Simple, Pythonic text processing. Sentiment analysis, POS tagging, noun phrase parsing, and more.
Project description
Requirements
Python >= 2.7, but not Python 3 (yet)
Installation
Just run:
$ pip install textblob && python download_corpora.py
This installs textblob and downloads the necessary NLTK models.
Best to see that everything is working by running:
$ nosetests
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
Part-of-speech and noun phrase tagging
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)
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
blob1 = TextBlob('apples')
blob2 = TextBlob('bananas')
blob1 < blob2 # True
blob1 + ' and ' + blob2 # TextBlob('apples and bananas')
Get start and end indices of sentences
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_index, sentence.end_index)) # 0, 30
Get a serialized version of the blob (a list of dicts)
blob.serialized # [{'end_index': 30,
# 'noun_phrases': ['beautiful'],
# 'raw_sentence': 'Beautiful is better than ugly.',
# 'start_index': 0,
# 'stripped_sentence': 'beautiful is better than ugly'},
Testing
Run
$ nosetests
to run all tests.
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