pyLDAvis
========
Python library for interactive topic model visualization.
This is a port of the fabulous `R package <https://github.com/cpsievert/LDAvis>`_ by `Carson Sievert <https://cpsievert.me/>`__ and `Kenny Shirley <http://www.kennyshirley.com/>`__.
.. figure:: http://www.kennyshirley.com/figures/ldavis-pic.png
:alt: LDAvis icon
**pyLDAvis** is designed to help users interpret the topics in a topic model that has been fit to a corpus of text data. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization.
The visualization is intended to be used within an IPython notebook but can also be saved to a stand-alone HTML file for easy sharing.
Note: LDA stands for `latent Dirichlet allocation <https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation>`_.
|version status| |build status| |docs|
Installation
~~~~~~~~~~~~~~~~~~~~~~
- Stable version using pip:
::
pip install pyldavis
- Development version on GitHub
Clone the repository and run ``python setup.py``
Usage
~~~~~~~~~~~~~~~~~~~~~~
The best way to learn how to use **pyLDAvis** is to see it in action.
Check out this `notebook for an overview <http://nbviewer.ipython.org/github/bmabey/pyLDAvis/blob/master/notebooks/pyLDAvis_overview.ipynb>`__.
Refer to the `documentation <https://pyLDAvis.readthedocs.org>`__ for details.
For a concise explanation of the visualization see this
`vignette <http://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf>`__ from the LDAvis R package.
Video demos
~~~~~~~~~~~
Ben Mabey walked through the visualization in this short talk using a Hacker News corpus:
- `Visualizing Topic Models <https://www.youtube.com/watch?v=tGxW2BzC_DU&index=4&list=PLykRMO7ZuHwP5cWnbEmP_mUIVgzd5DZgH>`__
- `Notebook and visualization used in the demo <http://nbviewer.ipython.org/github/bmabey/hacker_news_topic_modelling/blob/master/HN%20Topic%20Model%20Talk.ipynb>`__
- `Slide deck <https://speakerdeck.com/bmabey/visualizing-topic-models>`__
`Carson Sievert <https://cpsievert.me/>`__ created a video demoing the R package. The visualization is the same and so it applies equally to pyLDAvis:
- `Visualizing & Exploring the Twenty Newsgroup Data <http://stat-graphics.org/movies/ldavis.html>`__
More documentation
~~~~~~~~~~~~~~~~~~
To read about the methodology behind pyLDAvis, see `the original
paper <http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf>`__,
which was presented at the `2014 ACL Workshop on Interactive Language
Learning, Visualization, and
Interfaces <http://nlp.stanford.edu/events/illvi2014/>`__ in Baltimore
on June 27, 2014.
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:target: https://pyLDAvis.readthedocs.org
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"description": "pyLDAvis\n========\n\nPython library for interactive topic model visualization.\nThis is a port of the fabulous `R package <https://github.com/cpsievert/LDAvis>`_ by `Carson Sievert <https://cpsievert.me/>`__ and `Kenny Shirley <http://www.kennyshirley.com/>`__.\n\n.. figure:: http://www.kennyshirley.com/figures/ldavis-pic.png\n :alt: LDAvis icon\n\n**pyLDAvis** is designed to help users interpret the topics in a topic model that has been fit to a corpus of text data. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization.\n\nThe visualization is intended to be used within an IPython notebook but can also be saved to a stand-alone HTML file for easy sharing.\n\nNote: LDA stands for `latent Dirichlet allocation <https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation>`_.\n\n|version status| |build status| |docs|\n\nInstallation\n~~~~~~~~~~~~~~~~~~~~~~\n\n- Stable version using pip:\n\n::\n\n pip install pyldavis\n\n- Development version on GitHub\n\nClone the repository and run ``python setup.py``\n\nUsage\n~~~~~~~~~~~~~~~~~~~~~~\n\nThe best way to learn how to use **pyLDAvis** is to see it in action.\nCheck out this `notebook for an overview <http://nbviewer.ipython.org/github/bmabey/pyLDAvis/blob/master/notebooks/pyLDAvis_overview.ipynb>`__.\nRefer to the `documentation <https://pyLDAvis.readthedocs.org>`__ for details.\n\nFor a concise explanation of the visualization see this\n`vignette <http://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf>`__ from the LDAvis R package.\n\nVideo demos\n~~~~~~~~~~~\n\nBen Mabey walked through the visualization in this short talk using a Hacker News corpus:\n\n- `Visualizing Topic Models <https://www.youtube.com/watch?v=tGxW2BzC_DU&index=4&list=PLykRMO7ZuHwP5cWnbEmP_mUIVgzd5DZgH>`__\n- `Notebook and visualization used in the demo <http://nbviewer.ipython.org/github/bmabey/hacker_news_topic_modelling/blob/master/HN%20Topic%20Model%20Talk.ipynb>`__\n- `Slide deck <https://speakerdeck.com/bmabey/visualizing-topic-models>`__\n\n\n`Carson Sievert <https://cpsievert.me/>`__ created a video demoing the R package. The visualization is the same and so it applies equally to pyLDAvis:\n\n- `Visualizing & Exploring the Twenty Newsgroup Data <http://stat-graphics.org/movies/ldavis.html>`__\n\nMore documentation\n~~~~~~~~~~~~~~~~~~\n\nTo read about the methodology behind pyLDAvis, see `the original\npaper <http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf>`__,\nwhich was presented at the `2014 ACL Workshop on Interactive Language\nLearning, Visualization, and\nInterfaces <http://nlp.stanford.edu/events/illvi2014/>`__ in Baltimore\non June 27, 2014.\n\n\n\n\n.. |version status| image:: https://img.shields.io/pypi/v/pyLDAvis.svg\n :target: https://pypi.python.org/pypi/pyLDAvis\n.. |build status| image:: https://travis-ci.org/bmabey/pyLDAvis.png?branch=master\n :target: https://travis-ci.org/bmabey/pyLDAvis\n.. |docs| image:: https://readthedocs.org/projects/pyldavis/badge/?version=latest\n :target: https://pyLDAvis.readthedocs.org\n",
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