Metadata-Version: 1.1
Name: textblob-de
Version: 0.2.3
Summary: German language support for TextBlob.
Home-page: https://github.com/markuskiller/textblob-de
Author: Markus Killer
Author-email: m.killer@langui.ch
License: 

Copyright 2014 Markus Killer

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of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Description: ===========
        textblob-de
        ===========
        
        .. image:: https://badge.fury.io/py/textblob-de.png
            :target: http://badge.fury.io/py/textblob-de
            :alt: Latest version
        
        .. image:: https://travis-ci.org/markuskiller/textblob-de.png?branch=master
            :target: https://travis-ci.org/markuskiller/textblob-de
            :alt: Travis-CI
        
        .. image:: https://pypip.in/d/textblob-de/badge.png
            :target: https://crate.io/packages/textblob-de/
            :alt: Number of PyPI downloads
        
        
        German language support for `TextBlob <https://textblob.readthedocs.org/>`_ by Steven Loria.
        
        This python package is being developed as a ``TextBlob`` **Language Extension**.
        See `Extension Guidelines <https://textblob.readthedocs.org/en/dev/contributing.html>`_ for details.
        
        
        Features
        --------
        
        * All directly accessible ``textblob_de`` classes (e.g. ``Sentence()`` or ``Word()``) are now initialized with default models for German
        * Properties or methods that do not yet work for German now raise a ``NotImplementedError``
        * German sentence boundary detection and tokenization (``NLTKPunktTokenizer``)
        * Consistent use of specified tokenizer for all tools (``NLTKPunktTokenizer`` or ``PatternTokenizer``)
        * Part-of-speech tagging (``PatternTagger``) with keyword ``include_punc=True`` (defaults to ``False``)
        * Parsing (``PatternParser``) with keyword ``lemmata=True`` (defaults to ``False``)
        * Noun Phrase Extraction (``PatternParserNPExtractor``)
        * Lemmatization (``PatternParserLemmatizer``)
        * Polarity detection (``PatternAnalyzer``) - Still **EXPERIMENTAL**, does not yet have information on subjectivity
        * Supports Python 2 and 3
        * See `working features overview <http://langui.ch/nlp/python/textblob-de/>`_ for details
        
        
        Installing/Upgrading
        --------------------
        ::
        
            $ pip install -U textblob-de
            $ python -m textblob.download_corpora
            
        Or the latest development release (apparently this does not always work on Windows see 
        `issues #1744/5 <https://github.com/pypa/pip/pull/1745>`_ for details)::
        
            $ pip install -U git+https://github.com/markuskiller/textblob-de.git@dev
            $ python -m textblob.download_corpora
        
        
        .. note::
        
           ``TextBlob`` will be installed/upgraded automatically when running 
           ``pip install``. The second line (``python -m textblob.download_corpora``) 
           downloads/updates nltk corpora and language models used in ``TextBlob``.
        
        
        Usage
        -----
        
        
        .. code-block:: python
        
            >>> from textblob_de import TextBlobDE as TextBlob
            >>> text = '''Heute ist der 3. Mai 2014 und Dr. Meier feiert seinen 43. Geburtstag. 
            Ich muss unbedingt daran denken, Mehl, usw. für einen Kuchen einzukaufen. Aber leider 
            habe ich nur noch EUR 18.50 in meiner Brieftasche.'''
            >>> blob = TextBlob(text)
            >>> blob.sentences
            [Sentence("Heute ist der 3. Mai 2014 und Dr. Meier feiert seinen 43. Geburtstag."),
             Sentence("Ich muss unbedingt daran denken, Mehl, usw. für einen Kuchen einzukaufen."),
             Sentence("Aber leider habe ich nur noch EUR 18.50 in meiner Brieftasche.")]
            >>> blob.tokens
            WordList(['Heute', 'ist', 'der', '3.', 'Mai', ...]
            >>> blob.tags
            [('Heute', 'RB'), ('ist', 'VB'), ('der', 'DT'), ('3.', 'LS'), ('Mai', 'NN'), 
            ('2014', 'CD'), ...]
            # not perfect, but a start (relies heavily on parser accuracy)
            >>> blob.noun_phrases
            WordList(['Mai 2014', 'Dr. Meier', 'seinen 43. Geburtstag', 'Kuchen einzukaufen', 
            'meiner Brieftasche'])
            
        
        .. code-block:: python
        
            >>> blob = TextBlob("Das Auto ist sehr schön.")
            >>> blob.parse()
            'Das/DT/B-NP/O Auto/NN/I-NP/O ist/VB/B-VP/O sehr/RB/B-ADJP/O schön/JJ/I-ADJP/O'
            >>> from textblob_de import PatternParser
            >>> blob = TextBlob(text, parser=PatternParser(lemmata=True))
            'Das/DT/B-NP/O/das Auto/NN/I-NP/O/auto ist/VB/B-VP/O/sein sehr/RB/B-ADJP/O/sehr' \ 
            'schön/JJ/I-ADJP/O/schön ././O/O/.'
            >>> from textblob_de import PatternTagger
            >>> blob = TextBlob(text, pos_tagger=PatternTagger(include_punc=True))
            [('Das', 'DT'), ('Auto', 'NN'), ('ist', 'VB'), ('sehr', 'RB'), ('schön', 'JJ'), ('.', '.')]
        
