus.VCorpus() for constructing a corpus from a tm Corpus object.

  • Add UTF-8 preservation to ngrams.cpp.

  • Fix encoding issues for textfile(), improve functionality.

  • Added two data objects: Moby Dick is now available as mobydickText, without needing to access a zipped text file; encodedTextFiles.zip is now a zipped archive of different encodings of (mainly) the UN Declaration of Human Rights, for testing conversions from 8-bit encodings in different (non-Roman) languages.

  • phrasetotoken() now has a method correctly defined for corpus class objects.

  • lexdiv() now works just like readability(), and is faster (based on data.table) and the code is simpler.

  • removed quanteda::df() as a synonym for docfreq(), as this conflicted with stats::df().

  • added version information when package is attached.

  • improved rbind() and cbind() methods for dfm. Both now take any length sequence of dfms and perform better type checking.
    rbind.dfm() also knits together dfms with different features, which can be useful for information and retrieval purposes or machine learning.

  • selectFeatures(x, anyDfm) (where the second argument is a dfm) now works with a selection = "remove" option.

  • tokenize.character adds a removeURL option.

  • added a corpus method for data.frame objects, so that a corpus can be constructed directly from a data.frame. Requires the addition of a textField argument (similar to textfile).

  • added compress.dfm() to combine identically named columns or rows. #123

  • Much better phrasetotoken(), with additional methods for all combinations of corpus/character v. dictionary/character/collocations.

  • Added aweight(x, type, ...) signature where the second argument can be a named numeric vector of weights, not just a label for a type of weight. Thanks http://stackoverflow.com/questions/36815926/assigning-weights-to-different-features-in-r/36823475#36823475.

  • as.data.frame for dfms now passes ... to as.data.frame.matrix.

  • Fixed bug in predict.fitted_textmodel_NB() that caused a failure with k > 2 classes (#129)

  • Improved dfm.tokenizedTexts() performance by taking care of zero-token documents more efficiently.

  • dictionary(file = "liwc_formatted_dict.dic", format = "LIWC") now handles poorly formatted dictionary files better, such as the Moral Foundations Dictionary in the examples for ?dictionary.

  • added as.tokenizedTexts to coerce any list of characters to a tokenizedTexts object.

  • Fix bug in phrasetotoken, signature 'corpus,ANY' that was causing an infinite loop.

  • Fixed bug introduced in commit b88287f (0.9.5-26) that caused a failure in dfm() with empty (zero-token) documents. Also fixes Issue #168.

  • Fixed bug that caused dfm() to break if no features or only one feature was found.

  • Fixed bug in predict.fitted_textmodel_NB() that caused a failure with k > 2 classes (#129)

  • Fixed a false-alarm warning message in textmodel_wordfish()

  • Argument defaults for readability.corpus() now same as readability.character(). Fixes #107.

  • Fixed a bug causing LIWC format dictionary imports to fail if extra characters followed the closing % in the file header.

  • Fixed a bug in applyDictionary(x, dictionary, exclusive = FALSE) when the dictionary produced no matches at all, caused by an attempt to negative index a NULL. #115

  • Fixed #117, a bug where wordstem.tokenizedTexts() removed attributes from the object, causing a failure of dfm.tokenizedTexts().

  • Fixed #119, a bug in selectFeatures.tokenizedTexts(x, features, selection = "remove") that returned a NULL for a document's tokens when no matching pattern for removal was found.

  • Improved the behaviour of the removeHyphens option to tokenize() when what = "fasterword" or what "fastestword".

  • readability() now returns measures in order called, not function definition order.

  • textmodel(x, model = "wordfish") now removes zero-frequency documents and words prior to calling Rcpp.

  • Fixed a bug in sample.corpus() that caused an error when no docvars existed. #128

quanteda 0.9.4

  • Added presidents' first names to inaugCorpus

  • Added textmodel implementation of multinomial and Bernoulli Naive Bayes.

  • Improved documentation.

