Metadata-Version: 1.1
Name: textblob
Version: 0.7.0
Summary: Simple, Pythonic text processing. Sentiment analysis, POS tagging, noun phrase parsing, and more.
Home-page: https://github.com/sloria/TextBlob
Author: Steven Loria
Author-email: sloria1@gmail.com
License: Copyright 2013 Steven Loria

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Description: 
        TextBlob: Simplified Text Processing
        ====================================
        
        .. image:: https://badge.fury.io/py/textblob.png
            :target: http://badge.fury.io/py/textblob
            :alt: Latest version
        
        .. image:: https://travis-ci.org/sloria/TextBlob.png?branch=master
            :target: https://travis-ci.org/sloria/TextBlob
            :alt: Travis-CI
        
        .. image:: https://pypip.in/d/textblob/badge.png
            :target: https://crate.io/packages/textblob/
            :alt: Number of PyPI downloads
        
        .. image:: https://badge.waffle.io/sloria/TextBlob.png?label=Ready
             :target: https://waffle.io/sloria/TextBlob
             :alt: Issues in Ready
        
        
        Homepage: `https://textblob.readthedocs.org/ <https://textblob.readthedocs.org/>`_
        
        `TextBlob` is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.
        
        
        .. code-block:: python
        
            from text.blob import TextBlob
        
            text = '''
            The titular threat of The Blob has always struck me as the ultimate movie
            monster: an insatiably hungry, amoeba-like mass able to penetrate
            virtually any safeguard, capable of--as a doomed doctor chillingly
            describes it--"assimilating flesh on contact.
            Snide comparisons to gelatin be damned, it's a concept with the most
            devastating of potential consequences, not unlike the grey goo scenario
            proposed by technological theorists fearful of
            artificial intelligence run rampant.
            '''
        
            blob = TextBlob(text)
            blob.tags           # [(u'The', u'DT'), (u'titular', u'JJ'),
                                #  (u'threat', u'NN'), (u'of', u'IN'), ...]
        
            blob.noun_phrases   # WordList(['titular threat', 'blob',
                                #            'ultimate movie monster',
                                #            'amoeba-like mass', ...])
        
            for sentence in blob.sentences:
                print(sentence.sentiment)  # returns (polarity, subjectivity)
            # (0.060, 0.605)
            # (-0.341, 0.767)
        
            blob.translate(to="es")  # 'La amenaza titular de The Blob...'
        
        TextBlob stands on the giant shoulders of `NLTK`_ and `pattern`_, and plays nicely with both.
        
        Features
        --------
        
        - Noun phrase extraction
        - Part-of-speech tagging
        - Sentiment analysis
        - Classification (Naive Bayes, Decision Tree)
        - Language translation and detection powered by Google Translate
        - Tokenization (splitting text into words and sentences)
        - Word and phrase frequencies
        - Parsing
        - `n`-grams
        - Word inflection (pluralization and singularization) and lemmatization
        - Spelling correction
        - JSON serialization
        - Add new models or languages through extensions
        - WordNet integration
        
        Get it now
        ----------
        ::
        
            $ pip install -U textblob
            $ curl https://raw.github.com/sloria/TextBlob/master/download_corpora.py | python
        
        Examples
        --------
        
        See more examples at the `Quickstart guide`_.
        
        .. _`Quickstart guide`: https://textblob.readthedocs.org/en/latest/quickstart.html#quickstart
        
        
        Documentation
        -------------
        
        Full documentation is available at https://textblob.readthedocs.org/.
        
        Requirements
        ------------
        
        - Python >= 2.6 or >= 3.3
        
        
        License
        -------
        
        MIT licensed. See the bundled `LICENSE <https://github.com/sloria/TextBlob/blob/master/LICENSE>`_ file for more details.
        
        .. _pattern: http://www.clips.ua.ac.be/pattern
        .. _NLTK: http://nltk.org/
        
        
        Changelog
        =========
        
        0.7.0 (2013-09-25)
        ------------------
        - Wordnet integration. ``Word`` objects have ``synsets`` and ``definitions`` properties. The ``text.wordnet`` module allows you to create ``Synset`` and ``Lemma`` objects directly.
        - Move all English-specific code to its own module, ``text.en``.
        - Basic extensions framework in place. TextBlob has been refactored to make it easier to develop extensions.
        - Add ``text.classifiers.PositiveNaiveBayesClassifier``.
        - Update NLTK.
        - Fix ``__str__`` behavior. ``print(blob)`` should now print non-ascii text correctly in both Python 2 and 3.
        - *Backwards-incompatible*: All abstract base classes have been moved to the ``text.base`` module.
        - *Backwards-incompatible*: ``PerceptronTagger`` will now be maintained as an extension, ``textblob-aptagger``. Instantiating a ``text.taggers.PerceptronTagger()`` will raise a ``DeprecationWarning``.
        
        0.6.3 (2013-09-15)
        ------------------
        - Word tokenization fix: Words that stem from a contraction will still have an apostrophe, e.g. ``"Let's" => ["Let", "'s"]``.
        - Fix bug with comparing blobs to strings.
        - Add ``text.taggers.PerceptronTagger``, a fast and accurate POS tagger. Thanks `@syllog1sm <http://github.com/syllog1sm>`_.
        - Note for Python 3 users: You may need to update your corpora, since NLTK master has reorganized its corpus system. Just run ``curl https://raw.github.com/sloria/TextBlob/master/download_corpora.py | python`` again.
        - Add ``download_corpora_lite.py`` script for getting the minimum corpora requirements for TextBlob's basic features.
        
