textblob 0.6.2

Simple, Pythonic text processing. Sentiment analysis, POS tagging, noun phrase parsing, and more.

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

0.6.2 (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.