Simple, Pythonic text processing. Sentiment analysis, POS tagging, noun phrase parsing, and more.
Project description
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.
Release history Release notifications | RSS feed
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