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textblob Documentation
Release 0.13.0
Steven Loria
Aug 16, 2017
Contents
1
Features
3
2
Get it now
5
3
Guide
3.1 License . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Installation . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Tutorial: Quickstart . . . . . . . . . . . . . . . . . . . . .
3.4 Tutorial: Building a Text Classification System . . . . . . .
3.5 Advanced Usage: Overriding Models and the Blobber Class
3.6 Extensions . . . . . . . . . . . . . . . . . . . . . . . . . .
3.7 API Reference . . . . . . . . . . . . . . . . . . . . . . . .
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Project info
4.1 Changelog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Contributing guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Python Module Index
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textblob Documentation, Release 0.13.0
Release v0.13.0. (Changelog)
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 textblob 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
# [('The', 'DT'), ('titular', 'JJ'),
# ('threat', 'NN'), ('of', 'IN'), ...]
blob.noun_phrases
# WordList(['titular threat', 'blob',
#
'ultimate movie monster',
#
'amoeba-like mass', ...])
for sentence in blob.sentences:
print(sentence.sentiment.polarity)
# 0.060
# -0.341
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.
Contents
1
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2
Contents
CHAPTER
1
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
• Add new models or languages through extensions
• WordNet integration
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textblob Documentation, Release 0.13.0
4
Chapter 1. Features
CHAPTER
2
Get it now
$ pip install -U textblob
$ python -m textblob.download_corpora
Ready to dive in? Go on to the Quickstart guide.
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6
Chapter 2. Get it now
CHAPTER
3
Guide
License
Copyright 2013-2017 Steven Loria
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Installation
Installing/Upgrading From the PyPI
$ pip install -U textblob
$ python -m textblob.download_corpora
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This will install TextBlob and download the necessary NLTK corpora. If you need to change the default download
directory set the NLTK_DATA environment variable.
Downloading the minimum corpora
If you only intend to use TextBlob’s default models (no model overrides), you can pass the lite argument. This
downloads only those corpora needed for basic functionality.
$ python -m textblob.download_corpora lite
With conda
Note: Conda builds are currently available for Mac OSX only.
TextBlob is also available as a conda package. To install with conda, run
$ conda install -c https://conda.anaconda.org/sloria textblob
$ python -m textblob.download_corpora
From Source
TextBlob is actively developed on Github.
You can clone the public repo:
$ git clone https://github.com/sloria/TextBlob.git
Or download one of the following:
• tarball
• zipball
Once you have the source, you can install it into your site-packages with
$ python setup.py install
Get the bleeding edge version
To get the latest development version of TextBlob, run
$ pip install -U git+https://github.com/sloria/[email protected]
Migrating from older versions (<=0.7.1)
As of TextBlob 0.8.0, TextBlob’s core package was renamed to textblob, whereas earlier versions used a package
called text. Therefore, migrating to newer versions should be as simple as rewriting your imports, like so:
New:
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from textblob import TextBlob, Word, Blobber
from textblob.classifiers import NaiveBayesClassifier
from textblob.taggers import NLTKTagger
Old:
from text.blob import TextBlob, Word, Blobber
from text.classifiers import NaiveBayesClassifier
from text.taggers import NLTKTagger
Python
TextBlob supports Python >=2.7 or >=3.4.
Dependencies
TextBlob depends on NLTK 3. NLTK will be installed automatically when you run pip install textblob or
python setup.py install.
Some features, such as the maximum entropy classifier, require numpy, but it is not required for basic usage.
Tutorial: Quickstart
TextBlob aims to provide access to common text-processing operations through a familiar interface. You can treat
TextBlob objects as if they were Python strings that learned how to do Natural Language Processing.
Create a TextBlob
First, the import.
>>> from textblob import TextBlob
Let’s create our first TextBlob.
>>> wiki = TextBlob("Python is a high-level, general-purpose programming language.")
Part-of-speech Tagging
Part-of-speech tags can be accessed through the tags property.
>>> wiki.tags
[('Python', 'NNP'), ('is', 'VBZ'), ('a', 'DT'), ('high-level', 'JJ'), ('general˓→purpose', 'JJ'), ('programming', 'NN'), ('language', 'NN')]
Noun Phrase Extraction
Similarly, noun phrases are accessed through the noun_phrases property.
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>>> wiki.noun_phrases
WordList(['python'])
Sentiment Analysis
The sentiment property returns a namedtuple of the form Sentiment(polarity, subjectivity). The
polarity score is a float within the range [-1.0, 1.0]. The subjectivity is a float within the range [0.0, 1.0] where 0.0 is
very objective and 1.0 is very subjective.
>>> testimonial = TextBlob("Textblob is amazingly simple to use. What great fun!")
>>> testimonial.sentiment
Sentiment(polarity=0.39166666666666666, subjectivity=0.4357142857142857)
>>> testimonial.sentiment.polarity
0.39166666666666666
Tokenization
You can break TextBlobs into words or sentences.
>>> zen = TextBlob("Beautiful is better than ugly. "
...
"Explicit is better than implicit. "
...
"Simple is better than complex.")
>>> zen.words
WordList(['Beautiful', 'is', 'better', 'than', 'ugly', 'Explicit', 'is', 'better',
˓→'than', 'implicit', 'Simple', 'is', 'better', 'than', 'complex'])
>>> zen.sentences
[Sentence("Beautiful is better than ugly."), Sentence("Explicit is better than
˓→implicit."), Sentence("Simple is better than complex.")]
Sentence objects have the same properties and methods as TextBlobs.
>>> for sentence in zen.sentences:
...
print(sentence.sentiment)
For more advanced tokenization, see the Advanced Usage guide.
Words Inflection and Lemmatization
Each word in TextBlob.words or Sentence.words is a Word object (a subclass of unicode) with useful
methods, e.g. for word inflection.
>>> sentence = TextBlob('Use 4 spaces per indentation level.')
>>> sentence.words
WordList(['Use', '4', 'spaces', 'per', 'indentation', 'level'])
>>> sentence.words[2].singularize()
'space'
>>> sentence.words[-1].pluralize()
'levels'
Words can be lemmatized by calling the lemmatize method.
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>>> from textblob import Word
>>> w = Word("octopi")
>>> w.lemmatize()
'octopus'
>>> w = Word("went")
>>> w.lemmatize("v") # Pass in part of speech (verb)
'go'
WordNet Integration
You can access the synsets for a Word via the synsets property or the get_synsets method, optionally passing
in a part of speech.
>>> from textblob import Word
>>> from textblob.wordnet import VERB
>>> word = Word("octopus")
>>> word.synsets
[Synset('octopus.n.01'), Synset('octopus.n.02')]
>>> Word("hack").get_synsets(pos=VERB)
[Synset('chop.v.05'), Synset('hack.v.02'), Synset('hack.v.03'), Synset('hack.v.04'),
˓→Synset('hack.v.05'), Synset('hack.v.06'), Synset('hack.v.07'), Synset('hack.v.08')]
You can access the definitions for each synset via the definitions property or the define() method, which can
also take an optional part-of-speech argument.
>>> Word("octopus").definitions
['tentacles of octopus prepared as food', 'bottom-living cephalopod having a soft
˓→oval body with eight long tentacles']
You can also create synsets directly.
>>> from textblob.wordnet import Synset
>>> octopus = Synset('octopus.n.02')
>>> shrimp = Synset('shrimp.n.03')
>>> octopus.path_similarity(shrimp)
0.1111111111111111
For more information on the WordNet API, see the NLTK documentation on the Wordnet Interface.
WordLists
A WordList is just a Python list with additional methods.
>>> animals = TextBlob("cat dog octopus")
>>> animals.words
WordList(['cat', 'dog', 'octopus'])
>>> animals.words.pluralize()
WordList(['cats', 'dogs', 'octopodes'])
Spelling Correction
Use the correct() method to attempt spelling correction.
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>>> b = TextBlob("I havv goood speling!")
>>> print(b.correct())
I have good spelling!
Word objects have a spellcheck() Word.spellcheck() method that returns a list of (word,
confidence) tuples with spelling suggestions.
>>> from textblob import Word
>>> w = Word('falibility')
>>> w.spellcheck()
[('fallibility', 1.0)]
Spelling correction is based on Peter Norvig’s “How to Write a Spelling Corrector”1 as implemented in the pattern
library. It is about 70% accurate2 .
Get Word and Noun Phrase Frequencies
There are two ways to get the frequency of a word or noun phrase in a TextBlob.
The first is through the word_counts dictionary.
>>> monty = TextBlob("We are no longer the Knights who say Ni. "
...
"We are now the Knights who say Ekki ekki ekki PTANG.")
>>> monty.word_counts['ekki']
3
If you access the frequencies this way, the search will not be case sensitive, and words that are not found will have a
frequency of 0.
The second way is to use the count() method.
>>> monty.words.count('ekki')
3
You can specify whether or not the search should be case-sensitive (default is False).
>>> monty.words.count('ekki', case_sensitive=True)
2
Each of these methods can also be used with noun phrases.
>>> wiki.noun_phrases.count('python')
1
Translation and Language Detection
New in version 0.5.0.
TextBlobs can be translated between languages.
>>> en_blob = TextBlob(u'Simple is better than complex.')
>>> en_blob.translate(to='es')
TextBlob("Simple es mejor que complejo.")
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2
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http://www.clips.ua.ac.be/pages/pattern-en#spelling
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If no source language is specified, TextBlob will attempt to detect the language. You can specify the source language
explicitly, like so. Raises TranslatorError if the TextBlob cannot be translated into the requested language or
NotTranslated if the translated result is the same as the input string.
>>> chinese_blob = TextBlob(u"")
>>> chinese_blob.translate(from_lang="zh-CN", to='en')
TextBlob("Beauty is better than ugly")
You can also attempt to detect a TextBlob’s language using TextBlob.detect_language().
>>> b = TextBlob(u"
")
>>> b.detect_language()
'ar'
As a reference, language codes can be found here.
Language translation and detection is powered by the Google Translate API.
Parsing
Use the parse() method to parse the text.
>>> b = TextBlob("And now for something completely different.")
>>> print(b.parse())
And/CC/O/O now/RB/B-ADVP/O for/IN/B-PP/B-PNP something/NN/B-NP/I-PNP completely/RB/B˓→ADJP/O different/JJ/I-ADJP/O ././O/O
By default, TextBlob uses pattern’s parser3 .
TextBlobs Are Like Python Strings!
You can use Python’s substring syntax.
>>> zen[0:19]
TextBlob("Beautiful is better")
You can use common string methods.
>>> zen.upper()
TextBlob("BEAUTIFUL IS BETTER THAN UGLY. EXPLICIT IS BETTER THAN IMPLICIT. SIMPLE IS
˓→BETTER THAN COMPLEX.")
>>> zen.find("Simple")
65
You can make comparisons between TextBlobs and strings.
