textblob 0.4.0

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

TextBlob: Simplified Text Processing

Homepage: https://textblob.readthedocs.org/

TextBlob is a Python (2 and 3) library for processing textual data. It provides a consistent API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, and more.

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.pos_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(blob.sentiment)  # returns (sentiment, subjectivity)
# (0.060, 0.605)
# (-0.34, 0.77)

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.

Documentation

Hosted here at ReadTheDocs.

Requirements

• Python >= 2.6 or >= 3.3

Testing

Run

python run_tests.py

to run all tests.

License

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

Changelog

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.