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Simple, Pythonic text processing. Sentiment analysis, part-of-speech tagging, noun phrase parsing, and more.

Project description

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TextBlob is a Python 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.

from textblob 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  # [('The', 'DT'), ('titular', 'JJ'),
#  ('threat', 'NN'), ('of', 'IN'), ...]

blob.noun_phrases  # WordList(['titular threat', 'blob',
#            'ultimate movie monster',
#            'amoeba-like mass', ...])

for sentence in blob.sentences:
# 0.060
# -0.341

TextBlob stands on the giant shoulders of NLTK and pattern, and plays nicely with both.


  • Noun phrase extraction

  • Part-of-speech tagging

  • Sentiment analysis

  • Classification (Naive Bayes, Decision Tree)

  • Tokenization (splitting text into words and sentences)

  • Word and phrase frequencies

  • Parsing

  • n-grams

  • Word inflection (pluralization and singularization) and lemmatization

  • Spelling correction

  • Add new models or languages through extensions

  • WordNet integration

Get it now

$ pip install -U textblob
$ python -m textblob.download_corpora


See more examples at the Quickstart guide.


Full documentation is available at


MIT licensed. See the bundled LICENSE file for more details.

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