  • Added c.corpus() method for concatenating arbitarily large sets of corpus objects.

  • Default for similarity() is now margin = "documents" -- prevents overly massive results if selection = NULL.

  • Defined rowMeans() and colMeans() methods for dfm objects.

  • Enhancements to summary.character() and summary.corpus(): Added n = to summary.character(); added pass-through options to tokenize() in summary.corpus() and summary.character() methods; added toLower as an argument to both.

  • Enhancements to corpus object indexing, including [[ and [[<-.

  • Fixed a bug preventing smoother() from working.

  • Fixed a bug in segment.corpus(x, what = "tag") that was failing to recover the tag values after the first text.

  • Fix bug in plot.dfm(x, comparison = TRUE) method causing warning about rowMeans() failing.

  • Fixed an issue for mfdict <- dictionary(file = "http://ow.ly/VMRkL", format = "LIWC") causing it to fail because of the irregular combination of tabs and spaces in the dictionary file.

  • Fixed an exception thrown by wordstem.character(x) if one element of x was NA.

  • dfm() on a text or tokenized text containing an NA element now returns a row with 0 feature counts. Previously it returned a count of 1 for an NA feature.

  • Fix issue #91 removeHyphens = FALSE not working in tokenise for some multiple intra-word hyphens, such as "one-of-a-kind"

  • Fixed a bug in as.matrix.similMatrix() that caused scrambled conversion when feature sets compared were unequal, which normally occurs when setting similarity(x, n = <something>) when n < nfeature(x)

  • Fixed a bug in which a corpusSource object (from textfile()) with empty docvars prevented this argument from being supplied to corpus(corpusSourceObject, docvars = something).

  • Fixed inaccurate documentation for weight(), which previously listed unavailable options.

  • More accurate and complete documentation for tokenize().

  • traps an exception when calling wordstem.tokenizedTexts(x) where x was not word tokenized.

  • Fixed a bug in textfile() that prevented passthrough arguments in ..., such as fileEncoding = or encoding =

  • Fixed a bug in textfile() that caused exceptions with input documents containing docvars when there was only a single column of docvars (such as .csv files)

quanteda 0.9.2

  • added new methods for similarity(), including sparse matrix computation for method = "correlation" and "cosine". (More planned soon.) Also allows easy conversion to a matrix using as.matrix() on similarity lists.

  • more robust implementation of LIWC-formatted dictionary file imports

  • better implementation of tf-idf, and relative frequency weighting, especially for very large sparse matrix objects. tf(), idf(), and tfidf() now provide relative term frequency, inverse document frequency, and tf-idf directly.

  • textmodel_wordfish() now accepts an integer dispersionFloor argument to constrain the phi parameter to a minimium value (of underdispersion).

  • textfile() now takes a vector of filenames, if you wish to construct these yourself. See ?textfile examples.

  • removeFeatures() and selectFeatures.collocations() now all use a consistent interface and same underlying code, with removeFeatures() acting as a wrapper to selectFeatures().

  • convert(x, to = "stm") now about 3-4x faster because it uses index positions from the dgCMatrix to convert to the sparse matrix format expected by stm.

  • Fixed a bug in textfile() preventing encodingFrom and encodingTo from working properly.

  • Fixed a nasty bug problem in convert(x, to = "stm") that mixed up the word indexes. Thanks Felix Haass for spotting this!

  • Fixed a problem where wordstem was not working on ngram=1 tokenied objects

  • Fixed toLower(x, keepAcronyms = TRUE) that caused an error when x contained no acronyms.

  • Creating a corpus from a tm VCorpus now works if a "document" is a vector of texts rather than a single text

  • Fixed a bug in texts(x, groups = MORE THAN ONE DOCVAR) that now groups correctly on combinations of multiple groups

quanteda 0.9.0

  • trim() now accepts proportions in addition to integer thresholds. Also accepts a new sparsity argument, which works like tm's removeSparseTerms(x, sparse = ) (for those who really want to think of sparsity this way).

