Simple, Pythonic text processing. Sentiment analysis, POS tagging, noun phrase parsing, and more.
Project description
Simplified text processing for Python 2 and 3.
Requirements
Python >= 2.6 or >= 3.3
Usage
Simple.
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
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)
Tokenization
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}
# ...]'
Installation
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
Testing
Run
nosetests
to run all tests.
License
TextBlob is licenced under the MIT license. See the bundled LICENSE file for more details.
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