textstat_collocations()which calls it:
all_subtuplescollocation method based on Blaheta and Johnson’s (2001) in
method = c("unigram", "all_subtuples")to apply unigram subtuples and all subtuples algorithm.
method = c(...,"bj_uni", "bj_all")to apply unigram subtuples and all subtuples algorithm.
min_sizeto allow collocation returns of size between
min_size, which is used when
method = "bj_uni"and
method = "bj_all". This function is still under development and likely to change further.
quanteda_optionsthat affect the maximum documents and features displayed by the dfm print method (#756).
sequences(): Corrected the word matching, and lambda and sigma calculation methods for
unigram subtuplesalgorithm in
sequences_mt.cpp, and consequently the p-values on the bigrams are correct.
corpus()now works for a
char_trim()functions for selecting documents or subsets of documents based on sentence, paragraph, or document lengths.
$metaof the return object.
dfm_group(x, groups = )command, a convenience wrapper around
dfm.dfm(x, groups = )(#725).
textfields, which also provides interoperability with the readtext package. corpus construction methods are now more explicitly tailored to input object classes.
dfm_lookup()behaves more robustly on different platforms, especially for keys whose values match no features (#704).
textstat_dist()no longer take the
nargument, as this was not sorting features in correct order.
tokens(x, what = "character")when
xincluded Twitter characters
ntype.dfm()produced an incorrect result.
textstat_lexdiv()for single-document returns when
drop = TRUE.
dfmare more robust (#684).
convert(x, to = "stm")caused by zero-count documents and zero-count features in a dfm (#699, #700, #701). This also removes docvar rows from
$metawhen this is passed through the dfm, for zero-count documents.
dfm_compressnow preserves a dfm’s docvars if collapsing only on the features margin, which means that
dfm_toupper()no longer remove the docvars.
fcm_compress()now retains the fcm class, and generates and error when an asymmetric compression is attempted (#728).
textstat_collocations()now returns the collocations as character, not as a factor (#736)
dfm_lookup(x, exclusive = FALSE)wherein an empty dfm ws returned with there was no no match (#116).
tokens()is now robust, and preserves variables defined in the calling environment (#721).
names(), or other indexing operations, which started happening on Linux and Windows platforms following the CRAN move to 3.4.0. (#744)
dfm_weight()now print friendlier error messages when the weight vector contains features not found in the dfm. See this Stack Overflow question for the use case that sparked this improvement.
corpus_reshape()can now go from sentences and paragraph units back to documents.
by =argument to
corpus_sample(), for use in bootstrap resampling of sub-document units.
bootstrap_dfm()to generate a list of dimensionally-equivalent dfm objects based on sentence-level resampling of the original documents.
dfm()for passing docvars through to to tokens and dfm objects, and added
metadoc()methods for tokens and dfm class objects. Overall, the code for docvars and metadoc is now more robust and consistent.
docvars()on eligible objects that contain no docvars now returns an empty 0 x 0 data.frame (in the spirit of #242).
textmodel_scale1dnow produces sorted and grouped document positions for fitted wordfish models, and produces a ggplot2 plot object.
textmodel_wordfish()now preserves sparsity while processing the dfm, and uses a fast approximation to an SVD to get starting values. This also dramatically improves performance in computing this model. (#482, #124)
kwic()is now dramatically improved, and also returns an indexed set of tokens that makes subsequent commands on a kwic class object much faster. (#603)
char_trimsentences()to remove sentences from a corpus or character object, based on token length or pattern matching.
[), or accessing values directly (
textstat_collocations(), which combines the existing
sequences()functions. (#434) Collocations now behave as sequences for other functions (such as
tokens_compound()) and have a greatly improved performance for such uses.
docvars()now permits direct access to “metadoc” fields (starting with
metadoc()now returns a vector instead of a data.frame for a single variable, similar to
verboseoptions now take the default from
getOption("verbose")rather than fixing the value in the function signatures. (#577)
textstat_simil()now return a matrix if a
selectionargument is supplied, and coercion to a list produces a list of distances or similarities only for that selection.
tokens(), the old arguments (e.g.
removePunct) still produce the same behaviour but with a deprecation warning.
textstat_keyness()to return counts for each category being compared for keyness.
str()on a corpus with no docvars (#571).
tokens()now removes URLs where the first part of the URL is a single letter (#587).
dfm_selectnow works correctly for ngram features (#589).
featureswas a dfm, that failed to produce the intended featnames matches for the output dfm.
corpus_segment(x, what = "tags")when a document contained a whitespace just before a tag, at the beginning of the file, or ended with a tag followed by no text (#618, #634).
textstat_keyness()now returns a data.frame with p-values as well as the test statistic, and rownames containing the feature. This is more consistent with the other textstat functions.
tokens_lookup()implements new rules for nested and linked sequences in dictionary values. See #502.
tokens_compound()has a new
joinargument for better handling of nested and linked sequences. See #517.
tokensare now significantly faster due to a reimplementation of the hash table functions in C++. (#510)
dfm()now works with multi-word dictionaries and thesauruses, which previously worked only with
fcm()is now parallelized for improved performance on multi-core systems.
