{ "info": { "author": "David Johnson", "author_email": "djcomlab@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Information Analysis", "Topic :: Text Processing :: General", "Topic :: Text Processing :: Linguistic" ], "description": "===========\ntextmining3\n===========\n\n\n.. image:: https://img.shields.io/pypi/v/textmining.svg\n :target: https://pypi.python.org/pypi/textmining3\n\n.. image:: https://img.shields.io/travis/djcomlab/textmining3.svg\n :target: https://travis-ci.org/djcomlab/textmining3\n\n.. image:: https://readthedocs.org/projects/textmining3/badge/?version=latest\n :target: https://textmining3.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n\n\n\nText Mining Utilities for Python 3\n\n\n* Free software: GNU General Public License v3\n* Documentation: https://textmining3.readthedocs.io.\n* Requires Python >= 3.6\n\n\nFeatures\n--------\n\nThis package contains a variety of useful functions for text mining in Python 3.\n\nIt focuses on statistical text mining (i.e. the bag-of-words model) and makes it\nvery easy to create a term-document matrix from a collection of documents. This\nmatrix can then be read into a statistical package (R, MATLAB, etc.) for further\nanalysis. The package also provides some useful utilities for finding\ncollocations (i.e. significant two-word phrases), computing the edit distance\nbetween words, and chunking long documents up into smaller pieces.\n\nThe package has a large amount of curated data (stopwords, common names, an\nEnglish dictionary with parts of speech and word frequencies) which allows the\nuser to extract fairly sophisticated features from a document.\n\nThis package does NOT have any natural language processing capabilities such as\npart-of-speech tagging. Please see the Python NLTK for that sort of\nfunctionality (plus much, much more).\n\nThe original code and documentation is available in PyPI under the package name\n`textmining`. This package is a port to Python 3 and published in PyPI under the package\nname textmining3, and is based on the original.\n\n\nCredits\n-------\nThe original `textmining` 1.0 package code was authored by Christian Peccei \n\n.. _`textmining`: https://pypi.org/project/textmining/1.0/\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n1.1.0 (2018-13-19)\n------------------\n* Add new feature to export DTM to pandas.DataFrame\n\n1.0.2 (2018-12-19)\n------------------\n* First port of textmining to Python 3\n\n1.0.0 (2010-01-11)\n------------------\n\n* Original release of textmining on PyPI (see https://pypi.org/project/textmining/1.0/)\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/djcomlab/textmining3", "keywords": "textmining3", "license": "GNU General Public License v3", "maintainer": "", "maintainer_email": "", "name": "textmining3", "package_url": "https://pypi.org/project/textmining3/", "platform": "", "project_url": "https://pypi.org/project/textmining3/", "project_urls": { "Homepage": "https://github.com/djcomlab/textmining3" }, "release_url": "https://pypi.org/project/textmining3/1.1.0/", "requires_dist": [ "Click (>=6.0)", "pandas (>=0.23.4); 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