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        "description": "# topic-modelling-tools\nTopic Modelling with Latent Dirichlet Allocation using Gibbs sampling\n\nby Stephen Hansen, stephen.hansen@economics.ox.ac.uk\nAssociate Professor of Economics, University of Oxford\n\nPython/Cython code for cleaning text and estimating LDA via collapsed Gibbs sampling as in Griffiths and Steyvers (2004).\n\nTutorial scripts and notebooks making use of this library, along with some\nexample data, can be found in:\nhttps://github.com/sekhansen/text-mining-tutorial\n\n\n## Installation instructions\n\nIf you already have Python and pip installed, `pip install topic-modelling-tools` should work.  The package depends on some other python libraries such as\nnumpy and nltk but this should be taken care of by pip.\n\nThe only other requirement is that a C++ compiler is needed to build the Cython\ncode.  For Mac OS X you can download Xcode, while for Windows you can download\nthe Visual Studio C++ compiler.\n\n\n\n",
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