=====================================
Textnets: text analysis with networks
=====================================
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/jboynyc/textnets-binder/trunk?filepath=Tutorial.ipynb
:alt: Launch on Binder
.. image:: https://github.com/jboynyc/textnets/actions/workflows/ci.yml/badge.svg
:target: https://github.com/jboynyc/textnets/actions/workflows/ci.yml
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.. image:: https://readthedocs.org/projects/textnets/badge/?version=stable
:target: https://textnets.readthedocs.io/en/stable/?badge=stable
:alt: Documentation Status
.. image:: https://joss.theoj.org/papers/10.21105/joss.02594/status.svg
:target: https://doi.org/10.21105/joss.02594
:alt: Published in Journal of Open Source Software
**textnets** represents collections of texts as networks of documents and
words. This provides novel possibilities for the visualization and analysis of
texts.
.. figure:: https://textnets.readthedocs.io/en/dev/_static/impeachment-statements.svg
:alt: Bipartite network graph
Network of U.S. Senators and words used in their official statements
following the acquittal vote in the 2020 Senate impeachment trial (`source
<https://www.jboy.space/blog/enemies-foreign-and-partisan.html>`_).
**textnets** is free software under the terms of the GNU General Public License
v3.
The ideas underlying **textnets** are presented in this paper:
Christopher A. Bail, "`Combining natural language processing and network
analysis to examine how advocacy organizations stimulate conversation on social
media`__," *Proceedings of the National Academy of Sciences of the United States
of America* 113, no. 42 (2016), 11823–11828, doi:10.1073/pnas.1607151113.
__ https://doi.org/10.1073/pnas.1607151113
Initially begun as a Python implementation of `Chris Bail's textnets package
for R`_, **textnets** now comprises several unique features for term extraction
and weighing, visualization, and analysis.
.. _`Chris Bail's textnets package for R`: https://github.com/cbail/textnets/
Features
--------
**textnets** builds on `spaCy`_, a state-of-the-art library for
natural-language processing, and `igraph`_ for network analysis. It uses the
`Leiden algorithm`_ for community detection, which is able to perform community
detection on the bipartite (word–group) network.
.. _`igraph`: http://igraph.org/python/
.. _`Leiden algorithm`: https://doi.org/10.1038/s41598-019-41695-z
.. _`spaCy`: https://spacy.io/
**textnets** is installable using the ``pip`` and ``nix`` package managers. It
requires Python 3.9 or higher.
**textnets** integrates seamlessly with Python's excellent `scientific stack`_.
That means that you can use **textnets** to analyze and visualize your data in
Jupyter notebooks!
.. _`scientific stack`: https://scientific-python.org
Read `the documentation <https://textnets.readthedocs.io>`_ to learn more about
the package's features.
Citation
--------
Using **textnets** in a scholarly publication? Please cite this paper:
.. code-block:: bibtex
@article{Boy2020,
author = {John D. Boy},
title = {textnets},
subtitle = {A {P}ython Package for Text Analysis with Networks},
journal = {Journal of Open Source Software},
volume = {5},
number = {54},
pages = {2594},
year = {2020},
doi = {10.21105/joss.02594},
}
Learn More
----------
================== =============================================
**Documentation** https://textnets.readthedocs.io/
**Repository** https://github.com/jboynyc/textnets
**Issues & Ideas** https://github.com/jboynyc/textnets/issues
**PyPI** https://pypi.org/project/textnets/
**FOSDEM ’22** https://fosdem.org/2022/schedule/event/open_research_textnets/
**DOI** `10.21105/joss.02594 <https://doi.org/10.21105/joss.02594>`_
**Archive** `10.5281/zenodo.3866676 <https://doi.org/10.5281/zenodo.3866676>`_
================== =============================================
.. image:: https://textnets.readthedocs.io/en/dev/_static/textnets-logo.svg
:alt: textnets logo
:target: https://textnets.readthedocs.io
:align: center
:width: 140