  • [i] and [i, j] indexing of corpus objects is now possible, for extracting texts or docvars using convenient notation. See ?corpus Details.

  • ngrams() and skipgrams() now use the same underlying function, with skip replacing the previous window argument (where a skip = window - 1). For efficiency, both are now implemented in C++.

  • tokenize() has a new argument, removeHyphens, that controls the treatment of intra-word hyphens.

  • Added new measures from readability for mean syllables per word and mean words per sentence directly.

  • wordstem now works on ngrams (tokenizedTexts and dfm objects).

  • Enhanced operation of kwic(), including the definition of a kwic class object, and a plot method for this object (produces a dispersion plot).

  • Lots more error checking of arguments passed to ... (and potentially misspecified or misspelled). Addresses Issue #62.

  • Almost all methods are now methods defined for objects, from a generic.

  • texts(x, groups = ) now allows groups to be factors, not just document variable labels. There is a new method for texts.character(x, groups = ) which is useful for supplying a factor to concatenate character objects by group.

  • corrected inaccurate printing of valuetype in verbose note of selectFeatures.dfm(). (Did not affect functionality.)

  • fixed broken quanteda.R demo, expanded demonstration code.

quanteda 0.8.6

  • removeFeatures.dfm(x, stopwords), selectFeatures.dfm(x, features), and dfm(x, ignoredFeatures) now work on objects created with ngrams. (Any ngram containing a stopword is removed.) Performance on these functions is already good but will be improved further soon.

  • selectFeatures(x, features = ) is now possible, to produce a selection of features from x identical to those in . Not only are only features kept in x that are in , but also fatures in not in x are added to x as padded zero counts. This functionality can also be accessed via dfm(x, keptFeatures = ). This is useful when new data used in a test set needs to have identical features as a training set dfm constructed at an earlier stage.

  • head.dfm() and tail.dfm() methods added.

  • kwic() has new formals and new functionality, including a completely flexible set of matching for phrases, as well as control over how the texts and matching keyword(s) are tokenized.

  • segment(x, what = "sentence"), and changeunits(x, to = "sentences") now uses tokenize(x, what = "sentence"). Annoying warning messages now gone.

  • smoother() and weight() formal "smooth" now changed to "smoothing" to avoid clashes with stats::smooth().

  • Updated corpus.VCorpus() to work with recent updates to the tm package.

  • added print method for tokenizedTexts

  • fixed signature error message caused by weight(x, "relFreq") and weight(x, "tfidf"). Both now correctly produce objects of class dfmSparse.

  • fixed bug in dfm(, keptFeatures = "whatever") that passed it through as a glob rather than a regex to selectFeatures(). Now takes a regex, as per the manual description.

  • fixed textfeatures() for type json, where now it can call jsonlite::fromJSON() on a file directly.

  • dictionary(x, format = "LIWC") now expanded to 25 categories by default, and handles entries that are listed on multiple lines in .dic files, such as those distributed with the LIWC.

quanteda 0.8.4

  • ngrams() rewritten to accept fully vectorized arguments for n and for window, thus implementing "skip-grams". Separate function skipgrams() behaves in the standard "skipgram" fashion. bigrams(), deprecated since 0.7, has been removed from the namespace.

  • corpus() no longer checks all documents for text encoding; rather, this is now based on a random sample of max()

  • wordstem.dfm() both faster and more robust when working with large objects.

  • toLower.NULL() now allows toLower() to work on texts with no words (returns NULL for NULL input)

  • textfile() now works on zip archives of *.txt files, although this may not be entirely portable.

  • fixed bug in selectFeatures() / removeFeatures() that returned zero features if no features were found matching removal pattern

  • corpus() previously removed document names, now fixed

  • non-portable \donttest{} examples now removed completely from all documentation

quanteda 0.8.2

  • 0.8.2-1: Changed R version dependency to 3.2.0 so that Mac binary would build on CRAN.

