{ "info": { "author": "Shay Palachy", "author_email": "shay.palachy@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules" ], "description": "speks\n#####\n|PyPI-Status| |PyPI-Versions| |Build-Status| |Codecov| |LICENCE|\n\nA packaged version, adapted to Python 3, of the `TwitterGenderPredictor code by JT Wolohan `_, which itself is a Python 2 implementation of Sap et al.'s gender prediction algorithm for Twitter. SPEKS stands for Sap, Park, Eichstaedt, Kern and Stilwell, the first five writers of the `paper describing the algorithm `_ implemented here. \n\n.. code-block:: python\n\n >>> from speks import predict_gender_by_tweets\n >>> gender = predict_gender_by_tweets(\" \".join([\"Please Do.\", \"Join me in praying!\"]))\n\n\n.. contents::\n\n.. section-numbering::\n\n\nFeatures\n========\n\n* Supports Python 3.\n* ``pip``-installable.\n* Fully tested.\n\n\nInstallation\n============\n\n.. code-block:: bash\n\n pip install speks\n\n\nUse\n===\n\nThis is a Python 3, packaged version of the `TwitterGenderPredictor code by JT Wolohan `_, which itself is a Python 2 implementation of Sap et al.'s gender prediction algorithm for Twitter. The algorithm should be 90% accurate given a large sample of users and a reasonable amount of data for each user.\n\n\nYou can have the package predict the gender of a Twitter user by providing the ``predict_gender_by_tweets`` function with a string containing tweets contents.\n\n.. code-block:: python\n\n >>> from speks import predict_gender_by_tweets\n >>> gender = predict_gender_by_tweets(\" \".join([\"No touchy\", \"Trial by fire\"]))\n\n\nLicensing\n=========\n\nMost of the code was released by `JT Wolohan`_ under the `MPL 2.0 license `_, and thus I'm releasing my additions under the same license. However, the tokenization code - although slightly adapted - was originally written by `Christopher Potts`_ and released under the `CC BY-NC-SA 3.0 license `_, and thus remains released under this license.\n\n\nContributing\n============\n\nCurrent package maintainer and author is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.\n\nInstalling for development\n----------------------------\n\nClone:\n\n.. code-block:: bash\n\n git clone git@github.com:shaypal5/speks.git\n\n\nInstall in development mode, including test dependencies:\n\n.. code-block:: bash\n\n cd speks\n pip install -e '.[test]'\n\n\n\nRunning the tests\n-----------------\n\nTo run the tests use:\n\n.. code-block:: bash\n\n cd speks\n pytest\n\n\nAdding documentation\n--------------------\n\nThe project is documented using the `numpy docstring conventions`_, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, follow `these conventions`_.\n\n.. _`numpy docstring conventions`: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt\n.. _`these conventions`: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt\n\nAdditionally, if you update this ``README.rst`` file, use ``python setup.py checkdocs`` to validate it compiles.\n\n\nCredits\n=======\n\nAlgorithm by `Sap et al `_. Original code by `JT Wolohan`_, with tokenization code by `Christopher Potts`_. Packaging and Python 3 adaptation by `Shay Palachy `_.\n\nOriginal paper reference:\n*Sap, M., Park, G., Eichstaedt, J., Kern, M., Stillwell, D., Kosinski, M., ... & Schwartz, H. A. (2014). Developing age and gender predictive lexica over social media. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1146-1151).*\n\n\n.. _`JT Wolohan`: https://github.com/jtwool \n.. _`Christopher Potts`: https://web.stanford.edu/~cgpotts/\n\n\n.. |PyPI-Status| image:: https://img.shields.io/pypi/v/speks.svg\n :target: https://pypi.org/project/speks\n\n.. |PyPI-Versions| image:: https://img.shields.io/pypi/pyversions/speks.svg\n :target: https://pypi.org/project/speks\n\n.. |Build-Status| image:: https://travis-ci.org/shaypal5/speks.svg?branch=master\n :target: https://travis-ci.org/shaypal5/speks\n\n.. |LICENCE| image:: https://img.shields.io/badge/License-MIT-yellow.svg\n :target: https://pypi.python.org/pypi/pdpipe\n\n.. |Codecov| image:: https://codecov.io/github/shaypal5/speks/coverage.svg?branch=master\n :target: https://codecov.io/github/shaypal5/speks?branch=master\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/shaypal5/speks", "keywords": "", "license": "MPL2.0", "maintainer": "", "maintainer_email": "", "name": "speks", "package_url": "https://pypi.org/project/speks/", "platform": "", "project_url": "https://pypi.org/project/speks/", "project_urls": { "Homepage": "https://github.com/shaypal5/speks" }, "release_url": "https://pypi.org/project/speks/0.0.1/", "requires_dist": [ "pytest; extra == 'test'", "coverage; extra == 'test'", "pytest-cov; extra == 'test'", "collective.checkdocs; extra == 'test'", "pygments; extra == 'test'" ], "requires_python": ">=3.4", "summary": "Text-based gender prediction for Twitter.", "version": "0.0.1" }, "last_serial": 4214828, "releases": { "0.0.1": [ { "comment_text": "", "digests": { "md5": "db8d65e5b3d2c5baadeca8bf1245c15c", "sha256": "c0ee5c518214a9123dd176a49469cce8f783d12c12732e41c90c0380d1c45a90" }, "downloads": -1, "filename": "speks-0.0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "db8d65e5b3d2c5baadeca8bf1245c15c", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.4", "size": 97622, "upload_time": "2018-08-28T13:18:19", "url": "https://files.pythonhosted.org/packages/3b/94/fd91a8b5c5d123854a3ca7db07695b15515282f4100bc3bb6e222d334c6b/speks-0.0.1-py3-none-any.whl" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "db8d65e5b3d2c5baadeca8bf1245c15c", "sha256": "c0ee5c518214a9123dd176a49469cce8f783d12c12732e41c90c0380d1c45a90" }, "downloads": -1, "filename": "speks-0.0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "db8d65e5b3d2c5baadeca8bf1245c15c", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.4", "size": 97622, "upload_time": "2018-08-28T13:18:19", "url": "https://files.pythonhosted.org/packages/3b/94/fd91a8b5c5d123854a3ca7db07695b15515282f4100bc3bb6e222d334c6b/speks-0.0.1-py3-none-any.whl" } ] }