=====================================
Textnets: text analysis with networks
=====================================
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: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 ``conda``, ``pip`` and ``nix`` package
managers. It requires Python 3.8 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
**Conda-Forge** https://anaconda.org/conda-forge/textnets
**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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