{"id":13652428,"url":"https://github.com/bmabey/pyLDAvis","last_synced_at":"2025-04-23T03:30:49.460Z","repository":{"id":30146392,"uuid":"33696562","full_name":"bmabey/pyLDAvis","owner":"bmabey","description":"Python library for interactive topic model visualization. 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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 \u003chttps://en.wikipedia.org/wiki/Latent_Dirichlet_allocation\u003e`_.\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 \u003chttp://nbviewer.ipython.org/github/bmabey/pyLDAvis/blob/master/notebooks/pyLDAvis_overview.ipynb\u003e`__.\nRefer to the `documentation \u003chttps://pyLDAvis.readthedocs.org\u003e`__ for details.\n\nFor a concise explanation of the visualization see this\n`vignette \u003chttp://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf\u003e`__ 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 \u003chttps://www.youtube.com/watch?v=tGxW2BzC_DU\u0026index=4\u0026list=PLykRMO7ZuHwP5cWnbEmP_mUIVgzd5DZgH\u003e`__\n-  `Notebook and visualization used in the demo \u003chttp://nbviewer.ipython.org/github/bmabey/hacker_news_topic_modelling/blob/master/HN%20Topic%20Model%20Talk.ipynb\u003e`__\n-  `Slide deck \u003chttps://speakerdeck.com/bmabey/visualizing-topic-models\u003e`__\n\n\n`Carson Sievert \u003chttps://cpsievert.me/\u003e`__ created a video demoing the R package. The visualization is the same and so it applies equally to pyLDAvis:\n\n-  `Visualizing \u0026 Exploring the Twenty Newsgroup Data \u003chttps://www.youtube.com/watch?v=IksL96ls4o0\u003e`__\n\nMore documentation\n~~~~~~~~~~~~~~~~~~\n\nTo read about the methodology behind pyLDAvis, see `the original\npaper \u003chttp://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf\u003e`__,\nwhich was presented at the `2014 ACL Workshop on Interactive Language\nLearning, Visualization, and\nInterfaces \u003chttp://nlp.stanford.edu/events/illvi2014/\u003e`__ 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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbmabey%2FpyLDAvis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbmabey%2FpyLDAvis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbmabey%2FpyLDAvis/lists"}