>>> apple_blob = TextBlob('apples')
>>> banana_blob = TextBlob('bananas')
>>> apple_blob < banana_blob
True
>>> apple_blob == 'apples'
True
You can concatenate and interpolate TextBlobs and strings.
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>>> apple_blob + ' and ' + banana_blob
TextBlob("apples and bananas")
>>> "{0} and {1}".format(apple_blob, banana_blob)
'apples and bananas'
n-grams
The TextBlob.ngrams() method returns a list of tuples of n successive words.
>>> blob = TextBlob("Now is better than never.")
>>> blob.ngrams(n=3)
[WordList(['Now', 'is', 'better']), WordList(['is', 'better', 'than']), WordList([
˓→'better', 'than', 'never'])]
Get Start and End Indices of Sentences
Use sentence.start and sentence.end to get the indices where a sentence starts and ends within a
TextBlob.
>>> for s in zen.sentences:
...
print(s)
...
print("---- Starts at index {}, Ends at index {}".format(s.start, s.end))
Beautiful is better than ugly.
---- Starts at index 0, Ends at index 30
Explicit is better than implicit.
---- Starts at index 31, Ends at index 64
Simple is better than complex.
---- Starts at index 65, Ends at index 95
Next Steps
Want to build your own text classification system? Check out the Classifiers Tutorial.
Want to use a different POS tagger or noun phrase chunker implementation? Check out the Advanced Usage guide.
Tutorial: Building a Text Classification System
The textblob.classifiers module makes it simple to create custom classifiers.
As an example, let’s create a custom sentiment analyzer.
Loading Data and Creating a Classifier
First we’ll create some training and test data.
>>> train = [
...
('I love this sandwich.', 'pos'),
...
('this is an amazing place!', 'pos'),
...
('I feel very good about these beers.', 'pos'),
...
('this is my best work.', 'pos'),
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...
("what an awesome view", 'pos'),
...
('I do not like this restaurant', 'neg'),
...
('I am tired of this stuff.', 'neg'),
...
("I can't deal with this", 'neg'),
...
('he is my sworn enemy!', 'neg'),
...
('my boss is horrible.', 'neg')
... ]
>>> test = [
...
('the beer was good.', 'pos'),
...
('I do not enjoy my job', 'neg'),
...
("I ain't feeling dandy today.", 'neg'),
...
("I feel amazing!", 'pos'),
...
('Gary is a friend of mine.', 'pos'),
...
("I can't believe I'm doing this.", 'neg')
... ]
Now we’ll create a Naive Bayes classifier, passing the training data into the constructor.
>>> from textblob.classifiers import NaiveBayesClassifier
>>> cl = NaiveBayesClassifier(train)
Loading Data from Files
You can also load data from common file formats including CSV, JSON, and TSV.
CSV files should be formatted like so:
I love this sandwich.,pos
This is an amazing place!,pos
I do not like this restaurant,neg
JSON files should be formatted like so:
[
{"text": "I love this sandwich.", "label": "pos"},
{"text": "This is an amazing place!", "label": "pos"},
{"text": "I do not like this restaurant", "label": "neg"}
]
You can then pass the opened file into the constructor.
>>> with open('train.json', 'r') as fp:
...
cl = NaiveBayesClassifier(fp, format="json")
Classifying Text
Call the classify(text) method to use the classifier.
>>> cl.classify("This is an amazing library!")
'pos'
You can get the label probability distribution with the prob_classify(text) method.
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>>> prob_dist = cl.prob_classify("This one's a doozy.")
>>> prob_dist.max()
'pos'
>>> round(prob_dist.prob("pos"), 2)
0.63
>>> round(prob_dist.prob("neg"), 2)
0.37
Classifying TextBlobs
Another way to classify text is to pass a classifier into the constructor of TextBlob and call its classify()
method.
>>> from textblob import TextBlob
>>> blob = TextBlob("The beer is good. But the hangover is horrible.", classifier=cl)
>>> blob.classify()
'pos'
The advantage of this approach is that you can classify sentences within a TextBlob.
>>> for s in blob.sentences:
...
print(s)
...
print(s.classify())
...
The beer is good.
pos
But the hangover is horrible.
neg
Evaluating Classifiers
To compute the accuracy on our test set, use the accuracy(test_data) method.
>>> cl.accuracy(test)
0.8333333333333334
Note: You can also pass in a file object into the accuracy method. The file can be in any of the formats listed in
the Loading Data section.
Use the show_informative_features() method to display a listing of the most informative features.
>>> cl.show_informative_features(5)
Most Informative Features
contains(my) = True
contains(an) = False
contains(I) = True
contains(I) = False
contains(my) = False
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neg
neg
neg
pos
pos
:
:
:
:
:
pos
pos
pos
neg
neg
=
=
=
=
=
1.7
1.6
1.4
1.4
1.3
:
:
:
:
:
1.0
1.0
1.0
1.0
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Updating Classifiers with New Data
Use the update(new_data) method to update a classifier with new training data.
>>> new_data = [('She is my best friend.', 'pos'),
...
("I'm happy to have a new friend.", 'pos'),
...
("Stay thirsty, my friend.", 'pos'),
...
("He ain't from around here.", 'neg')]
>>> cl.update(new_data)
True
>>> cl.accuracy(test)
1.0
Feature Extractors
By default, the NaiveBayesClassifier uses a simple feature extractor that indicates which words in the training
set are contained in a document.
For example, the sentence “I feel happy” might have the features contains(happy):
contains(angry): False.
True or
You can override this feature extractor by writing your own. A feature extractor is simply a function with document
(the text to extract features from) as the first argument. The function may include a second argument, train_set
(the training dataset), if necessary.
The function should return a dictionary of features for document.
For example, let’s create a feature extractor that just uses the first and last words of a document as its features.
>>> def end_word_extractor(document):
...
tokens = document.split()
...
first_word, last_word = tokens[0], tokens[-1]
...
feats = {}
...
feats["first({0})".format(first_word)] = True
...
feats["last({0})".format(last_word)] = False
...
return feats
>>> features = end_word_extractor("I feel happy")
>>> assert features == {'last(happy)': False, 'first(I)': True}
We can then use the feature extractor in a classifier by passing it as the second argument of the constructor.
>>> cl2 = NaiveBayesClassifier(test, feature_extractor=end_word_extractor)
>>> blob = TextBlob("I'm excited to try my new classifier.", classifier=cl2)
>>> blob.classify()
'pos'
Next Steps
Be sure to check out the API Reference for the classifiers module.
Want to try different POS taggers or noun phrase chunkers with TextBlobs? Check out the Advanced Usage guide.
Advanced Usage: Overriding Models and the Blobber Class
TextBlob allows you to specify which algorithms you want to use under the hood of its simple API.
3.5. Advanced Usage: Overriding Models and the Blobber Class
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Sentiment Analyzers
New in version 0.5.0.
The textblob.sentiments module contains two sentiment analysis implementations, PatternAnalyzer
(based on the pattern library) and NaiveBayesAnalyzer (an NLTK classifier trained on a movie reviews corpus).
The default implementation is PatternAnalyzer, but you can override the analyzer by passing another implementation into a TextBlob’s constructor.
For instance, the NaiveBayesAnalyzer returns
Sentiment(classification, p_pos, p_neg).
its
result
as
a
namedtuple
of
the
form:
>>> from textblob import TextBlob
>>> from textblob.sentiments import NaiveBayesAnalyzer
>>> blob = TextBlob("I love this library", analyzer=NaiveBayesAnalyzer())
>>> blob.sentiment
Sentiment(classification='pos', p_pos=0.7996209910191279, p_neg=0.2003790089808724)
Tokenizers
New in version 0.4.0.
The words and sentences properties are helpers that use the textblob.tokenizers.WordTokenizer
and textblob.tokenizers.SentenceTokenizer classes, respectively.
You can use other tokenizers, such as those provided by NLTK, by passing them into the TextBlob constructor then
accessing the tokens property.
>>> from textblob import TextBlob
>>> from nltk.tokenize import TabTokenizer
>>> tokenizer = TabTokenizer()
>>> blob = TextBlob("This is\ta rather tabby\tblob.", tokenizer=tokenizer)
>>> blob.tokens
WordList(['This is', 'a rather tabby', 'blob.'])
You can also use the tokenize([tokenizer]) method.
>>> from textblob import TextBlob
>>> from nltk.tokenize import BlanklineTokenizer
>>> tokenizer = BlanklineTokenizer()
>>> blob = TextBlob("A token\n\nof appreciation")
>>> blob.tokenize(tokenizer)
WordList(['A token', 'of appreciation'])
Noun Phrase Chunkers
TextBlob currently has two noun phrases chunker implementations, textblob.np_extractors.
FastNPExtractor (default, based on Shlomi Babluki’s implementation from this blog post) and textblob.
np_extractors.ConllExtractor, which uses the CoNLL 2000 corpus to train a tagger.
You can change the chunker implementation (or even use your own) by explicitly passing an instance of a noun phrase
extractor to a TextBlob’s constructor.
>>> from textblob import TextBlob
>>> from textblob.np_extractors import ConllExtractor
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>>> extractor = ConllExtractor()
>>> blob = TextBlob("Python is a high-level programming language.", np_
˓→extractor=extractor)
>>> blob.noun_phrases
WordList(['python', 'high-level programming language'])
POS Taggers
TextBlob currently has two POS tagger implementations, located in textblob.taggers. The default is the
PatternTagger which uses the same implementation as the pattern library.
The second implementation is NLTKTagger which uses NLTK‘s TreeBank tagger. Numpy is required to use the
NLTKTagger.
Similar to the tokenizers and noun phrase chunkers, you can explicitly specify which POS tagger to use by passing a
tagger instance to the constructor.
>>> from textblob import TextBlob
>>> from textblob.taggers import NLTKTagger
>>> nltk_tagger = NLTKTagger()
>>> blob = TextBlob("Tag! You're It!", pos_tagger=nltk_tagger)
>>> blob.pos_tags
[(Word('Tag'), u'NN'), (Word('You'), u'PRP'), (Word('''), u'VBZ'), (Word('re'), u'NN
˓→'), (Word('It')
, u'PRP')]
Parsers
New in version 0.6.0.
Parser implementations can also be passed to the TextBlob constructor.
>>> from textblob import TextBlob
>>> from textblob.parsers import PatternParser
>>> blob = TextBlob("Parsing is fun.", parser=PatternParser())
>>> blob.parse()
'Parsing/VBG/B-VP/O is/VBZ/I-VP/O fun/VBG/I-VP/O ././O/O'
Blobber: A TextBlob Factory
New in 0.4.0.
It can be tedious to repeatedly pass taggers, NP extractors, sentiment analyzers, classifiers, and tokenizers to multiple
TextBlobs. To keep your code DRY, you can use the Blobber class to create TextBlobs that share the same models.
