To help you get started, we've collected the most common ways that textblob is being used within popular public projects.

sloria / TextBlob / tests / test_blob.py View on Github

('I am tired of this stuff.', 'neg'),
    ("I can't deal with this", 'neg'),
    ('He is my sworn enemy!', 'neg'),
    ('My boss is horrible.', 'neg')
]

test = [
    ('The beer was good.', 'pos'),
    ('I do not enjoy my job', 'neg'),
    ("I ain't feeling dandy today.", 'neg'),
    ("I feel amazing!", 'pos'),
    ('Gary is a friend of mine.', 'pos'),
    ("I can't believe I'm doing this.", 'neg')
]

classifier = NaiveBayesClassifier(train)

class WordListTest(TestCase):

    def setUp(self):
        self.words = 'Beautiful is better than ugly'.split()
        self.mixed = ['dog', 'dogs', 'blob', 'Blobs', 'text']

    def test_len(self):
        wl = tb.WordList(['Beautiful', 'is', 'better'])
        assert_equal(len(wl), 3)

    def test_slicing(self):
        wl = tb.WordList(self.words)
        first = wl[0]
        assert_true(isinstance(first, tb.Word))
        assert_equal(first, 'Beautiful')

View more ways to use textblob

TextBlob: Simplified Text Processing

Homepage:

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:
    print(sentence.sentiment.polarity)
# 0.060
# -0.341

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

Features

• 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

Examples

See more examples at the Quickstart guide.

Documentation

Full documentation is available at .

Project Links

• Docs:

• Changelog:

• PyPI:

• Issues:

License

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