The `quanteda` package provides a fast, flexible toolset for the management, processing, and quantitative analysis of textual data in R.
quanteda
The quanteda package, written by Kenneth Benoit and Paul Nulty, provides a fast, flexible toolset for the management, processing, and quantitative analysis of textual data in R.
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, for instance by tokenizing them, with or without stopwords or stemming, or to segment them by sentence or paragraph units.
quanteda is carefully designed to work with Unicode and UTF-8 encodings, and is based on the stringi package which in turn is based on the ICU libraries.
quanteda implements 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. quanteda includes a suite of sophisticated tools to extract features of the texts into a quantitative matrix, where these features can be defined according to a dictionary or thesaurus, including the declaration of collocations to be treated as single features.
Once converted into a quantitative matrix (known as a "dfm" for document-feature matrix), the textual feature can be analyzed using quantitative methods for describing, comparing, or scaling texts, or used to train machine learning methods for class prediction.
Resources
Source code on GitHub (including the latest version in the
devbranch)