convert(x, to = "lsa")that transposed row and column names (#526)
fcm()method for corpus objects (#538)
tokensto break on > 10,000 documents. (#438)
tokens(x, what = "character", removeSeparators = TRUE)that returned an empty string.
corpus.VCorpusif the VCorpus contains a single document. (#445)
dfm_compressin which the function failed on documents that contained zero feature counts. (#467)
textmodel_NBthat caused the class priors
Pcto be refactored alphabetically instead of in the order of assignment (#471), also affecting predicted classes (#476).
textstat_keyness()discovers words that occur at differential rates between partitions of a dfm (using chi-squared, Fisher’s exact test, and the G^2 likelihood ratio test to measure the strength of associations).
dfm()that uses this function), which will now coerce to a factor rather than requiring one.
max_length, the latter to prevent memory leaks from extremely long sequences.
dictionary()now accepts YAML as an input file format.
tokens_lookupnow accept a
levelsargument to determine which level of a hierarchical dictionary should be applied.
dictionary()can now be called on the argument of a
list()without explicitly wrapping it in
fcmnow works directly on a dfm object when
context = "documents".
This release has some major changes to the API, described below.
|new name||original name||notes|
The following objects have been renamed, but will not affect user-level functionality because they are primarily internal. Their man pages have been moved to a common ?
data-internal man page, hidden from the index, but linked from some of the functions that use them.
|new name||original name||notes|
The following functions will still work, but issue a deprecation warning:
|new function||deprecated function||contructs:|
The following are new to v0.9.9 (and not associated with deprecated functions):
|new function||description||ouput class|
||constructor for a feature co-occurrence matrix||
||selects features from an
||removes features from an
||lowercases the features of an
||uppercases the features of an
||lowercases the features of a
||uppercases the features of a
||experimental collocation detection||
||moved to the readtext package|
||deprecated several versions ago for
||moved to package readtext|
||moved to package readtext, as
to = "lsa"functionality added to
valuetypematches work for many functions.
kwicis completely rewritten, now uses fast hashed index matching in C++ and fully implements vectorized matches (#306) and all
tokens_removeare faster and use parallelization (based on the TBB library).
textstat_similadd fast, sparse, and parallel computation of many new distance and similarity matrices.
min_docfreqarguments, and better verbose output, to
tokens(), for more memory-efficient token hashing when dealing with very large numbers of documents.
joinTokens()performance for hashed
dfm.character()by using new
tokens()constructor to create hashed tokenized texts by default when creating a dfm, resulting in performance gains when constructing a dfm. Creating a dfm from a hashed
tokensobject is now 4-5 times faster than the older
applyDictionary()), that also works with dictionaries that have multi-word keys. Addresses but does not entirely yet solve #188.
sparsity()function to compute the sparsity of a dfm.
convert(x, to = "stm")for dfm export, including adding an argument for meta-data (docvars, in quanteda parlance). (#209)
textfile(), now supports more file types, more wildcard patterns, and is far more robust generally.
formatkeyword for loading dictionaries (#227)
messages()to display messages rather than
collocations()to provide new options for handling collocations separated by punctuation characters (#220).
fcm(x, tri = TRUE)temporarily created a dense logical matrix.
case_insensitive = TRUEsettings (#251) correct the documentation for this function.
tf(x, scheme = "propmax")that returned a wrong computation; correct the documentation for this function.
phrasetotoken()where if pattern included a
valuetype = c("glob", "fixed")it threw a regex error. #239
textfile()where source is a remote .zip set. (#172)
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.
wordstem.dfm(), introduced in fixing #181.
toLower =argument in
dictionary()now works correctly when reading LIWC dictionaries where all terms belong to one key (#229).
warn = FALSEto the
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)
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).
compress.dfm() to combine identically named columns or rows. #123
phrasetotoken(), with additional methods for all combinations of corpus/character v. dictionary/character/collocations.
weight(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
Fixed bug in
predict.fitted_textmodel_NB() that caused a failure with k > 2 classes (#129)
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
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
what = "fasterword" or
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
Added presidents’ first names to inaugCorpus
Added textmodel implementation of multinomial and Bernoulli Naive Bayes.
c.corpus() method for concatenating arbitarily large sets of corpus objects.
similarity() is now
margin = "documents" – prevents overly massive results if
selection = NULL.
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 inaccurate documentation for
weight(), which previously listed unavailable options.
More accurate and complete documentation for
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
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)
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
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.
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 =
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().
corpus.VCorpus() to work with recent updates to the tm package.
added print method for tokenizedTexts
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.
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.
0.8.2-1: Changed R version dependency to 3.2.0 so that Mac binary would build on CRAN.
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.
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:
subset.corpus()related to environments that sometimes caused the method to break if nested in function environments.
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.
syllables() is now much faster, using matching through
stringi and merging using
readability() to compute (fast!) readability indexes on a text or corpus
fixed a problem in
textfile() causing it to fail on Windows machines when loading
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.
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.)
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.
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)
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.
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=
stopwordsRemove() now defined for sparse dfms and for collocations.
stopwordsRemove() now requires an explicit stopwords=
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.
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.
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.)
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().
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
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().
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.
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
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.
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.
+ 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.
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.
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())
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.
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).