First, instantiate a Blobber with the tagger, NP extractor, sentiment analyzer, classifier, and/or tokenizer of your
choice.
>>> from textblob import Blobber
>>> from textblob.taggers import NLTKTagger
>>> tb = Blobber(pos_tagger=NLTKTagger())
You can now create new TextBlobs like so:
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>>> blob1 = tb("This is a blob.")
>>> blob2 = tb("This is another blob.")
>>> blob1.pos_tagger is blob2.pos_tagger
True
Extensions
TextBlob supports adding custom models and new languages through “extensions”.
Extensions can be installed from the PyPI.
$ pip install textblob-name
where “name” is the name of the package.
Available extensions
Languages
• textblob-fr: French
• textblob-de: German
Part-of-speech Taggers
• textblob-aptagger: A fast and accurate tagger based on the Averaged Perceptron.
Interested in creating an extension?
See the Contributing guide.
API Reference
Blob Classes
Wrappers for various units of text, including the main TextBlob, Word, and WordList classes. Example usage:
>>> from textblob import TextBlob
>>> b = TextBlob("Simple is better than complex.")
>>> b.tags
[(u'Simple', u'NN'), (u'is', u'VBZ'), (u'better', u'JJR'), (u'than', u'IN'), (u
˓→'complex', u'NN')]
>>> b.noun_phrases
WordList([u'simple'])
>>> b.words
WordList([u'Simple', u'is', u'better', u'than', u'complex'])
>>> b.sentiment
(0.06666666666666667, 0.41904761904761906)
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>>> b.words[0].synsets()[0]
Synset('simple.n.01')
Changed in version 0.8.0: These classes are now imported from textblob rather than text.blob.
class textblob.blob.BaseBlob(text, tokenizer=None, pos_tagger=None, np_extractor=None, analyzer=None, parser=None, classifier=None, clean_html=False)
An abstract base class that all textblob classes will inherit from. Includes words, POS tag, NP, and word count
properties. Also includes basic dunder and string methods for making objects like Python strings.
Parameters
• text – A string.
• tokenizer – (optional) A tokenizer instance. If None, defaults to WordTokenizer().
• np_extractor – (optional) An NPExtractor instance.
FastNPExtractor().
If None, defaults to
• pos_tagger – (optional) A Tagger instance. If None, defaults to NLTKTagger.
• analyzer – (optional) A sentiment analyzer. If None, defaults to PatternAnalyzer.
• parser – A parser. If None, defaults to PatternParser.
• classifier – A classifier.
Changed in version 0.6.0: clean_html parameter deprecated, as it was in NLTK.
classify()
Classify the blob using the blob’s classifier.
correct()
Attempt to correct the spelling of a blob.
New in version 0.6.0.
Return type BaseBlob
detect_language()
Detect the blob’s language using the Google Translate API.
Requires an internet connection.
Usage:
>>> b = TextBlob("bonjour")
>>> b.detect_language()
u'fr'
Language code reference: https://developers.google.com/translate/v2/using_rest#language-params
New in version 0.5.0.
Return type str
ends_with(suffix, start=0, end=9223372036854775807)
Returns True if the blob ends with the given suffix.
endswith(suffix, start=0, end=9223372036854775807)
Returns True if the blob ends with the given suffix.
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find(sub, start=0, end=9223372036854775807)
Behaves like the built-in str.find() method. Returns an integer, the index of the first occurrence of the
substring argument sub in the sub-string given by [start:end].
format(*args, **kwargs)
Perform a string formatting operation, like the built-in str.format(*args, **kwargs). Returns a
blob object.
index(sub, start=0, end=9223372036854775807)
Like blob.find() but raise ValueError when the substring is not found.
join(iterable)
Behaves like the built-in str.join(iterable) method, except returns a blob object.
Returns a blob which is the concatenation of the strings or blobs in the iterable.
lower()
Like str.lower(), returns new object with all lower-cased characters.
ngrams(n=3)
Return a list of n-grams (tuples of n successive words) for this blob.
Return type List of WordLists
noun_phrases
Returns a list of noun phrases for this blob.
np_counts
Dictionary of noun phrase frequencies in this text.
parse(parser=None)
Parse the text.
Parameters parser – (optional) A parser instance. If None, defaults to this blob’s default
parser.
New in version 0.6.0.
polarity
Return the polarity score as a float within the range [-1.0, 1.0]
Return type float
pos_tags
Returns an list of tuples of the form (word, POS tag).
Example:
[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
('Thursday', 'NNP'), ('morning', 'NN')]
Return type list of tuples
replace(old, new, count=9223372036854775807)
Return a new blob object with all the occurence of old replaced by new.
rfind(sub, start=0, end=9223372036854775807)
Behaves like the built-in str.rfind() method. Returns an integer, the index of he last (right-most) occurence
of the substring argument sub in the sub-sequence given by [start:end].
rindex(sub, start=0, end=9223372036854775807)
Like blob.rfind() but raise ValueError when substring is not found.
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sentiment
Return a tuple of form (polarity, subjectivity ) where polarity is a float within the range [-1.0, 1.0] and
subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.
Return type namedtuple of the form Sentiment(polarity, subjectivity)
split(sep=None, maxsplit=9223372036854775807)
Behaves like the built-in str.split() except returns a WordList.
Return type WordList
starts_with(prefix, start=0, end=9223372036854775807)
Returns True if the blob starts with the given prefix.
startswith(prefix, start=0, end=9223372036854775807)
Returns True if the blob starts with the given prefix.
strip(chars=None)
Behaves like the built-in str.strip([chars]) method. Returns an object with leading and trailing whitespace
removed.
subjectivity
Return the subjectivity score as a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is
very subjective.
Return type float
tags
Returns an list of tuples of the form (word, POS tag).
Example:
[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
('Thursday', 'NNP'), ('morning', 'NN')]
Return type list of tuples
title()
Returns a blob object with the text in title-case.
tokenize(tokenizer=None)
Return a list of tokens, using tokenizer.
Parameters tokenizer – (optional) A tokenizer object. If None, defaults to this blob’s default
tokenizer.
tokens
Return a list of tokens, using this blob’s tokenizer object (defaults to WordTokenizer).
translate(from_lang=u’auto’, to=u’en’)
Translate the blob to another language. Uses the Google Translate API. Returns a new TextBlob.
Requires an internet connection.
Usage:
>>> b = TextBlob("Simple is better than complex")
>>> b.translate(to="es")
TextBlob('Lo simple es mejor que complejo')
Language code reference: https://developers.google.com/translate/v2/using_rest#language-params
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New in version 0.5.0..
Parameters
• from_lang (str) – Language to translate from. If None, will attempt to detect the
language.
• to (str) – Language to translate to.
Return type BaseBlob
upper()
Like str.upper(), returns new object with all upper-cased characters.
word_counts
Dictionary of word frequencies in this text.
words
Return a list of word tokens. This excludes punctuation characters. If you want to include punctuation
characters, access the tokens property.
Returns A WordList of word tokens.
class textblob.blob.Blobber(tokenizer=None,
pos_tagger=None,
np_extractor=None,
analyzer=None, parser=None, classifier=None)
A factory for TextBlobs that all share the same tagger, tokenizer, parser, classifier, and np_extractor.
Usage:
>>> from textblob import Blobber
>>> from textblob.taggers import NLTKTagger
>>> from textblob.tokenizers import SentenceTokenizer
>>> tb = Blobber(pos_tagger=NLTKTagger(), tokenizer=SentenceTokenizer())
>>> blob1 = tb("This is one blob.")
>>> blob2 = tb("This blob has the same tagger and tokenizer.")
>>> blob1.pos_tagger is blob2.pos_tagger
True
Parameters
• tokenizer – (optional) A tokenizer instance. If None, defaults to WordTokenizer().
• np_extractor – (optional) An NPExtractor instance.
FastNPExtractor().
If None, defaults to
• pos_tagger – (optional) A Tagger instance. If None, defaults to NLTKTagger.
• analyzer – (optional) A sentiment analyzer. If None, defaults to PatternAnalyzer.
• parser – A parser. If None, defaults to PatternParser.
• classifier – A classifier.
New in version 0.4.0.
class textblob.blob.Sentence(sentence, start_index=0, end_index=None, *args, **kwargs)
A sentence within a TextBlob. Inherits from BaseBlob.
Parameters
• sentence – A string, the raw sentence.
• start_index – An int, the index where this sentence begins in a TextBlob. If not given,
defaults to 0.
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• end_index – An int, the index where this sentence ends in a TextBlob. If not given,
defaults to the length of the sentence - 1.
classify()
Classify the blob using the blob’s classifier.
correct()
Attempt to correct the spelling of a blob.
New in version 0.6.0.
Return type BaseBlob
detect_language()
Detect the blob’s language using the Google Translate API.
Requires an internet connection.
Usage:
>>> b = TextBlob("bonjour")
>>> b.detect_language()
u'fr'
Language code reference: https://developers.google.com/translate/v2/using_rest#language-params
New in version 0.5.0.
Return type str
dict
The dict representation of this sentence.
end = None
The end index within a textBlob
end_index = None
The end index within a textBlob
ends_with(suffix, start=0, end=9223372036854775807)
Returns True if the blob ends with the given suffix.
endswith(suffix, start=0, end=9223372036854775807)
Returns True if the blob ends with the given suffix.
find(sub, start=0, end=9223372036854775807)
Behaves like the built-in str.find() method. Returns an integer, the index of the first occurrence of the
substring argument sub in the sub-string given by [start:end].
format(*args, **kwargs)
Perform a string formatting operation, like the built-in str.format(*args, **kwargs). Returns a
blob object.
index(sub, start=0, end=9223372036854775807)
Like blob.find() but raise ValueError when the substring is not found.
join(iterable)
Behaves like the built-in str.join(iterable) method, except returns a blob object.
Returns a blob which is the concatenation of the strings or blobs in the iterable.
lower()
Like str.lower(), returns new object with all lower-cased characters.
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ngrams(n=3)
Return a list of n-grams (tuples of n successive words) for this blob.
Return type List of WordLists
noun_phrases
Returns a list of noun phrases for this blob.
np_counts
Dictionary of noun phrase frequencies in this text.
parse(parser=None)
Parse the text.
Parameters parser – (optional) A parser instance. If None, defaults to this blob’s default
parser.
New in version 0.6.0.
polarity
Return the polarity score as a float within the range [-1.0, 1.0]
Return type float
pos_tags
Returns an list of tuples of the form (word, POS tag).
