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>_.

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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 <https://www.youtube.com/watch?v=IksL96ls4o0>`__

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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