  • 0.8.2-1: sample.corpus() now samples documents from a corpus, and sample.dfm() samples documents or features from a dfm. trim() method for with nsample argument now calls sample.dfm().

  • sample.corpus() now samples documents from a corpus, and sample.dfm() samples documents or features from a dfm. trim() method for with nsample argument now calls sample.dfm().

  • tokenize improvements for what = "sentence": more robust to specifying options, and does not split sentences after common abbreviations such as "Dr.", "Prof.", etc.

  • corpus() no longer automatically converts encodings detected as non-UTF-8, as this detection is too imprecise.

  • new function scrabble() computes English Scrabble word values for any text, applying any summary numerical function.

  • dfm() now 2x faster, replacing previous data.table matching with direct construction of sparse matrix from match().
    Code is also much simpler, based on using three new functions that are also available directly:

    • new "dfm" method for removeFeatures()
    • new "dfm" method: selectFeatures() that is now how features can be added or removed from a dfm, based on vectors of regular expressions, globs, or fixed matching
    • new "dfm" method: applyDictionary() that can replace features through matching with values in key-value lists from a dictionary class objects, based on vectors of regular expressions, globs, or fixed matching for dictionary values. All functionality for applying dictionaries now takes place through applyDictionary().
  • fixed the problem that document names were getting erased in corpus() because stringi functions were removing them
  • fixed problem in tokenize(x, "character", removePunct = TRUE) that deleted texts that had no punctuation to begin with
  • fixed problem in dictionary(, format = "LIWC") causing import to fail for some LIWC dictionaries.
  • fixed problem in tokenize(x, ngrams = N) where N > length(x). Now returns NULL instead of an erroneously tokenized set of ngrams.
  • Fixed a bug in subset.corpus() related to environments that sometimes caused the method to break if nested in function environments.
  • clean() is no more.
  • addto option removed from dfm()
  • change behaviour of ignoredFeatures and removeFeatures() applied to ngrams; change behaviour of stem = TRUE applied to ngrams (in dfm())
  • create ngrams.tokenizedTexts() method, replacing current ngrams(), bigrams()

quanteda 0.8.0

The workflow is now more logical and more streamlined, with a new workflow vignette as well as a design vignette explaining the principles behind the workflow and the commands that encourage this workflow. The document also details the development plans and things remaining to be done on the project.

Newly rewritten command encoding() detects encoding for character, corpus, and corpusSource objects (created by textfile). When creating a corpus using corpus(), detection is automatic to UTF-8 if an encoding other than UTF-8, ASCII, or ISO-8859-1 is detected.

The tokenization, cleaning, lower-casing, and dfm construction functions now use the stringi package, based on the ICU library. This results not only in substantial speed improvements, but also more correctly handles Unicode characters and strings.

  • tokenize() and clean() now using stringi, resulting in much faster performance and more consistent behaviour across platforms.

  • tokenize() now works on sentences

  • summary.corpus() and summary.character() now use the new tokenization functions for counting tokens

  • dfm(x, dictionary = mydict) now uses stringi and is both more reliable and many many times faster.

  • phrasetotoken() now using stringi.

  • removeFeatures() now using stringi and fixed binary matches on tokenized texts

  • textfile has a new option, cache = FALSE, for not writing the data to a temporary file, but rather storing the object in memory if that is preferred.

  • language() is removed. (See Encoding... section above for changes to encoding().)

  • new object encodedTexts contains some encoded character objects for testing.

  • ie2010Corpus now has UTF-8 encoded texts (previously was unicode escaped for non-ASCII characters)

  • texts() and docvars() methods added for corpusSource objects.

  • new methods for tokenizedTexts objects: dfm(), removeFeatures(), and syllables()

  • syllables() is now much faster, using matching through stringi and merging using data.table.

  • added readability() to compute (fast!) readability indexes on a text or corpus

  • tokenize() now creates ngrams of any length, with two new arguments: ngrams = and concatenator = "_". The new arguments to tokenize() can be passed through from dfm().

