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

Project description

Simplified text processing for Python 2 and 3.


  • Python >= 2.6 or >= 3.3



Create a TextBlob

from text.blob import TextBlob

zen = """Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

blob = TextBlob(zen) # Create a new TextBlob

Part-of-speech tags and noun phrases…

…are just properties.

blob.pos_tags         # [('beautiful', 'JJ'), ('is', 'VBZ'), ('better', 'RBR'),
                      # ('than', 'IN'), ('ugly', 'RB'), ...]

blob.noun_phrases     # ['beautiful', 'explicit', 'simple', 'complex', 'flat',
                      # 'sparse', 'readability', 'special cases',
                      # 'practicality beats purity', 'errors', 'unless',
                      # 'obvious way','dutch', 'right now', 'bad idea',
                      # 'good idea', 'namespaces', 'great idea']

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)


blob.words            # WordList(['Beautiful', 'is', 'better'...'more',
                      #           'of', 'those'])

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

Get word and noun phrase frequencies

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

blob.noun_phrases.count('great idea')  # 1

TextBlobs are like Python strings!

blob[0:19]            # TextBlob("Beautiful is better")
blob.upper()          # TextBlob("BEAUTIFUL IS BETTER THAN UGLY...")
blob.find("purity")   # 293

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 blob.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

blob.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}
            #  ...]'


If you have pip:

pip install textblob

Or (if you must):

easy_install textblob

IMPORTANT: TextBlob depends on some NLTK models to work. The easiest way to get these is to run the script included with this distribution. You can get it here . Then run:





to run all tests.


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

Project details

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