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

Project description

TextBlob: Simplified Text Processing

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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.

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.


  • 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
  • Easily swap models, or create your own

Get it now

$ pip install -U textblob
$ curl | python


See more examples at the Quickstart guide.


Full documentation is available at


  • Python >= 2.6 or >= 3.3


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


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.
  • Note for Python 3 users: You may need to update your corpora, since NLTK master has reorganized its corpus system. Just run curl | python again.
  • Add 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:
  • 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/ -> text/
  • Add 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.

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