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