Simplified text processing for Python 2 and 3.
- Python >= 2.6 or >= 3.3
Simple.
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
...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']
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.'),
# ...]
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
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'
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
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 download_corpora.py
script included with this distribution. You can get it here . Then run: :
python download_corpora.py
Run :
nosetests
to run all tests.
TextBlob is licenced under the MIT license. See the bundled LICENSE file for more details.