Example:
[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
('Thursday', 'NNP'), ('morning', 'NN')]
Return type list of tuples
replace(old, new, count=9223372036854775807)
Return a new blob object with all the occurence of old replaced by new.
rfind(sub, start=0, end=9223372036854775807)
Behaves like the built-in str.rfind() method. Returns an integer, the index of he last (right-most) occurence
of the substring argument sub in the sub-sequence given by [start:end].
rindex(sub, start=0, end=9223372036854775807)
Like blob.rfind() but raise ValueError when substring is not found.
sentiment
Return a tuple of form (polarity, subjectivity ) where polarity is a float within the range [-1.0, 1.0] and
subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.
Return type namedtuple of the form Sentiment(polarity, subjectivity)
split(sep=None, maxsplit=9223372036854775807)
Behaves like the built-in str.split() except returns a WordList.
Return type WordList
start = None
The start index within a TextBlob
start_index = None
The start index within a TextBlob
starts_with(prefix, start=0, end=9223372036854775807)
Returns True if the blob starts with the given prefix.
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startswith(prefix, start=0, end=9223372036854775807)
Returns True if the blob starts with the given prefix.
strip(chars=None)
Behaves like the built-in str.strip([chars]) method. Returns an object with leading and trailing whitespace
removed.
subjectivity
Return the subjectivity score as a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is
very subjective.
Return type float
tags
Returns an list of tuples of the form (word, POS tag).
Example:
[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
('Thursday', 'NNP'), ('morning', 'NN')]
Return type list of tuples
title()
Returns a blob object with the text in title-case.
tokenize(tokenizer=None)
Return a list of tokens, using tokenizer.
Parameters tokenizer – (optional) A tokenizer object. If None, defaults to this blob’s default
tokenizer.
tokens
Return a list of tokens, using this blob’s tokenizer object (defaults to WordTokenizer).
translate(from_lang=u’auto’, to=u’en’)
Translate the blob to another language. Uses the Google Translate API. Returns a new TextBlob.
Requires an internet connection.
Usage:
>>> b = TextBlob("Simple is better than complex")
>>> b.translate(to="es")
TextBlob('Lo simple es mejor que complejo')
Language code reference: https://developers.google.com/translate/v2/using_rest#language-params
New in version 0.5.0..
Parameters
• from_lang (str) – Language to translate from. If None, will attempt to detect the
language.
• to (str) – Language to translate to.
Return type BaseBlob
upper()
Like str.upper(), returns new object with all upper-cased characters.
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word_counts
Dictionary of word frequencies in this text.
words
Return a list of word tokens. This excludes punctuation characters. If you want to include punctuation
characters, access the tokens property.
Returns A WordList of word tokens.
class textblob.blob.TextBlob(text, tokenizer=None, pos_tagger=None, np_extractor=None, analyzer=None, parser=None, classifier=None, clean_html=False)
A general text block, meant for larger bodies of text (esp. those containing sentences). Inherits from BaseBlob.
Parameters
• text (str) – A string.
• tokenizer – (optional) A tokenizer instance. If None, defaults to WordTokenizer().
• np_extractor – (optional) An NPExtractor instance.
FastNPExtractor().
If None, defaults to
• pos_tagger – (optional) A Tagger instance. If None, defaults to NLTKTagger.
• analyzer – (optional) A sentiment analyzer. If None, defaults to PatternAnalyzer.
• classifier – (optional) A classifier.
classify()
Classify the blob using the blob’s classifier.
correct()
Attempt to correct the spelling of a blob.
New in version 0.6.0.
Return type BaseBlob
detect_language()
Detect the blob’s language using the Google Translate API.
Requires an internet connection.
Usage:
>>> b = TextBlob("bonjour")
>>> b.detect_language()
u'fr'
Language code reference: https://developers.google.com/translate/v2/using_rest#language-params
New in version 0.5.0.
Return type str
ends_with(suffix, start=0, end=9223372036854775807)
Returns True if the blob ends with the given suffix.
endswith(suffix, start=0, end=9223372036854775807)
Returns True if the blob ends with the given suffix.
find(sub, start=0, end=9223372036854775807)
Behaves like the built-in str.find() method. Returns an integer, the index of the first occurrence of the
substring argument sub in the sub-string given by [start:end].
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format(*args, **kwargs)
Perform a string formatting operation, like the built-in str.format(*args, **kwargs). Returns a
blob object.
index(sub, start=0, end=9223372036854775807)
Like blob.find() but raise ValueError when the substring is not found.
join(iterable)
Behaves like the built-in str.join(iterable) method, except returns a blob object.
Returns a blob which is the concatenation of the strings or blobs in the iterable.
json
The json representation of this blob.
Changed in version 0.5.1: Made json a property instead of a method to restore backwards compatibility
that was broken after version 0.4.0.
lower()
Like str.lower(), returns new object with all lower-cased characters.
ngrams(n=3)
Return a list of n-grams (tuples of n successive words) for this blob.
Return type List of WordLists
noun_phrases
Returns a list of noun phrases for this blob.
np_counts
Dictionary of noun phrase frequencies in this text.
parse(parser=None)
Parse the text.
Parameters parser – (optional) A parser instance. If None, defaults to this blob’s default
parser.
New in version 0.6.0.
polarity
Return the polarity score as a float within the range [-1.0, 1.0]
Return type float
pos_tags
Returns an list of tuples of the form (word, POS tag).
Example:
[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
('Thursday', 'NNP'), ('morning', 'NN')]
Return type list of tuples
raw_sentences
List of strings, the raw sentences in the blob.
replace(old, new, count=9223372036854775807)
Return a new blob object with all the occurence of old replaced by new.
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rfind(sub, start=0, end=9223372036854775807)
Behaves like the built-in str.rfind() method. Returns an integer, the index of he last (right-most) occurence
of the substring argument sub in the sub-sequence given by [start:end].
rindex(sub, start=0, end=9223372036854775807)
Like blob.rfind() but raise ValueError when substring is not found.
sentences
Return list of Sentence objects.
sentiment
Return a tuple of form (polarity, subjectivity ) where polarity is a float within the range [-1.0, 1.0] and
subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.
Return type namedtuple of the form Sentiment(polarity, subjectivity)
serialized
Returns a list of each sentence’s dict representation.
split(sep=None, maxsplit=9223372036854775807)
Behaves like the built-in str.split() except returns a WordList.
Return type WordList
starts_with(prefix, start=0, end=9223372036854775807)
Returns True if the blob starts with the given prefix.
startswith(prefix, start=0, end=9223372036854775807)
Returns True if the blob starts with the given prefix.
strip(chars=None)
Behaves like the built-in str.strip([chars]) method. Returns an object with leading and trailing whitespace
removed.
subjectivity
Return the subjectivity score as a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is
very subjective.
Return type float
tags
Returns an list of tuples of the form (word, POS tag).
Example:
[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
('Thursday', 'NNP'), ('morning', 'NN')]
Return type list of tuples
title()
Returns a blob object with the text in title-case.
to_json(*args, **kwargs)
Return a json representation (str) of this blob. Takes the same arguments as json.dumps.
New in version 0.5.1.
tokenize(tokenizer=None)
Return a list of tokens, using tokenizer.
Parameters tokenizer – (optional) A tokenizer object. If None, defaults to this blob’s default
tokenizer.
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tokens
Return a list of tokens, using this blob’s tokenizer object (defaults to WordTokenizer).
translate(from_lang=u’auto’, to=u’en’)
Translate the blob to another language. Uses the Google Translate API. Returns a new TextBlob.
Requires an internet connection.
Usage:
>>> b = TextBlob("Simple is better than complex")
>>> b.translate(to="es")
TextBlob('Lo simple es mejor que complejo')
Language code reference: https://developers.google.com/translate/v2/using_rest#language-params
New in version 0.5.0..
Parameters
• from_lang (str) – Language to translate from. If None, will attempt to detect the
language.
• to (str) – Language to translate to.
Return type BaseBlob
upper()
Like str.upper(), returns new object with all upper-cased characters.
word_counts
Dictionary of word frequencies in this text.
words
Return a list of word tokens. This excludes punctuation characters. If you want to include punctuation
characters, access the tokens property.
Returns A WordList of word tokens.
class textblob.blob.Word(string, pos_tag=None)
A simple word representation. Includes methods for inflection, translation, and WordNet integration.
capitalize() → unicode
Return a capitalized version of S, i.e. make the first character have upper case and the rest lower case.
center(width[, fillchar ]) → unicode
Return S centered in a Unicode string of length width. Padding is done using the specified fill character
(default is a space)
correct()
Correct the spelling of the word. Returns the word with the highest confidence using the spelling corrector.
New in version 0.6.0.
count(sub[, start[, end ]]) → int
Return the number of non-overlapping occurrences of substring sub in Unicode string S[start:end]. Optional arguments start and end are interpreted as in slice notation.
decode([encoding[, errors ]]) → string or unicode
Decodes S using the codec registered for encoding. encoding defaults to the default encoding. errors may
be given to set a different error handling scheme. Default is ‘strict’ meaning that encoding errors raise a
UnicodeDecodeError. Other possible values are ‘ignore’ and ‘replace’ as well as any other name registered
with codecs.register_error that is able to handle UnicodeDecodeErrors.
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define(pos=None)
Return a list of definitions for this word. Each definition corresponds to a synset for this word.
Parameters pos – A part-of-speech tag to filter upon. If None, definitions for all parts of
speech will be loaded.
Return type List of strings
New in version 0.7.0.
definitions
The list of definitions for this word. Each definition corresponds to a synset.
New in version 0.7.0.
detect_language()
Detect the word’s language using Google’s Translate API.
New in version 0.5.0.
encode([encoding[, errors ]]) → string or unicode
Encodes S using the codec registered for encoding. encoding defaults to the default encoding. errors may
be given to set a different error handling scheme. Default is ‘strict’ meaning that encoding errors raise a
UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and ‘xmlcharrefreplace’ as well as any
other name registered with codecs.register_error that can handle UnicodeEncodeErrors.
endswith(suffix[, start[, end ]]) → bool
Return True if S ends with the specified suffix, False otherwise. With optional start, test S beginning at
that position. With optional end, stop comparing S at that position. suffix can also be a tuple of strings to
try.
expandtabs([tabsize ]) → unicode
Return a copy of S where all tab characters are expanded using spaces. If tabsize is not given, a tab size of
8 characters is assumed.
find(sub[, start[, end ]]) → int
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
Optional arguments start and end are interpreted as in slice notation.
Return -1 on failure.
format(*args, **kwargs) → unicode
Return a formatted version of S, using substitutions from args and kwargs. The substitutions are identified
by braces (‘{‘ and ‘}’).
get_synsets(pos=None)
Return a list of Synset objects for this word.
Parameters pos – A part-of-speech tag to filter upon. If None, all synsets for all parts of speech
will be loaded.
