{ "info": { "author": "sylhare", "author_email": "sylhare@outlook.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 2 - Pre-Alpha", "Environment :: Other Environment", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 3.5", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Text Processing", "Topic :: Utilities" ], "description": "simple-lda\n==========\n\n|Generic badge| |PyPI version| |Build Status| |Codacy Badge|\n\nLatent Dirichlet allocation (LDA) is a topic model that generates topics\nbased on word frequency from a set of documents. LDA is particularly\nuseful for finding reasonably accurate mixtures of topics within a given\ndocument set.\n\nInstallation\n------------\n\nThe library is available on PyPI:\n\n::\n\n pip install simple-lda\n\nSources\n-------\n\n- `Edwin Chen Introduction to latent Dirichlet\n Allocation `__\n- `Jordan Barder LDA with Python\n walkthrough `__\n- `Gensim library for lda based on M Hoffman's\n paper `__\n- `Snowball for Porter stemming\n algorithm `__\n- `Bird, Steven, Edward Loper and Ewan Klein (2009), Natural Language\n Processing with Python. 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