        0.6.2 (2013-09-05)
        ------------------
        - Fix bug that resulted in a ``UnicodeEncodeError`` when tagging text with non-ascii characters.
        - Add ``DecisionTreeClassifier``.
        - Add ``labels()`` and ``train()`` methods to classifiers.
        
        0.6.1 (2013-09-01)
        ------------------
        - Classifiers can be trained and tested on CSV, JSON, or TSV data.
        - Add basic WordNet lemmatization via the ``Word.lemma`` property.
        - ``WordList.pluralize()`` and ``WordList.singularize()`` methods return ``WordList`` objects.
        
        0.6.0 (2013-08-25)
        ------------------
        - Add Naive Bayes classification. New ``text.classifiers`` module, ``TextBlob.classify()``, and ``Sentence.classify()`` methods.
        - Add parsing functionality via the ``TextBlob.parse()`` method. The ``text.parsers`` module currently has one implementation (``PatternParser``).
        - Add spelling correction. This includes the ``TextBlob.correct()`` and ``Word.spellcheck()`` methods.
        - Update NLTK.
        - Backwards incompatible: ``clean_html`` has been deprecated, just as it has in NLTK. Use Beautiful Soup's ``soup.get_text()`` method for HTML-cleaning instead.
        - Slight API change to language translation: if ``from_lang`` isn't specified, attempts to detect the language.
        - Add ``itokenize()`` method to tokenizers that returns a generator instead of a list of tokens.
        
        0.5.3 (2013-08-21)
        ------------------
        - Unicode fixes: This fixes a bug that sometimes raised a ``UnicodeEncodeError`` upon creating accessing ``sentences`` for TextBlobs with non-ascii characters.
        - Update NLTK
        
        0.5.2 (2013-08-14)
        ------------------
        - `Important patch update for NLTK users`: Fix bug with importing TextBlob if local NLTK is installed.
        - Fix bug with computing start and end indices of sentences.
        
        
        0.5.1 (2013-08-13)
        ------------------
        - Fix bug that disallowed display of non-ascii characters in the Python REPL.
        - Backwards incompatible: Restore ``blob.json`` property for backwards compatibility with textblob<=0.3.10. Add a ``to_json()`` method that takes the same arguments as ``json.dumps``.
        - Add ``WordList.append`` and ``WordList.extend`` methods that append Word objects.
        
        0.5.0 (2013-08-10)
        ------------------
        - Language translation and detection API!
        - Add ``text.sentiments`` module. Contains the ``PatternAnalyzer`` (default implementation) as well as a ``NaiveBayesAnalyzer``.
        - Part-of-speech tags can be accessed via ``TextBlob.tags`` or ``TextBlob.pos_tags``.
        - Add ``polarity`` and ``subjectivity`` helper properties.
        
        0.4.0 (2013-08-05)
        ------------------
        - New ``text.tokenizers`` module with ``WordTokenizer`` and ``SentenceTokenizer``. Tokenizer instances (from either textblob itself or NLTK) can be passed to TextBlob's constructor. Tokens are accessed through the new ``tokens`` property.
        - New ``Blobber`` class for creating TextBlobs that share the same tagger, tokenizer, and np_extractor.
        - Add ``ngrams`` method.
        - `Backwards-incompatible`: ``TextBlob.json()`` is now a method, not a property. This allows you to pass arguments (the same that you would pass to ``json.dumps()``).
        - New home for documentation: https://textblob.readthedocs.org/
        - Add parameter for cleaning HTML markup from text.
        - Minor improvement to word tokenization.
        - Updated NLTK.
        - Fix bug with adding blobs to bytestrings.
        
        0.3.10 (2013-08-02)
        -------------------
        - Bundled NLTK no longer overrides local installation.
        - Fix sentiment analysis of text with non-ascii characters.
        
        0.3.9 (2013-07-31)
        ------------------
        - Updated nltk.
        - ConllExtractor is now Python 3-compatible.
        - Improved sentiment analysis.
        - Blobs are equal (with `==`) to their string counterparts.
        - Added instructions to install textblob without nltk bundled.
        - Dropping official 3.1 and 3.2 support.
        
        0.3.8 (2013-07-30)
        ------------------
        - Importing TextBlob is now **much faster**. This is because the noun phrase parsers are trained only on the first call to ``noun_phrases`` (instead of training them every time you import TextBlob).
        - Add text.taggers module which allows user to change which POS tagger implementation to use. Currently supports PatternTagger and NLTKTagger (NLTKTagger only works with Python 2).
        - NPExtractor and Tagger objects can be passed to TextBlob's constructor.
        - Fix bug with POS-tagger not tagging one-letter words.
        - Rename text/np_extractor.py -> text/np_extractors.py
        - Add run_tests.py script.
        
        0.3.7 (2013-07-28)
        ------------------
        
        - Every word in a ``Blob`` or ``Sentence`` is a ``Word`` instance which has methods for inflection, e.g ``word.pluralize()`` and ``word.singularize()``.
        
        - Updated the ``np_extractor`` module. Now has an new implementation, ``ConllExtractor`` that uses the Conll2000 chunking corpus. Only works on Py2.
        
Keywords: textblob,nlp
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
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 :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Text Processing :: Linguistic