        
        .. code-block:: python
            
            >>> blob = TextBlob("Das Auto ist sehr schön.")
            >>> blob.sentiment
            (1.0, 0.0)
            >>> blob = TextBlob("Das ist ein hässliches Auto.")     
            >>> blob.sentiment
            (-1.0, 0.0)
        
        
        .. warning::
        
            **WORK IN PROGRESS:** The German polarity lexicon contains only uninflected
            forms and there are no subjectivity scores yet. As of version 0.2.3, lemmatized
            word forms are submitted to the ``PatternAnalyzer``, increasing the accuracy
            of polarity values.
        
        
        .. code-block:: python
        
            >>> blob.words.lemmatize()
            WordList(['das', 'sein', 'ein', 'hässlich', 'Auto'])
            >>> from textblob_de.lemmatizers import PatternParserLemmatizer
            >>> _lemmatizer = PatternParserLemmatizer()
            >>> _lemmatizer.lemmatize("Das ist ein hässliches Auto.")
            [('das', 'DT'), ('sein', 'VB'), ('ein', 'DT'), ('hässlich', 'JJ'), ('Auto', 'NN')]
        
        
        .. note::
        
            Make sure that you use unicode strings on Python2 if your input contains
            non-ascii characters (e.g. ``word = u"schön"``).
        
        
        Requirements
        ------------
        
        - Python >= 2.6 or >= 3.3
        
        TODO
        ----
        
        - **TextBlob Extension:** ``textblob-cmd`` (command-line wrapper for ``TextBlob``, basically TextBlob for files 
        - **TextBlob Extension:** ``textblob-rftagger`` (wrapper class for ``RFTagger``)
        - **TextBlob Extension:** ``textblob-stanfordparser`` (wrapper class for ``StanfordParser`` via NLTK)
        - **TextBlob Extension:** ``textblob-berkeleyparser`` (wrapper class for ``BerkeleyParser``)
        - **TextBlob Extension:** ``textblob-sent-align`` (sentence alignment for parallel TextBlobs)
        - **TextBlob Extension:** ``textblob-converters`` (various input and output conversions)
        - Additional PoS tagging options, e.g. NLTK tagging (``NLTKTagger``)
        - Improve noun phrase extraction (e.g. based on ``RFTagger`` output)
        - Improve sentiment analysis (find suitable subjectivity scores)
        - Improve functionality of ``Sentence()`` and ``Word()`` objects
        - Adapt more tests from ``textblob`` main package (esp. for ``TextBlobDE()`` in ``test_blob.py``)
        
        License
        -------
        
        MIT licensed. See the bundled ``LICENSE``  file for more details.
        
        
        Changelog
        ---------
        
        0.2.3 (26/07/2014)
        ++++++++++++++++++
        
        * Lemmatizer: ``PatternParserLemmatizer()`` extracts lemmas from Parser output
        * Improved polarity analysis through look-up of lemmatised word forms
        
        0.2.2 (22/07/2014)
        ++++++++++++++++++
        
        * Option: Include punctuation in ``tags``/``pos_tags`` properties (``b = TextBlobDE(text, tagger=PatternTagger(include_punc=True))``)
        * Added ``BlobberDE()`` class initialized with German models
        * ``TextBlobDE()``, ``Sentence()``, ``WordList()`` and ``Word()`` classes are now all initialized with German models
        * Restored complete API compatibility with ``textblob.tokenizers`` module of ``textblob`` main package
        
        0.2.1 (20/07/2014)
        ++++++++++++++++++
        
        * Noun Phrase Extraction: ``PatternParserNPExtractor()`` extracts NPs from Parser output
        * Refactored the way ``TextBlobDE()`` passes on arguments and keyword arguments to individual tools
        * *Backwards-incompatible*: Deprecate ``parser_show_lemmata=True`` keyword in ``TextBlob()``. Use ``parser=PatternParser(lemmata=True)`` instead.
        
        0.2.0 (18/07/2014)
        ++++++++++++++++++
        
        * vastly improved tokenization (``NLTKPunktTokenizer`` and ``PatternTokenizer`` with tests)
        * consistent use of specified tokenizer for all tools
        * ``TextBlobDE`` with initialized default models for German
        * Parsing (``PatternParser``) plus ``test_parsers.py``
        * **EXPERIMENTAL** implementation of Polarity detection (``PatternAnalyzer``)
        * first attempt at extracting German Polarity clues into ``de-sentiment.xml``
        * tox tests passing for py26, py27, py33 and py34
        
        0.1.3 (09/07/2014)
        ++++++++++++++++++
        
        * First release on PyPI
        
        0.1.0 - 0.1.2 (09/07/2014)
        ++++++++++++++++++++++++++
        
        * First release on github
        * A number of experimental releases for testing purposes
        * Adapted version badges, tests & travis-ci config
        * Code adapted from sample extension `textblob-fr <https://github.com/sloria/textblob-fr>`_
        * Language specific linguistic resources copied from `pattern-de <https://github.com/clips/pattern/tree/master/pattern/text/de>`_
        
Keywords: textblob_de
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: German
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing
Classifier: Topic :: Text Processing :: Linguistic