Return type list of Synsets
New in version 0.7.0.
index(sub[, start[, end ]]) → int
Like S.find() but raise ValueError when the substring is not found.
isalnum() → bool
Return True if all characters in S are alphanumeric and there is at least one character in S, False otherwise.
isalpha() → bool
Return True if all characters in S are alphabetic and there is at least one character in S, False otherwise.
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isdecimal() → bool
Return True if there are only decimal characters in S, False otherwise.
isdigit() → bool
Return True if all characters in S are digits and there is at least one character in S, False otherwise.
islower() → bool
Return True if all cased characters in S are lowercase and there is at least one cased character in S, False
otherwise.
isnumeric() → bool
Return True if there are only numeric characters in S, False otherwise.
isspace() → bool
Return True if all characters in S are whitespace and there is at least one character in S, False otherwise.
istitle() → bool
Return True if S is a titlecased string and there is at least one character in S, i.e. upper- and titlecase
characters may only follow uncased characters and lowercase characters only cased ones. Return False
otherwise.
isupper() → bool
Return True if all cased characters in S are uppercase and there is at least one cased character in S, False
otherwise.
join(iterable) → unicode
Return a string which is the concatenation of the strings in the iterable. The separator between elements is
S.
lemma
Return the lemma of this word using Wordnet’s morphy function.
lemmatize(*args, **kwargs)
Return the lemma for a word using WordNet’s morphy function.
Parameters pos – Part of speech to filter upon. If None, defaults to _wordnet.NOUN.
New in version 0.8.1.
ljust(width[, fillchar ]) → int
Return S left-justified in a Unicode string of length width. Padding is done using the specified fill character
(default is a space).
lower() → unicode
Return a copy of the string S converted to lowercase.
lstrip([chars ]) → unicode
Return a copy of the string S with leading whitespace removed. If chars is given and not None, remove
characters in chars instead. If chars is a str, it will be converted to unicode before stripping
partition(sep) -> (head, sep, tail)
Search for the separator sep in S, and return the part before it, the separator itself, and the part after it. If
the separator is not found, return S and two empty strings.
pluralize()
Return the plural version of the word as a string.
replace(old, new[, count ]) → unicode
Return a copy of S with all occurrences of substring old replaced by new. If the optional argument count
is given, only the first count occurrences are replaced.
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rfind(sub[, start[, end ]]) → int
Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
Optional arguments start and end are interpreted as in slice notation.
Return -1 on failure.
rindex(sub[, start[, end ]]) → int
Like S.rfind() but raise ValueError when the substring is not found.
rjust(width[, fillchar ]) → unicode
Return S right-justified in a Unicode string of length width. Padding is done using the specified fill character (default is a space).
rpartition(sep) -> (head, sep, tail)
Search for the separator sep in S, starting at the end of S, and return the part before it, the separator itself,
and the part after it. If the separator is not found, return two empty strings and S.
rsplit([sep[, maxsplit ]]) → list of strings
Return a list of the words in S, using sep as the delimiter string, starting at the end of the string and working
to the front. If maxsplit is given, at most maxsplit splits are done. If sep is not specified, any whitespace
string is a separator.
rstrip([chars ]) → unicode
Return a copy of the string S with trailing whitespace removed. If chars is given and not None, remove
characters in chars instead. If chars is a str, it will be converted to unicode before stripping
singularize()
Return the singular version of the word as a string.
spellcheck()
Return a list of (word, confidence) tuples of spelling corrections.
Based on: Peter Norvig, “How to Write a Spelling Corrector” (http://norvig.com/spell-correct.html) as
implemented in the pattern library.
New in version 0.6.0.
split([sep[, maxsplit ]]) → list of strings
Return a list of the words in S, using sep as the delimiter string. If maxsplit is given, at most maxsplit
splits are done. If sep is not specified or is None, any whitespace string is a separator and empty strings
are removed from the result.
splitlines(keepends=False) → list of strings
Return a list of the lines in S, breaking at line boundaries. Line breaks are not included in the resulting list
unless keepends is given and true.
startswith(prefix[, start[, end ]]) → bool
Return True if S starts with the specified prefix, False otherwise. With optional start, test S beginning at
that position. With optional end, stop comparing S at that position. prefix can also be a tuple of strings to
try.
stem(stemmer=<PorterStemmer>)
Stem a word using various NLTK stemmers. (Default: Porter Stemmer)
New in version 0.12.0.
strip([chars ]) → unicode
Return a copy of the string S with leading and trailing whitespace removed. If chars is given and not None,
remove characters in chars instead. If chars is a str, it will be converted to unicode before stripping
swapcase() → unicode
Return a copy of S with uppercase characters converted to lowercase and vice versa.
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synsets
The list of Synset objects for this Word.
Return type list of Synsets
New in version 0.7.0.
title() → unicode
Return a titlecased version of S, i.e. words start with title case characters, all remaining cased characters
have lower case.
translate(from_lang=u’auto’, to=u’en’)
Translate the word to another language using Google’s Translate API.
New in version 0.5.0.
upper() → unicode
Return a copy of S converted to uppercase.
zfill(width) → unicode
Pad a numeric string S with zeros on the left, to fill a field of the specified width. The string S is never
truncated.
class textblob.blob.WordList(collection)
A list-like collection of words.
append(obj)
Append an object to end. If the object is a string, appends a Word object.
count(strg, case_sensitive=False, *args, **kwargs)
Get the count of a word or phrase s within this WordList.
Parameters
• strg – The string to count.
• case_sensitive – A boolean, whether or not the search is case-sensitive.
extend(iterable)
Extend WordList by appending elements from iterable. If an element is a string, appends a Word
object.
index(value[, start[, stop ]]) → integer – return first index of value.
Raises ValueError if the value is not present.
insert()
L.insert(index, object) – insert object before index
lemmatize()
Return the lemma of each word in this WordList.
lower()
Return a new WordList with each word lower-cased.
pluralize()
Return the plural version of each word in this WordList.
pop([index ]) → item – remove and return item at index (default last).
Raises IndexError if list is empty or index is out of range.
remove()
L.remove(value) – remove first occurrence of value. Raises ValueError if the value is not present.
reverse()
L.reverse() – reverse IN PLACE
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singularize()
Return the single version of each word in this WordList.
sort()
L.sort(cmp=None, key=None, reverse=False) – stable sort IN PLACE; cmp(x, y) -> -1, 0, 1
stem(*args, **kwargs)
Return the stem for each word in this WordList.
upper()
Return a new WordList with each word upper-cased.
Base Classes
Abstract base classes for models (taggers, noun phrase extractors, etc.) which define the interface for descendant
classes.
Changed in version 0.7.0: All base classes are defined in the same module, textblob.base.
class textblob.base.BaseNPExtractor
Abstract base class from which all NPExtractor classes inherit. Descendant classes must implement an
extract(text) method that returns a list of noun phrases as strings.
extract(text)
Return a list of noun phrases (strings) for a body of text.
class textblob.base.BaseParser
Abstract parser class from which all parsers inherit from. All descendants must implement a parse() method.
parse(text)
Parses the text.
class textblob.base.BaseSentimentAnalyzer
Abstract base class from which all sentiment analyzers inherit. Should implement an analyze(text) method
which returns either the results of analysis.
analyze(text)
Return the result of of analysis. Typically returns either a tuple, float, or dictionary.
class textblob.base.BaseTagger
Abstract tagger class from which all taggers inherit from. All descendants must implement a tag() method.
tag(text, tokenize=True)
Return a list of tuples of the form (word, tag) for a given set of text.
class textblob.base.BaseTokenizer
Abstract base class from which all Tokenizer classes inherit. Descendant classes must implement a
tokenize(text) method that returns a list of noun phrases as strings.
itokenize(text, *args, **kwargs)
Return a generator that generates tokens “on-demand”.
New in version 0.6.0.
Return type generator
tokenize(text)
Return a list of tokens (strings) for a body of text.
Return type list
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Tokenizers
Various tokenizer implementations.
New in version 0.4.0.
class textblob.tokenizers.SentenceTokenizer
NLTK’s sentence tokenizer (currently PunkSentenceTokenizer). Uses an unsupervised algorithm to build a
model for abbreviation words, collocations, and words that start sentences, then uses that to find sentence boundaries.
itokenize(text, *args, **kwargs)
Return a generator that generates tokens “on-demand”.
New in version 0.6.0.
Return type generator
tokenize(*args, **kwargs)
Return a list of sentences.
class textblob.tokenizers.WordTokenizer
NLTK’s recommended word tokenizer (currently the TreeBankTokenizer). Uses regular expressions to tokenize
text. Assumes text has already been segmented into sentences.
Performs the following steps:
•split standard contractions, e.g. don’t -> do n’t
•split commas and single quotes
•separate periods that appear at the end of line
itokenize(text, *args, **kwargs)
Return a generator that generates tokens “on-demand”.
New in version 0.6.0.
Return type generator
tokenize(text, include_punc=True)
Return a list of word tokens.
Parameters
• text – string of text.
• include_punc – (optional) whether to include punctuation as separate tokens. Default
to True.
textblob.tokenizers.sent_tokenize = <bound method SentenceTokenizer.itokenize of <textblob.tokenizers.SentenceT
Convenience function for tokenizing sentences
textblob.tokenizers.word_tokenize(text, include_punc=True, *args, **kwargs)
Convenience function for tokenizing text into words.
NOTE: NLTK’s word tokenizer expects sentences as input, so the text will be tokenized to sentences before
being tokenized to words.
POS Taggers
Parts-of-speech tagger implementations.
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class textblob.en.taggers.NLTKTagger
Tagger that uses NLTK’s standard TreeBank tagger. NOTE: Requires numpy. Not yet supported with PyPy.
tag(*args, **kwargs)
Tag a string text.
class textblob.en.taggers.PatternTagger
Tagger that uses the implementation in Tom de Smedt’s pattern library (http://www.clips.ua.ac.be/pattern).
tag(text, tokenize=True)
Tag a string text.
Noun Phrase Extractors
Various noun phrase extractors.
class textblob.en.np_extractors.ConllExtractor(parser=None)
A noun phrase extractor that uses chunk parsing trained with the ConLL-2000 training corpus.
extract(text)
Return a list of noun phrases (strings) for body of text.
class textblob.en.np_extractors.FastNPExtractor
A fast and simple noun phrase extractor.
Credit to Shlomi Babluk. Link to original blog post:
http://thetokenizer.com/2013/05/09/efficient-way-to-extract-the-main-topics-of-a-sentence/
extract(sentence)
Return a list of noun phrases (strings) for body of text.
Sentiment Analyzers
Sentiment analysis implementations.
New in version 0.5.0.
class textblob.en.sentiments.NaiveBayesAnalyzer(feature_extractor=<function
_default_feature_extractor>)
Naive Bayes analyzer that is trained on a dataset of movie reviews. Returns results as a named tuple of the form:
Sentiment(classification, p_pos, p_neg)
Parameters feature_extractor (callable) – Function that returns a dictionary of features,
given a list of words.