  • fixed a problem in textfile() causing it to fail on Windows machines when loading *.txt

  • nsentence() was not counting sentences correctly if the text was lower-cased - now issues an error if no upper-case characters are detected. This was also causing readability() to fail.

quanteda 0.7.3

  • added an ntoken() method for dfm objects.

  • fixed a bug wherein convert(anydfm, to="tm") created a DocumentTermMatrix, not a TermDocumentMatrix. Now correctly creates a TermDocumentMatrix. (Both worked previously in topicmodels::LDA() so many users may not notice the change.)

quanteda 0.7.2

  • phrasetotokens works with dictionaries and collocations, to transform multi-word expressions into single tokens in texts or corpora

  • dictionaries now redefined as S4 classes

  • improvements to collocations(), now does not include tokens that are separated by punctuation

  • created tokenizeOnly*() functions, for testing tokenizing separately from cleaning, and a cleanC(), where both new separate functions are implemented in C

  • tokenize() now has a new option, cpp=TRUE, to use a C++ tokenizer and cleaner, resulting in much faster text tokenization and cleaning, including that used in dfm()

  • textmodel_wordfish now implemented entirely in C for speed. No std errors yet but coming soon. No predict method currently working either.

  • ie2010Corpus, and exampleString now moved into quanteda (formerly were only in quantedaData because of non-ASCII characters in each - solved with native2ascii and \uXXXX encodings).

  • All dependencies, even conditional, to the quantedaData and austin packages have been removed.

quanteda 0.7.1

Many major changes to the syntax in this version.

  • trimdfm, flatten.dictionary, the textfile functions, dictionary converters are all gone from the NAMESPACE

  • formals changed a bit in clean(), kwic().

  • compoundWords() -> phrasetotoken()

  • Cleaned up minor issues in documentation.

  • countSyllables data object renamed to englishSyllables.Rdata, and function renamed to syllables().

  • stopwordsGet() changed to stopwords(). stopwordsRemove() changed to removeFeatures().

  • new dictionary() constructor function that also does import and conversion, replacing old readWStatdict and readLIWCdict functions.

  • one function to read in text files, called textsource, that does the work for different file types based on the filename extension, and works also for wildcard expressions (that can link to directories for example)

quanteda 0.7.0

  • dfm now sparse by default, implemented as subclasses of the Matrix package. Option dfm(..., matrixType="sparse") is now the default, although matrixType="dense" will still produce the old S3-class dfm based on a regular matrix, and all dfm methods will still work with this object.

  • Improvements to: weight(), print() for dfms.

  • New methods for dfms: docfreq(), weight(), summary(), as.matrix(), as.data.frame.

quanteda 0.6.6

  • No more depends, all done through imports. Passes clean check. The start of our reliance more on the master branch rather than having merges from dev to master happen only once in a blue moon.

  • bigrams in dfm() when bigrams=TRUE and ignoredFeatures= now removed if any bigram contains an ignoredFeature

  • stopwordsRemove() now defined for sparse dfms and for collocations.

  • stopwordsRemove() now requires an explicit stopwords= argument, to emphasize the user's responsibility for applying stopwords.

quanteda 0.6.5

  • New engine for dfm now implemented as standard, using data.table and Matrix for fast, efficient (sparse) matrixes.

  • Added trigram collocations (n=3) to collocations().

  • Improvements to clean(): Minor fixes to clean() so that removeDigits=TRUE removes €10bn entirely and not just the €10. clean() now removes http and https URLs by default, although does not preserve them (yet). clean also handles numbers better, to remove 1,000,000 and 3.14159 if removeDigits=TRUE but not crazy8 or 4sure.

  • dfm works for documents that contain no features, including for dictionary counts. Thanks to Kevin Munger for catching this.

quanteda 0.6.4

  • first cut at REST APIs for Twitter and Facebook

  • some minor improvements to sentence segmentation

  • improvements to package dependencies and imports - but this is ongoing!

