quanteda/quanteda

quanteda is an R package for managing and analyzing text, created and maintained by Kenneth Benoit and Kohei Watanabe. Its creation was funded by the European Research Council grant ERC-2011-StG 283794-QUANTESS and its continued development is supported by the Quanteda Initiative CIC.

For more details, see https://quanteda.io.

The quanteda 4.0 is a major release that improves functionality and performance and further improves function consistency by removing previously deprecated functions. It also includes significant new tokeniser rules that make the default tokeniser smarter than ever, with new Unicode and ICU-compliant rules enabling it to work more consistently with even more languages.

We describe more fully these significant changes in:

• an article about the new external pointer tokens objects;
• an article showing performance benchmarks for the new external pointer tokens objects, as well as some of the tokeniser improvements in v4; and
• the changelog for v4 a full listing of the changes, improvements, and deprecations in v4.

We completed the trend of splitting quanteda into modular packages with the release of v3. The quanteda family of packages includes the following:

• quanteda: contains all of the core natural language processing and textual data management functions
• quanteda.textmodels: contains all of the text models and supporting functions, namely the textmodel_*() functions. This was split from the main package with the v2 release
• quanteda.textstats: statistics for textual data, namely the textstat_*() functions, split with the v3 release
• quanteda.textplots: plots for textual data, namely the textplot_*() functions, split with the v3 release

We are working on additional package releases, available in the meantime from our GitHub pages:

• quanteda.sentiment: Functions and lexicons for sentiment analysis using dictionaries
• quanteda.tidy: Extensions for manipulating document variables in core quanteda objects using your favourite tidyverse functions

and more to come.

The normal way from CRAN, using your R GUI or

install.packages("quanteda") 

(New for quanteda v4.0) For Linux users: Because all installations on Linux are compiled, Linux users will first need to install the Intel oneAPI Threading Building Blocks for parallel computing for installation to work.

To install TBB on Linux:

# Fedora, CentOS, RHEL
sudo yum install tbb-devel

# Debian and Ubuntu
sudo apt install libtbb-dev

Windows or macOS users do not have to install TBB or any other packages to enable parallel computing when installing quanteda from CRAN.

Because this compiles some C++ and Fortran source code, you will need to have installed the appropriate compilers to build the development version.

You will also need to install TBB:

macOS:

After installing Homebrew:

brew install tbb

Windows:

Install RTools, which includes the TBB libraries.

See the quick start guide to learn how to use quanteda.

• Read out documentation at https://quanteda.io.
• Check out the quanteda cheatsheet.
• Submit a question on the quanteda channel on StackOverflow.
• See our tutorial site.

Benoit, Kenneth, Kohei Watanabe, Haiyan Wang, Paul Nulty, Adam Obeng, Stefan Müller, and Akitaka Matsuo. (2018) “quanteda: An R package for the quantitative analysis of textual data”. Journal of Open Source Software 3(30), 774. https://doi.org/10.21105/joss.00774.

For a BibTeX entry, use the output from citation(package = "quanteda").

If you like quanteda, please consider leaving feedback or a testimonial here.

Contributions in the form of feedback, comments, code, and bug reports are most welcome. How to contribute:

• Fork the source code, modify, and issue a pull request through the project GitHub page. See our Contributor Code of Conduct and the all-important quanteda Style Guide.
• Issues, bug reports, and wish lists: File a GitHub issue.
• Contact the maintainer by email.