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Simple, Pythonic text processing. Sentiment analysis, POS tagging, noun phrase parsing, and more.

Project description

TextBlob

Travis-CI Number of PyPI downloads

Simplified text processing for Python 2 and 3.

Requirements

  • Python >= 2.6 or >= 3.1

Installation

TextBlob’s only external dependency is PyYAML. A vendorized version of NLTK is bundled internally.

If you have pip:

pip install textblob

Or (if you must):

easy_install textblob

IMPORTANT: TextBlob depends on some NLTK corpora to work. The easiest way to get these is to run this command:

curl https://raw.github.com/sloria/TextBlob/master/download_corpora.py | python

You can also download the script here . Then run:

python download_corpora.py

Usage

Simple.

Create a TextBlob

from text.blob import TextBlob

wikitext = '''
Python is a widely used general-purpose, high-level programming language.
Its design philosophy emphasizes code readability, and its syntax allows
programmers to express concepts in fewer lines of code than would be
possible in languages such as C.
'''

wiki = TextBlob(wikitext)

Part-of-speech tags and noun phrases…

…are just properties.

wiki.pos_tags       # [(Word('Python'), 'NNP'), (Word('is'), 'VBZ'),
                    #  (Word('widely'), 'RB')...]

wiki.noun_phrases   # WordList(['python', 'design philosophy',  'code readability'])

Sentiment analysis

The sentiment property returns a tuple of the form (polarity, subjectivity) where polarity ranges from -1.0 to 1.0 and subjectivity ranges from 0.0 to 1.0.

blob.sentiment        # (0.20, 0.58)

Tokenization

zen = TextBlob("Beautiful is better than ugly. "
                "Explicit is better than implicit. "
                "Simple is better than complex.")

zen.words            # WordList(['Beautiful', 'is', 'better'...])

zen.sentences        # [Sentence('Beautiful is better than ugly.'),
                      #  Sentence('Explicit is better than implicit.'),
                      #  ...]

Words and inflection

Each word in TextBlob.words or Sentence.words is a Word object (a subclass of unicode) with useful methods, e.g. for word inflection.

sentence = TextBlob('Use 4 spaces per indentation level.')
sentence.words
# OUT: WordList(['Use', '4', 'spaces', 'per', 'indentation', 'level'])
sentence.words[2].singularize()
# OUT: 'space'
sentence.words[-1].pluralize()
# OUT: 'levels'

Get word and noun phrase frequencies

wiki.word_counts['its']   # 2 (not case-sensitive by default)
wiki.words.count('its')   # Same thing
wiki.words.count('its', case_sensitive=True)  # 1

wiki.noun_phrases.count('code readability')  # 1

TextBlobs are like Python strings!

zen[0:19]            # TextBlob("Beautiful is better")
zen.upper()          # TextBlob("BEAUTIFUL IS BETTER THAN UGLY...")
zen.find("Simple")   # 65

apple_blob = TextBlob('apples')
banana_blob = TextBlob('bananas')
apple_blob < banana_blob           # True
apple_blob + ' and ' + banana_blob # TextBlob('apples and bananas')
"{0} and {1}".format(apple_blob, banana_blob)  # 'apples and bananas'

Get start and end indices of sentences

Use sentence.start and sentence.end. This can be useful for sentence highlighting, for example.

for sentence in zen.sentences:
    print(sentence)  # Beautiful is better than ugly
    print("---- Starts at index {}, Ends at index {}"\
                .format(sentence.start, sentence.end))  # 0, 30

Get a JSON-serialized version of the blob

zen.json   # '[{"sentiment": [0.2166666666666667, ' '0.8333333333333334],
            # "stripped": "beautiful is better than ugly", '
            # '"noun_phrases": ["beautiful"], "raw": "Beautiful is better than ugly. ", '
            # '"end_index": 30, "start_index": 0}
            #  ...]'

Overriding the noun phrase extractor

TextBlob currently has two noun phrases chunker implementations, text.np_extractor.FastNPExtractor (default, based on Shlomi Babluki’s implementation from this blog post) and text.np_extractor.ConllExtractor (currently working on Python 2 only).

You can change the chunker implementation (or even use your own) by overriding TextBlob.np_extractor

from text.np_extractor import ConllExtractor
extractor = ConllExtractor()
blob = TextBlob("Python is a widely used general-purpose, high-level programming language.")
blob.np_extractor = extractor
blob.noun_phrases  # This will use the Conll2000 noun phrase extractor

Testing

Run

nosetests

to run all tests.

License

TextBlob is licenced under the MIT license. See the bundled LICENSE file for more details.

Changelog for textblob

0.3.7 (2013-07-29)

  • 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.

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