  • Added more functions to dfms, getting there...

  • Added the ability to segment a corpus on tags (e.g. ##TAG1 text text, ##TAG2) and have the document split using the tags as a delimiter and the tag then added to the corpus as a docvar.

quanteda 0.6.3

  • added textmodel_lda support, including LDA, CTM, and STM. Added a converter dfm2stmformat() between dfm and stm's input format.

  • as.dfm works now for data.frame objects

  • added Arabic to list of stopwords. (Still working on a stemmer for Arabic.)

quanteda 0.6.2

  • The first appearance of dfms(), to create a sparse Matrix using the Matrix package. Eventually this will become the default format for all but small dfms. Not only is this far more efficient, it is also much faster.

  • Minor speed gains for clean() -- but still much more work to be done with clean().

quanteda 0.6.1

  • started textmodel_wordfish, textmodel_ca. textmodel_wordfish takes an mcmc argument that calls JAGS wordfish.

  • now depends on ca, austin rather than importing them

  • dfm subsetting with [,] now works

  • docnames()[], []<-, docvars()[] and []<- now work correctly

quanteda 0.6.0

  • Added textmodel for scaling and prediction methods, including for starters, wordscores and naivebayes class models. LIKELY TO BE BUGGY AND QUIRKY FOR A WHILE.

  • Added smoothdfm() and weight() methods for dfms.

  • Fixed a bug in segmentSentence().

quanteda 0.5.8

  • New dfm methods for fitmodel(), predict(), and specific model fitting and prediction methods called by these, for classification and scaling of different "textmodel" types, such as wordscores and Naive Bayes (for starters).

quanteda 0.5.7

  • added compoundWords() to turn space-delimited phrases into single "tokens". Works with dfm(, dictionary=) if the text has been pre-processed with compoundWords() and the dictionary joins phrases with the connector ("_"). May add this functionality to be more automatic in future versions.

  • new keep argument for trimdfm() now takes a regular expression for which feature labels to retain. New defaults for minDoc and minCount (1 each).

  • added nfeature() method for dfm objects.

  • thesaurus: works to record equivalency classes as lists of words or regular expressions for a given key/label.

  • keep: regular expression pattern match for features to keep

quanteda 0.5.6

  • added readLIWCdict() to read LIWC-formatted dictionaries

  • fixed a "bug"/feature in readWStatDict() that eliminated wildcards (and all other punctuation marks) - now only converts to lower.

  • improved clean() functions to better handle Twitter, punctuation, and removing extra whitespace

quanteda 0.5.5

  • fixed broken dictionary option in dfm()

  • fixed a bug in dfm() that was preventing clean() options from being passed through

  • added Dice and point-wise mutual information as association measures for collocations()

  • added: similarity() to implement similarity measures for documents or features as vector representations

  • begun: implementing dfm resample methods, but this will need more time to work.
    (Solution: a three way table where the third dim is the resampled text.)

  • added is.resample() for dfm and corpus objects

  • added Twitter functions: getTweets() performs a REST search through twitteR, corpus.twitter creates a corpus object with test and docvars form each tweet (operational but needs work)

  • added various resample functions, including making dfm a multi-dimensional object when created from a resampled corpus and dfm(, bootstrap=TRUE).

  • modified the print.dfm() method.

quanteda 0.5.4

  • updated corpus.directory to allow specification of the file extension mask

  • updated docvars<- and metadoc<- to take the docvar names from the assigned data.frame if field is omitted.

  • added field to docvars()

  • enc argument in corpus() methods now actually converts from enc to "UTF-8"

  • started working on clean to give it exceptions for @ # _ for twitter text and to allow preservation of underscores used in bigrams/collocations.

  • Added: a + method for corpus objects, to combine a corpus using this operator.