RETURN_TYPE
Return type declaration
alias of Sentiment
analyze(text)
Return the sentiment as a named tuple of the form: Sentiment(classification, p_pos,
p_neg)
train(*args, **kwargs)
Train the Naive Bayes classifier on the movie review corpus.
class textblob.en.sentiments.PatternAnalyzer
Sentiment analyzer that uses the same implementation as the pattern library. Returns results as a named tuple of
the form:
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Sentiment(polarity, subjectivity)
RETURN_TYPE
Return type declaration
alias of Sentiment
analyze(text)
Return the sentiment as a named tuple of the form: Sentiment(polarity, subjectivity).
Parsers
Various parser implementations.
New in version 0.6.0.
class textblob.en.parsers.PatternParser
Parser that uses the implementation in Tom de Smedt’s pattern library.
pattern-en#parser
http://www.clips.ua.ac.be/pages/
parse(text)
Parses the text.
Classifiers
Various classifier implementations. Also includes basic feature extractor methods.
Example Usage:
>>> from textblob import TextBlob
>>> from textblob.classifiers import NaiveBayesClassifier
>>> train = [
...
('I love this sandwich.', 'pos'),
...
('This is an amazing place!', 'pos'),
...
('I feel very good about these beers.', 'pos'),
...
('I do not like this restaurant', 'neg'),
...
('I am tired of this stuff.', 'neg'),
...
("I can't deal with this", 'neg'),
...
("My boss is horrible.", "neg")
... ]
>>> cl = NaiveBayesClassifier(train)
>>> cl.classify("I feel amazing!")
'pos'
>>> blob = TextBlob("The beer is good. But the hangover is horrible.", classifier=cl)
>>> for s in blob.sentences:
...
print(s)
...
print(s.classify())
...
The beer is good.
pos
But the hangover is horrible.
neg
New in version 0.6.0.
class textblob.classifiers.BaseClassifier(train_set,
feature_extractor=<function
basic_extractor>, format=None, **kwargs)
Abstract classifier class from which all classifers inherit. At a minimum, descendant classes must implement a
classify method and have a classifier property.
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Parameters
• train_set – The training set, either a list of tuples of the form (text,
classification) or a file-like object. text may be either a string or an iterable.
• feature_extractor (callable) – A feature extractor function that takes one or two
arguments: document and train_set.
• format (str) – If train_set is a filename, the file format, e.g. "csv" or "json". If
None, will attempt to detect the file format.
• kwargs – Additional keyword arguments are passed to the constructor of the Format
class used to read the data. Only applies when a file-like object is passed as train_set.
New in version 0.6.0.
classifier
The classifier object.
classify(text)
Classifies a string of text.
extract_features(text)
Extracts features from a body of text.
Return type dictionary of features
labels()
Returns an iterable containing the possible labels.
train(labeled_featureset)
Trains the classifier.
class textblob.classifiers.DecisionTreeClassifier(train_set, feature_extractor=<function
basic_extractor>,
format=None,
**kwargs)
A classifier based on the decision tree algorithm, as implemented in NLTK.
Parameters
• train_set – The training set, either a list of tuples of the form (text,
classification) or a filename. text may be either a string or an iterable.
• feature_extractor – A feature extractor function that takes one or two arguments:
document and train_set.
• format – If train_set is a filename, the file format, e.g. "csv" or "json". If None,
will attempt to detect the file format.
New in version 0.6.2.
accuracy(test_set, format=None)
Compute the accuracy on a test set.
Parameters
• test_set – A list of tuples of the form (text, label), or a file pointer.
• format – If test_set is a filename, the file format, e.g. "csv" or "json". If None,
will attempt to detect the file format.
classifier
The classifier.
classify(text)
Classifies the text.
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Parameters text (str) – A string of text.
extract_features(text)
Extracts features from a body of text.
Return type dictionary of features
labels()
Return an iterable of possible labels.
pprint(*args, **kwargs)
Return a string containing a pretty-printed version of this decision tree. Each line in the string corresponds
to a single decision tree node or leaf, and indentation is used to display the structure of the tree.
Return type str
pretty_format(*args, **kwargs)
Return a string containing a pretty-printed version of this decision tree. Each line in the string corresponds
to a single decision tree node or leaf, and indentation is used to display the structure of the tree.
Return type str
pseudocode(*args, **kwargs)
Return a string representation of this decision tree that expresses the decisions it makes as a nested set of
pseudocode if statements.
Return type str
train(*args, **kwargs)
Train the classifier with a labeled feature set and return the classifier. Takes the same arguments as the
wrapped NLTK class. This method is implicitly called when calling classify or accuracy methods
and is included only to allow passing in arguments to the train method of the wrapped NLTK class.
New in version 0.6.2.
Return type A classifier
update(new_data, *args, **kwargs)
Update the classifier with new training data and re-trains the classifier.
Parameters new_data – New data as a list of tuples of the form (text, label).
class textblob.classifiers.MaxEntClassifier(train_set, feature_extractor=<function basic_extractor>, format=None, **kwargs)
A maximum entropy classifier (also known as a “conditional exponential classifier”). This classifier is parameterized by a set of “weights”, which are used to combine the joint-features that are generated from a featureset
by an “encoding”. In particular, the encoding maps each (featureset, label) pair to a vector. The
probability of each label is then computed using the following equation:
dotprod(weights, encode(fs,label))
prob(fs|label) = --------------------------------------------------sum(dotprod(weights, encode(fs,l)) for l in labels)
Where dotprod is the dot product:
dotprod(a,b) = sum(x*y for (x,y) in zip(a,b))
accuracy(test_set, format=None)
Compute the accuracy on a test set.
Parameters
• test_set – A list of tuples of the form (text, label), or a file pointer.
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• format – If test_set is a filename, the file format, e.g. "csv" or "json". If None,
will attempt to detect the file format.
classifier
The classifier.
classify(text)
Classifies the text.
Parameters text (str) – A string of text.
extract_features(text)
Extracts features from a body of text.
Return type dictionary of features
labels()
Return an iterable of possible labels.
nltk_class
alias of MaxentClassifier
prob_classify(text)
Return the label probability distribution for classifying a string of text.
Example:
>>> classifier = MaxEntClassifier(train_data)
>>> prob_dist = classifier.prob_classify("I feel happy this morning.")
>>> prob_dist.max()
'positive'
>>> prob_dist.prob("positive")
0.7
Return type nltk.probability.DictionaryProbDist
train(*args, **kwargs)
Train the classifier with a labeled feature set and return the classifier. Takes the same arguments as the
wrapped NLTK class. This method is implicitly called when calling classify or accuracy methods
and is included only to allow passing in arguments to the train method of the wrapped NLTK class.
New in version 0.6.2.
Return type A classifier
update(new_data, *args, **kwargs)
Update the classifier with new training data and re-trains the classifier.
Parameters new_data – New data as a list of tuples of the form (text, label).
class textblob.classifiers.NLTKClassifier(train_set,
feature_extractor=<function
sic_extractor>, format=None, **kwargs)
An abstract class that wraps around the nltk.classify module.
ba-
Expects that descendant classes include a class variable nltk_class which is the class in the nltk.classify
module to be wrapped.
Example:
class MyClassifier(NLTKClassifier):
nltk_class = nltk.classify.svm.SvmClassifier
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accuracy(test_set, format=None)
Compute the accuracy on a test set.
Parameters
• test_set – A list of tuples of the form (text, label), or a file pointer.
• format – If test_set is a filename, the file format, e.g. "csv" or "json". If None,
will attempt to detect the file format.
classifier
The classifier.
classify(text)
Classifies the text.
Parameters text (str) – A string of text.
extract_features(text)
Extracts features from a body of text.
Return type dictionary of features
labels()
Return an iterable of possible labels.
nltk_class = None
The NLTK class to be wrapped. Must be a class within nltk.classify
train(*args, **kwargs)
Train the classifier with a labeled feature set and return the classifier. Takes the same arguments as the
wrapped NLTK class. This method is implicitly called when calling classify or accuracy methods
and is included only to allow passing in arguments to the train method of the wrapped NLTK class.
New in version 0.6.2.
Return type A classifier
update(new_data, *args, **kwargs)
Update the classifier with new training data and re-trains the classifier.
Parameters new_data – New data as a list of tuples of the form (text, label).
class textblob.classifiers.NaiveBayesClassifier(train_set,
feature_extractor=<function
basic_extractor>,
format=None,
**kwargs)
A classifier based on the Naive Bayes algorithm, as implemented in NLTK.
Parameters
• train_set – The training set, either a list of tuples of the form (text,
classification) or a filename. text may be either a string or an iterable.
• feature_extractor – A feature extractor function that takes one or two arguments:
document and train_set.
• format – If train_set is a filename, the file format, e.g. "csv" or "json". If None,
will attempt to detect the file format.
New in version 0.6.0.
accuracy(test_set, format=None)
Compute the accuracy on a test set.
Parameters
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• test_set – A list of tuples of the form (text, label), or a file pointer.
• format – If test_set is a filename, the file format, e.g. "csv" or "json". If None,
will attempt to detect the file format.
classifier
The classifier.
classify(text)
Classifies the text.
Parameters text (str) – A string of text.
extract_features(text)
Extracts features from a body of text.
Return type dictionary of features
informative_features(*args, **kwargs)
Return the most informative features as a list of tuples of the form (feature_name,
feature_value).
Return type list
labels()
Return an iterable of possible labels.
nltk_class
alias of NaiveBayesClassifier
prob_classify(text)
Return the label probability distribution for classifying a string of text.
Example:
>>> classifier = NaiveBayesClassifier(train_data)
>>> prob_dist = classifier.prob_classify("I feel happy this morning.")
>>> prob_dist.max()
'positive'
>>> prob_dist.prob("positive")
0.7
Return type nltk.probability.DictionaryProbDist
show_informative_features(*args, **kwargs)
Displays a listing of the most informative features for this classifier.
Return type None
train(*args, **kwargs)
Train the classifier with a labeled feature set and return the classifier. Takes the same arguments as the
wrapped NLTK class. This method is implicitly called when calling classify or accuracy methods
and is included only to allow passing in arguments to the train method of the wrapped NLTK class.
New in version 0.6.2.
Return type A classifier
update(new_data, *args, **kwargs)
Update the classifier with new training data and re-trains the classifier.
Parameters new_data – New data as a list of tuples of the form (text, label).
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class textblob.classifiers.PositiveNaiveBayesClassifier(positive_set, unlabeled_set,
feature_extractor=<function
contains_extractor>,
positive_prob_prior=0.5,
**kwargs)
A variant of the Naive Bayes Classifier that performs binary classification with partially-labeled training sets,
i.e. when only one class is labeled and the other is not. Assuming a prior distribution on the two labels, uses the
unlabeled set to estimate the frequencies of the features.