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"description": "=====================================\nTextnets: text analysis with networks\n=====================================\n\n.. image:: https://mybinder.org/badge_logo.svg\n :target: https://mybinder.org/v2/gh/jboynyc/textnets-binder/trunk?filepath=Tutorial.ipynb\n :alt: Launch on Binder\n\n.. image:: https://github.com/jboynyc/textnets/actions/workflows/ci.yml/badge.svg\n :target: https://github.com/jboynyc/textnets/actions/workflows/ci.yml\n :alt: CI Status\n\n.. image:: https://readthedocs.org/projects/textnets/badge/?version=stable\n :target: https://textnets.readthedocs.io/en/stable/?badge=stable\n :alt: Documentation Status\n\n.. image:: https://joss.theoj.org/papers/10.21105/joss.02594/status.svg\n :target: https://doi.org/10.21105/joss.02594\n :alt: Published in Journal of Open Source Software\n\n**textnets** represents collections of texts as networks of documents and\nwords. This provides novel possibilities for the visualization and analysis of\ntexts.\n\n.. figure:: https://textnets.readthedocs.io/en/dev/_static/impeachment-statements.svg\n :alt: Bipartite network graph\n\n Network of U.S. Senators and words used in their official statements\n following the acquittal vote in the 2020 Senate impeachment trial (`source\n <https://www.jboy.space/blog/enemies-foreign-and-partisan.html>`_).\n\n**textnets** is free software under the terms of the GNU General Public License\nv3.\n\nThe ideas underlying **textnets** are presented in this paper:\n\n Christopher A. Bail, \"`Combining natural language processing and network\n analysis to examine how advocacy organizations stimulate conversation on social\n media`__,\" *Proceedings of the National Academy of Sciences of the United States\n of America* 113, no. 42 (2016), 11823\u201311828, doi:10.1073/pnas.1607151113.\n\n__ https://doi.org/10.1073/pnas.1607151113\n\nInitially begun as a Python implementation of `Chris Bail's textnets package\nfor R`_, **textnets** now comprises several unique features for term extraction\nand weighing, visualization, and analysis.\n\n.. _`Chris Bail's textnets package for R`: https://github.com/cbail/textnets/\n\nFeatures\n--------\n\n**textnets** builds on `spaCy`_, a state-of-the-art library for\nnatural-language processing, and `igraph`_ for network analysis. It uses the\n`Leiden algorithm`_ for community detection, which is able to perform community\ndetection on the bipartite (word\u2013group) network.\n\n.. _`igraph`: http://igraph.org/python/\n.. _`Leiden algorithm`: https://doi.org/10.1038/s41598-019-41695-z\n.. _`spaCy`: https://spacy.io/\n\n**textnets** is installable using the ``pip`` and ``nix`` package managers. It\nrequires Python 3.9 or higher.\n\n**textnets** integrates seamlessly with Python's excellent `scientific stack`_.\nThat means that you can use **textnets** to analyze and visualize your data in\nJupyter notebooks!\n\n.. _`scientific stack`: https://scientific-python.org\n\nRead `the documentation <https://textnets.readthedocs.io>`_ to learn more about\nthe package's features.\n\nCitation\n--------\n\nUsing **textnets** in a scholarly publication? Please cite this paper:\n\n.. code-block:: bibtex\n\n @article{Boy2020,\n author = {John D. Boy},\n title = {textnets},\n subtitle = {A {P}ython Package for Text Analysis with Networks},\n journal = {Journal of Open Source Software},\n volume = {5},\n number = {54},\n pages = {2594},\n year = {2020},\n doi = {10.21105/joss.02594},\n }\n\nLearn More\n----------\n\n================== =============================================\n**Documentation** https://textnets.readthedocs.io/\n**Repository** https://github.com/jboynyc/textnets\n**Issues & Ideas** https://github.com/jboynyc/textnets/issues\n**PyPI** https://pypi.org/project/textnets/\n**FOSDEM \u201922** https://fosdem.org/2022/schedule/event/open_research_textnets/\n**DOI** `10.21105/joss.02594 <https://doi.org/10.21105/joss.02594>`_\n**Archive** `10.5281/zenodo.3866676 <https://doi.org/10.5281/zenodo.3866676>`_\n================== =============================================\n\n.. image:: https://textnets.readthedocs.io/en/dev/_static/textnets-logo.svg\n :alt: textnets logo\n :target: https://textnets.readthedocs.io\n :align: center\n :width: 140\n",
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