TextBlob: Simplified Text Processing
Homepage: https://textblob.readthedocs.org/
TextBlob is a Python (2 and 3) 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 text.blob 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 # [(u'The', u'DT'), (u'titular', u'JJ'),
# (u'threat', u'NN'), (u'of', u'IN'), ...]
blob.noun_phrases # WordList(['titular threat', 'blob',
# 'ultimate movie monster',
# 'amoeba-like mass', ...])
for sentence in blob.sentences:
print(sentence.sentiment) # returns (polarity, subjectivity)
# (0.060, 0.605)
# (-0.341, 0.767)
blob.translate(to="es") # 'La amenaza titular de The Blob...'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)
Language translation and detection powered by Google Translate
Tokenization (splitting text into words and sentences)
Word and phrase frequencies
Parsing
n-grams
Word inflection (pluralization and singularization) and lemmatization
Spelling correction
JSON serialization
Easily swap models, or create your own
Get it now
$ pip install -U textblob $ curl https://raw.github.com/sloria/TextBlob/master/download_corpora.py | python
Examples
See more examples at the Quickstart guide.
Documentation
Full documentation is available at https://textblob.readthedocs.org/.
Requirements
Python >= 2.6 or >= 3.3
License
MIT licensed. See the bundled LICENSE file for more details.
Changelog
0.6.3 (2013-09-15)
Word tokenization fix: Words that stem from a contraction will still have an apostrophe, e.g. "Let's" => ["Let", "'s"].
Fix bug with comparing blobs to strings.
Add text.taggers.PerceptronTagger, a fast and accurate POS tagger. Thanks @syllog1sm.
Note for Python 3 users: You may need to update your corpora, since NLTK master has reorganized its corpus system. Just run curl https://raw.github.com/sloria/TextBlob/master/download_corpora.py | python again.
Add download_corpora_lite.py script for getting the minimum corpora requirements for TextBlob’s basic features.
0.6.2 (2013-09-05)
Fix bug that resulted in a UnicodeEncodeError when tagging text with non-ascii characters.
Add DecisionTreeClassifier.
Add labels() and train() methods to classifiers.
0.6.1 (2013-09-01)
Classifiers can be trained and tested on CSV, JSON, or TSV data.
Add basic WordNet lemmatization via the Word.lemma property.
WordList.pluralize() and WordList.singularize() methods return WordList objects.
0.6.0 (2013-08-25)
Add Naive Bayes classification. New text.classifiers module, TextBlob.classify(), and Sentence.classify() methods.
Add parsing functionality via the TextBlob.parse() method. The text.parsers module currently has one implementation (PatternParser).
Add spelling correction. This includes the TextBlob.correct() and Word.spellcheck() methods.
Update NLTK.
Backwards incompatible: clean_html has been deprecated, just as it has in NLTK. Use Beautiful Soup’s soup.get_text() method for HTML-cleaning instead.
Slight API change to language translation: if from_lang isn’t specified, attempts to detect the language.
Add itokenize() method to tokenizers that returns a generator instead of a list of tokens.
0.5.3 (2013-08-21)
Unicode fixes: This fixes a bug that sometimes raised a UnicodeEncodeError upon creating accessing sentences for TextBlobs with non-ascii characters.
Update NLTK
0.5.2 (2013-08-14)
Important patch update for NLTK users: Fix bug with importing TextBlob if local NLTK is installed.
Fix bug with computing start and end indices of sentences.
0.5.1 (2013-08-13)
Fix bug that disallowed display of non-ascii characters in the Python REPL.
Backwards incompatible: Restore blob.json property for backwards compatibility with textblob<=0.3.10. Add a to_json() method that takes the same arguments as json.dumps.
Add WordList.append and WordList.extend methods that append Word objects.
0.5.0 (2013-08-10)
Language translation and detection API!
Add text.sentiments module. Contains the PatternAnalyzer (default implementation) as well as a NaiveBayesAnalyzer.
Part-of-speech tags can be accessed via TextBlob.tags or TextBlob.pos_tags.
Add polarity and subjectivity helper properties.
0.4.0 (2013-08-05)
New text.tokenizers module with WordTokenizer and SentenceTokenizer. Tokenizer instances (from either textblob itself or NLTK) can be passed to TextBlob’s constructor. Tokens are accessed through the new tokens property.
New Blobber class for creating TextBlobs that share the same tagger, tokenizer, and np_extractor.
Add ngrams method.
Backwards-incompatible: TextBlob.json() is now a method, not a property. This allows you to pass arguments (the same that you would pass to json.dumps()).
New home for documentation: https://textblob.readthedocs.org/
Add parameter for cleaning HTML markup from text.
Minor improvement to word tokenization.
Updated NLTK.
Fix bug with adding blobs to bytestrings.
0.3.10 (2013-08-02)
Bundled NLTK no longer overrides local installation.
Fix sentiment analysis of text with non-ascii characters.
0.3.9 (2013-07-31)
Updated nltk.
ConllExtractor is now Python 3-compatible.
Improved sentiment analysis.
Blobs are equal (with ==) to their string counterparts.
Added instructions to install textblob without nltk bundled.
Dropping official 3.1 and 3.2 support.
0.3.8 (2013-07-30)
Importing TextBlob is now much faster. This is because the noun phrase parsers are trained only on the first call to noun_phrases (instead of training them every time you import TextBlob).
Add text.taggers module which allows user to change which POS tagger implementation to use. Currently supports PatternTagger and NLTKTagger (NLTKTagger only works with Python 2).
NPExtractor and Tagger objects can be passed to TextBlob’s constructor.
Fix bug with POS-tagger not tagging one-letter words.
Rename text/np_extractor.py -> text/np_extractors.py
Add run_tests.py script.
0.3.7 (2013-07-28)
Every word in a Blob or Sentence is a Word instance which has methods for inflection, e.g word.pluralize() and word.singularize().
Updated the np_extractor module. Now has an new implementation, ConllExtractor that uses the Conll2000 chunking corpus. Only works on Py2.
1 maintainer
Release files for textblob 0.6.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| textblob-0.6.3.tar.gz | 1.8 MB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| textblob-0.6.3-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details |
Total release size: 3.4 MB
Release files / textblob-0.6.3.tar.gz
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| Size | 1.8 MB |
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| Size | 1.5 MB |
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