Example usage:
>>> from text.classifiers import PositiveNaiveBayesClassifier
>>> sports_sentences = ['The team dominated the game',
...
'They lost the ball',
...
'The game was intense',
...
'The goalkeeper catched the ball',
...
'The other team controlled the ball']
>>> various_sentences = ['The President did not comment',
...
'I lost the keys',
...
'The team won the game',
...
'Sara has two kids',
...
'The ball went off the court',
...
'They had the ball for the whole game',
...
'The show is over']
>>> classifier = PositiveNaiveBayesClassifier(positive_set=sports_sentences,
...
unlabeled_set=various_sentences)
>>> classifier.classify("My team lost the game")
True
>>> classifier.classify("And now for something completely different.")
False
Parameters
• positive_set – A collection of strings that have the positive label.
• unlabeled_set – A collection of unlabeled strings.
• feature_extractor – A feature extractor function.
• positive_prob_prior – A prior estimate of the probability of the label True.
New in version 0.7.0.
accuracy(test_set, format=None)
Compute the accuracy on a test set.
Parameters
• test_set – A list of tuples of the form (text, label), or a file pointer.
• format – If test_set is a filename, the file format, e.g. "csv" or "json". If None,
will attempt to detect the file format.
classifier
The classifier.
classify(text)
Classifies the text.
Parameters text (str) – A string of text.
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extract_features(text)
Extracts features from a body of text.
Return type dictionary of features
labels()
Return an iterable of possible labels.
train(*args, **kwargs)
Train the classifier with a labeled and unlabeled feature sets and return the classifier. Takes the same
arguments as the wrapped NLTK class. This method is implicitly called when calling classify or
accuracy methods and is included only to allow passing in arguments to the train method of the
wrapped NLTK class.
Return type A classifier
update(new_positive_data=None, new_unlabeled_data=None,
**kwargs)
Update the classifier with new data and re-trains the classifier.
positive_prob_prior=0.5,
*args,
Parameters
• new_positive_data – List of new, labeled strings.
• new_unlabeled_data – List of new, unlabeled strings.
textblob.classifiers.basic_extractor(document, train_set)
A basic document feature extractor that returns a dict indicating what words in train_set are contained in
document.
Parameters
• document – The text to extract features from. Can be a string or an iterable.
• train_set (list) – Training data set, a list of tuples of the form (words, label)
OR an iterable of strings.
textblob.classifiers.contains_extractor(document)
A basic document feature extractor that returns a dict of words that the document contains.
Blobber
class textblob.blob.Blobber(tokenizer=None,
pos_tagger=None,
np_extractor=None,
analyzer=None, parser=None, classifier=None)
A factory for TextBlobs that all share the same tagger, tokenizer, parser, classifier, and np_extractor.
Usage:
>>> from textblob import Blobber
>>> from textblob.taggers import NLTKTagger
>>> from textblob.tokenizers import SentenceTokenizer
>>> tb = Blobber(pos_tagger=NLTKTagger(), tokenizer=SentenceTokenizer())
>>> blob1 = tb("This is one blob.")
>>> blob2 = tb("This blob has the same tagger and tokenizer.")
>>> blob1.pos_tagger is blob2.pos_tagger
True
Parameters
• tokenizer – (optional) A tokenizer instance. If None, defaults to WordTokenizer().
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• np_extractor – (optional) An NPExtractor instance.
FastNPExtractor().
If None, defaults to
• pos_tagger – (optional) A Tagger instance. If None, defaults to NLTKTagger.
• analyzer – (optional) A sentiment analyzer. If None, defaults to PatternAnalyzer.
• parser – A parser. If None, defaults to PatternParser.
• classifier – A classifier.
New in version 0.4.0.
__call__(text)
Return a new TextBlob object with this Blobber’s np_extractor, pos_tagger, tokenizer,
analyzer, and classifier.
Returns A new TextBlob.
File Formats
File formats for training and testing data.
Includes a registry of valid file formats. New file formats can be added to the registry like so:
from textblob import formats
class PipeDelimitedFormat(formats.DelimitedFormat):
delimiter = '|'
formats.register('psv', PipeDelimitedFormat)
Once a format has been registered, classifiers will be able to read data files with that format.
from textblob.classifiers import NaiveBayesAnalyzer
with open('training_data.psv', 'r') as fp:
cl = NaiveBayesAnalyzer(fp, format='psv')
class textblob.formats.BaseFormat(fp, **kwargs)
Interface for format classes. Individual formats can decide on the composition and meaning of **kwargs.
Parameters fp (File) – A file-like object.
Changed in version 0.9.0: Constructor receives a file pointer rather than a file path.
classmethod detect(stream)
Detect the file format given a filename. Return True if a stream is this file format.
Changed in version 0.9.0: Changed from a static method to a class method.
to_iterable()
Return an iterable object from the data.
class textblob.formats.CSV(fp, **kwargs)
CSV format. Assumes each row is of the form text,label.
Today is a good day,pos
I hate this car.,pos
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detect(stream)
Return True if stream is valid.
to_iterable()
Return an iterable object from the data.
class textblob.formats.DelimitedFormat(fp, **kwargs)
A general character-delimited format.
classmethod detect(stream)
Return True if stream is valid.
to_iterable()
Return an iterable object from the data.
class textblob.formats.JSON(fp, **kwargs)
JSON format.
Assumes that JSON is formatted as an array of objects with text and label properties.
[
{"text": "Today is a good day.", "label": "pos"},
{"text": "I hate this car.", "label": "neg"}
]
classmethod detect(stream)
Return True if stream is valid JSON.
to_iterable()
Return an iterable object from the JSON data.
class textblob.formats.TSV(fp, **kwargs)
TSV format. Assumes each row is of the form text label.
detect(stream)
Return True if stream is valid.
to_iterable()
Return an iterable object from the data.
textblob.formats.detect(fp, max_read=1024)
Attempt to detect a file’s format, trying each of the supported formats. Return the format class that was detected.
If no format is detected, return None.
textblob.formats.get_registry()
Return a dictionary of registered formats.
textblob.formats.register(name, format_class)
Register a new format.
Parameters
• name (str) – The name that will be used to refer to the format, e.g. ‘csv’
• format_class (type) – The format class to register.
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Wordnet
Exceptions
exception textblob.exceptions.TextBlobError
A TextBlob-related error.
exception textblob.exceptions.MissingCorpusError(message=”nLooks like you are missing some required data for this
feature.nnTo download the necessary data, simply runnn python -m
textblob.download_corporannor
use
the NLTK downloader to download the
missing data: http://nltk.org/data.htmlnIf
this doesn’t fix the problem, file an issue at
https://github.com/sloria/TextBlob/issues.n”,
*args, **kwargs)
Exception thrown when a user tries to use a feature that requires a dataset or model that the user does not have
on their system.
exception textblob.exceptions.DeprecationError
Raised when user uses a deprecated feature.
exception textblob.exceptions.TranslatorError
Raised when an error occurs during language translation or detection.
exception textblob.exceptions.NotTranslated
Raised when text is unchanged after translation. This may be due to the language being unsupported by the
translator.
exception textblob.exceptions.FormatError
Raised if a data file with an unsupported format is passed to a classifier.
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CHAPTER
4
Project info
Changelog
0.13.0 (2017-08-15)
Features:
• Performance improvements to NaiveBayesClassifier (#63, #77, #123). Thanks @jcalbert for the PR.
0.12.0 (2017-02-27)
Features:
• Add Word.stem and WordList.stem methods (#145). Thanks @nitkul.
Bug fixes:
• Fix translation and language detection (#137). Thanks @EpicJhon for the fix.
Changes:
• Backwards-incompatible: Remove Python 2.6 and 3.3 support.
0.11.1 (2016-02-17)
Bug fixes:
• Fix translation and language detection (#115, #117, #119). Thanks @AdrianLC and @jschnurr for the fix.
Thanks @AdrianLC, @edgaralts, and @pouya-cognitiv for reporting.
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0.11.0 (2015-11-01)
Changes:
• Compatible with nltk>=3.1. NLTK versions < 3.1 are no longer supported.
• Change default tagger to NLTKTagger (uses NLTK’s averaged perceptron tagger).
• Tested on Python 3.5.
Bug fixes:
• Fix singularization of a number of words. Thanks @jonmcoe.
• Fix spelling correction when nltk>=3.1 is installed (#99). Thanks @shubham12101 for reporting.
0.10.0 (2015-10-04)
Changes:
• Unchanged text is now considered a translation error. Raises NotTranslated (#76). Thanks @jschnurr.
Bug fixes:
• Translator.translate will detect language of input text by default (#85). Thanks again @jschnurr.
• Fix matching of tagged phrases with CFG in ConllExtractor. Thanks @lragnarsson.
• Fix inflection of a few irregular English nouns. Thanks @jonmcoe.
0.9.1 (2015-06-10)
Bug fixes:
• Fix DecisionTreeClassifier.pprint for compatibility with nltk>=3.0.2.
• Translation no longer adds erroneous whitespace around punctuation characters (#83). Thanks @AdrianLC for
reporting and thanks @jschnurr for the patch.
0.9.0 (2014-09-15)
• TextBlob now depends on NLTK 3. The vendorized version of NLTK has been removed.
• Fix bug that raised a SyntaxError when translating text with non-ascii characters on Python 3.
• Fix bug that showed “double-escaped” unicode characters in translator output (issue #56). Thanks Evan
Dempsey.
• Backwards-incompatible: Completely remove import text.blob. You should import textblob instead.
• Backwards-incompatible: Completely remove PerceptronTagger. Install textblob-aptagger instead.
• Backwards-incompatible:
Rename
TextBlobException
MissingCorpusException to MissingCorpusError.
to
TextBlobError
and
• Backwards-incompatible: Format classes are passed a file object rather than a file path.
• Backwards-incompatible: If training a classifier with data from a file, you must pass a file object (rather than a
file path).
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• Updated English sentiment corpus.
• Add feature_extractor parameter to NaiveBayesAnalyzer.
• Add textblob.formats.get_registry() and textblob.formats.register() which allows
users to register custom data source formats.
• Change BaseClassifier.detect from a staticmethod to a classmethod.
• Improved docs.
• Tested on Python 3.4.
0.8.4 (2014-02-02)
• Fix display (__repr__) of WordList slices on Python 3.
• Add download_corpora module.
download_corpora.
Corpora must now be downloaded using python -m textblob.
0.8.3 (2013-12-29)
• Sentiment analyzers return namedtuples, e.g. Sentiment(polarity=0.12, subjectivity=0.34).
• Memory usage improvements to NaiveBayesAnalyzer and basic_extractor (default feature extractor for classifiers module).
• Add textblob.tokenizers.sent_tokenize and textblob.tokenizers.word_tokenize
convenience functions.
