Metadata-Version: 1.1
Name: pyLDAvis
Version: 3.3.0
Summary: Interactive topic model visualization. Port of the R package.
Home-page: https://github.com/bmabey/pyLDAvis
Author: Ben Mabey
Author-email: ben@benmabey.com
License: MIT
Download-URL: https://github.com/bmabey/pyLDAvis/tarball/3.3.0
Description: 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.
        
        
        
        
        .. |version status| image:: https://img.shields.io/pypi/v/pyLDAvis.svg
           :target: https://pypi.python.org/pypi/pyLDAvis
        .. |build status| image:: https://travis-ci.org/bmabey/pyLDAvis.png?branch=master
           :target: https://travis-ci.org/bmabey/pyLDAvis
        .. |docs| image:: https://readthedocs.org/projects/pyldavis/badge/?version=latest
           :target: https://pyLDAvis.readthedocs.org
        
Keywords: data science,visualization
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
