quanteda 0.5.0 Unreleased

Lots of new functions

  • 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.

Old functions vastly improved

  • 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

Better object and class design

  • 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).

more complete documentation

  • 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

quanteda 0.5.1 Unreleased

  • 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 0.5.2 Unreleased

  • 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 0.5.3 Unreleased

  • 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 0.5.4 Unreleased

  • 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 0.5.5 Unreleased

  • 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 0.5.6 Unreleased

  • 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 0.5.7 Unreleased

  • 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.

New arguments for dfm()

  • 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 0.5.8 Unreleased

Classification and scaling methods

  • 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).