  • Changed and fixed: collocations(), which was not only fatally slow and inefficient, but also wrong. Now is much faster and O(n) because it uses data.table and vector operations only.

  • Added: resample() for corpus texts.

quanteda 0.5.3

  • added statLexdiv() to compute the lexical diversity of texts from a dfm.

  • minor bug fixes; update to print.corpus() output messages.

  • added a wrapper function for SnowballC::wordStem, called wordstem(), so that this can be imported without loading the whole package.

quanteda 0.5.2

  • Added a corpus constructor method for the VCorpus class object from the tm package.

  • added zipfiles() to unzip a directory of text files from disk or a URL, for easy import into a corpus using corpus.directory(zipfiles())

quanteda 0.5.1

  • Fixed all the remaining issues causing warnings in R CMD CHECK, now all are fixed.
    Mostly these related to documentation.

  • Fixed corpus.directory to better implementing naming of docvars, if found.

  • Moved twitter.R to the R_NEEDFIXING until it can be made to pass tests. Apparently setup_twitter_oauth() is deprecated in the latest version of the twitteR package.

quanteda 0.5.0

  • plot.dfm method for producing word clouds from dfm objects

  • print.dfm, print.corpus, and summary.corpus methods now defined

  • new accessor functions defined, such as docnames(), settings(), docvars(), metadoc(), metacorpus(), encoding(), and language()

  • replacement functions defined that correspond to most of the above accessor functions, e.g. encoding(mycorpus) <- "UTF-8"

  • segment(x, to=c("tokens", "sentences", "paragraphs", "other", ...) now provides an easy and powerful method for segmenting a corpus by units other than just tokens

  • a settings() function has been added to manage settings that would commonly govern how texts are converted for processing, so that these can be preserved in a corpus and applied to operations that are relevant. These settings also propagate to a dfm for both replication purposes and to govern operations for which they would be relevant, when applied to a dfm.

  • better ways now exist to manage corpus internals, such as through the accessor functions, rather than trying to access the internal structure of the corpus directly.

  • basic functions such as tokenize(), clean(), etc are now faster, neater, and operate generally on vectors and return consistent object types

  • the corpus object has been redesigned with more flexible components, including a settings list, better corpus-level metadata, and smarter implementation of document-level attributes including user-defined variables (docvars) and document- level meta-data (metadoc)

  • the dfm now has a proper class definition, including additional attributes that hold the settings used to produce the dfm.

  • all important functions are now defined as methods for classes of built-in (e.g. character) objects, or quanteda objects such as a corpus or dfm. Lots of functions operate on both, for instance dfm.corpus(x) and dfm.character(x).

  • all functions are now documented and have working examples

  • quanteda.pdf provides a pdf version of the function documentation in one easy-to-access document

Reference manual

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0.99.22 by Kenneth Benoit, 11 days ago


Report a bug at https://github.com/kbenoit/quanteda/issues

Browse source code at https://github.com/cran/quanteda

Authors: Kenneth Benoit [aut, cre, cph], Kohei Watanabe [ctb], Paul Nulty [ctb], Adam Obeng [ctb], Haiyan Wang [ctb], Benjamin Lauderdale [ctb], Will Lowe [ctb]

Documentation:   PDF Manual  

Task views: Natural Language Processing

GPL-3 license

Imports utils, stats, Matrix, data.table, SnowballC, wordcloud, Rcpp, RcppParallel, RSpectra, stringi, fastmatch, ggplot2, XML, yaml, lubridate, magrittr, spacyr

Depends on methods

Suggests knitr, rmarkdown, lda, proxy, topicmodels, tm, slam, testthat, RColorBrewer, xtable, DT, ca, purrr

Linking to Rcpp, RcppParallel, RcppArmadillo

System requirements: C++11

Imported by clustRcompaR, gofastr, politeness, preText, sentometrics, stm, textstem.

Depended on by word.alignment.

Suggested by corpustools, phrasemachine, readtext, tidytext.

Enhanced by corpus.

See at CRAN