• Add textblob.classifiers.MaxEntClassifer.
• Improved NLTKTagger.
0.8.2 (2013-12-21)
• Fix bug in spelling correction that stripped some punctuation (Issue #48).
• Various improvements to spelling correction: preserves whitespace characters (Issue #12); handle contractions
and punctuation between words. Thanks @davidnk.
• Make TextBlob.words more memory-efficient.
• Translator now sends POST instead of GET requests. This allows for larger bodies of text to be translated (Issue
#49).
• Update pattern tagger for better accuracy.
0.8.1 (2013-11-16)
• Fix bug that caused ValueError upon sentence tokenization. This removes modifications made to the NLTK
sentence tokenizer.
• Add Word.lemmatize() method that allows passing in a part-of-speech argument.
• Word.lemma returns correct part of speech for Word objects that have their pos attribute set. Thanks @RomanYankovsky.
4.1. Changelog
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0.8.0 (2013-10-23)
• Backwards-incompatible: Renamed package to textblob. This avoids clashes with other namespaces called
text. TextBlob should now be imported with from textblob import TextBlob.
• Update pattern resources for improved parser accuracy.
• Update NLTK.
• Allow Translator to connect to proxy server.
• PerceptronTagger completely deprecated. Install the textblob-aptagger extension instead.
0.7.1 (2013-09-30)
• Bugfix updates.
• Fix bug in feature extraction for NaiveBayesClassifier.
• basic_extractor is now case-sensitive, e.g. contains(I) != contains(i)
• Fix repr output when a TextBlob contains non-ascii characters.
• Fix part-of-speech tagging with PatternTagger on Windows.
• Suppress warning about not having scikit-learn installed.
0.7.0 (2013-09-25)
• Wordnet integration. Word objects have synsets and definitions properties. The text.wordnet
module allows you to create Synset and Lemma objects directly.
• Move all English-specific code to its own module, text.en.
• Basic extensions framework in place. TextBlob has been refactored to make it easier to develop extensions.
• Add text.classifiers.PositiveNaiveBayesClassifier.
• Update NLTK.
• NLTKTagger now working on Python 3.
• Fix __str__ behavior. print(blob) should now print non-ascii text correctly in both Python 2 and 3.
• Backwards-incompatible: All abstract base classes have been moved to the text.base module.
• Backwards-incompatible:
textblob-aptagger.
DeprecationWarning.
PerceptronTagger will now be maintained as an extension,
Instantiating a text.taggers.PerceptronTagger() will raise a
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.
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• 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.
4.1. Changelog
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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.io/
• 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.
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• 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.
Authors
Development Lead
• Steven Loria <[email protected]> @sloria
Contributors (chronological)
• Pete Keen @peterkeen
• Matthew Honnibal @syllog1sm
• Roman Yankovsky @RomanYankovsky
• David Karesh @davidnk
• Evan Dempsey @evandempsey
• Wesley Childs @mrchilds
• Jeff Schnurr @jschnurr
• Adel Qalieh @adelq
• Lage Ragnarsson @lragnarsson
• Jonathon Coe @jonmcoe
• Adrián López Calvo @AdrianLC
4.2. Authors
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• Nitish Kulshrestha @nitkul
• Jhon Eslava @EpicJhon
• @jcalbert
Contributing guidelines
In General
• PEP 8, when sensible.
• Conventions and configuration.
• TextBlob wraps functionality in NLTK and pattern.en. Anything outside of that should be written as an extension.
• Test ruthlessly. Write docs for new features.
• Even more important than Test-Driven Development–Human-Driven Development.
• These guidelines may–and probably will–change.
In Particular
Questions, Feature Requests, Bug Reports, and Feedback. . .
. . .should all be reported on the Github Issue Tracker .
Setting Up for Local Development
1. Fork TextBlob on Github.
$ git clone https://github.com/sloria/TextBlob.git
$ cd TextBlob
2. Install development requirements. It is highly recommended that you use a virtualenv.
# After activating your virtualenv
$ pip install -r dev-requirements.txt
3. Install TextBlob in develop mode.
$ python setup.py develop
Developing Extensions
Extensions are packages with the name textblob-something, where “something” is the name of your extension.
Extensions should be imported with import textblob_something.
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Model Extensions
To create a new extension for a part-of-speech tagger, sentiment analyzer, noun phrase extractor, classifier, tokenizer,
or parser, simply create a module that has a class that implements the correct interface from textblob.base. For
example, a tagger might look like this:
from textblob.base import BaseTagger
class MyTagger(BaseTagger):
def tag(self, text):
# Your implementation goes here
Language Extensions
The process for developing language extensions is the same as developing model extensions. Create your part-ofspeech taggers, tokenizers, parsers, etc. in the language of your choice. Packages should be named textblob-xx
where “xx” is the two- or three-letter language code (Language code reference).
To see examples of existing extensions, visit the Extensions page.
Check out the API reference for more info on the model interfaces.
Git Branch Structure
TextBlob loosely follows Vincent Driessen’s Successful Git Branching Model . In practice, the following branch
conventions are used:
dev The next release branch.
master Current production release on PyPI.
Pull Requests
1. Create a new local branch.
$ git checkout -b name-of-feature
2. Commit your changes. Write good commit messages.
$ git commit -m "Detailed commit message"
$ git push origin name-of-feature
3. Before submitting a pull request, check the following:
• If the pull request adds functionality, it is tested and the docs are updated.
• If you’ve developed an extension, it is on the Extensions List.
• The pull request works on Python 2.7, 3.4, 3.5, 3.6, and PyPy. Use tox to verify that it does.
• You’ve added yourself to AUTHORS.rst.
4. Submit a pull request to the sloria:dev branch.
4.3. Contributing guidelines
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Running tests
To run all the tests:
$ python run_tests.py
To skip slow tests:
$ python run_tests.py fast
To skip tests that require internet:
$ python run_tests.py no-internet
To get test coverage reports (must have coverage installed):
$ python run_tests.py cover
To run tests on Python 2.7, 3.4, 3.5, and 3.6 virtual environments (must have each interpreter installed):
$ tox
Documentation
Contributions to the documentation are welcome. Documentation is written in reStructured Text (rST). A quick rST
reference can be found here. Builds are powered by Sphinx.
To build docs:
$ invoke docs -b
The -b (for “browse”) automatically opens up the docs in your browser after building.
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Python Module Index
t
textblob.base, 36
textblob.blob, 20
textblob.classifiers, 39
textblob.en.np_extractors, 38
textblob.en.parsers, 39
textblob.en.sentiments, 38
textblob.en.taggers, 37
textblob.exceptions, 49
textblob.formats, 47
textblob.tokenizers, 37
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62
Python Module Index
Index
Symbols
classifier (textblob.classifiers.DecisionTreeClassifier attribute), 40
__call__() (textblob.blob.Blobber method), 47
classifier (textblob.classifiers.MaxEntClassifier attribute),
42
A
classifier
(textblob.classifiers.NaiveBayesClassifier
accuracy()
(textblob.classifiers.DecisionTreeClassifier
attribute), 44
method), 40
classifier (textblob.classifiers.NLTKClassifier attribute),
accuracy()
(textblob.classifiers.MaxEntClassifier
43
method), 41
classifier (textblob.classifiers.PositiveNaiveBayesClassifier
accuracy()
(textblob.classifiers.NaiveBayesClassifier
attribute), 45
method), 43
classify() (textblob.blob.BaseBlob method), 21
accuracy() (textblob.classifiers.NLTKClassifier method),
classify() (textblob.blob.Sentence method), 25
42
classify() (textblob.blob.TextBlob method), 28
accuracy() (textblob.classifiers.PositiveNaiveBayesClassifier
classify() (textblob.classifiers.BaseClassifier method), 40
method), 45
classify()
(textblob.classifiers.DecisionTreeClassifier
analyze()
(textblob.base.BaseSentimentAnalyzer
method), 40
method), 36
classify() (textblob.classifiers.MaxEntClassifier method),
analyze() (textblob.en.sentiments.NaiveBayesAnalyzer
42
method), 38
classify()
(textblob.classifiers.NaiveBayesClassifier
analyze()
(textblob.en.sentiments.PatternAnalyzer
method), 44
method), 39
classify() (textblob.classifiers.NLTKClassifier method),
append() (textblob.blob.WordList method), 35
43
classify()
(textblob.classifiers.PositiveNaiveBayesClassifier
B
method), 45
BaseBlob (class in textblob.blob), 21
ConllExtractor (class in textblob.en.np_extractors), 38
BaseClassifier (class in textblob.classifiers), 39
contains_extractor() (in module textblob.classifiers), 46
BaseFormat (class in textblob.formats), 47
correct() (textblob.blob.BaseBlob method), 21
BaseNPExtractor (class in textblob.base), 36
correct() (textblob.blob.Sentence method), 25
BaseParser (class in textblob.base), 36
correct() (textblob.blob.TextBlob method), 28
BaseSentimentAnalyzer (class in textblob.base), 36
correct() (textblob.blob.Word method), 31
BaseTagger (class in textblob.base), 36
count() (textblob.blob.Word method), 31
BaseTokenizer (class in textblob.base), 36
count() (textblob.blob.WordList method), 35
basic_extractor() (in module textblob.classifiers), 46
CSV (class in textblob.formats), 47
Blobber (class in textblob.blob), 24, 46
C
capitalize() (textblob.blob.Word method), 31
center() (textblob.blob.Word method), 31
classifier (textblob.classifiers.BaseClassifier attribute), 40
D
DecisionTreeClassifier (class in textblob.classifiers), 40
decode() (textblob.blob.Word method), 31
define() (textblob.blob.Word method), 31
definitions (textblob.blob.Word attribute), 32
DelimitedFormat (class in textblob.formats), 48
63
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DeprecationError, 49
detect() (in module textblob.formats), 48
detect() (textblob.formats.BaseFormat class method), 47
detect() (textblob.formats.CSV method), 47
detect()
(textblob.formats.DelimitedFormat
class
method), 48
detect() (textblob.formats.JSON class method), 48
detect() (textblob.formats.TSV method), 48
detect_language() (textblob.blob.BaseBlob method), 21
detect_language() (textblob.blob.Sentence method), 25
detect_language() (textblob.blob.TextBlob method), 28
detect_language() (textblob.blob.Word method), 32
dict (textblob.blob.Sentence attribute), 25
format() (textblob.blob.Word method), 32
FormatError, 49
G
get_registry() (in module textblob.formats), 48
get_synsets() (textblob.blob.Word method), 32
I
index() (textblob.blob.BaseBlob method), 22
index() (textblob.blob.Sentence method), 25
index() (textblob.blob.TextBlob method), 29
index() (textblob.blob.Word method), 32
index() (textblob.blob.WordList method), 35
informative_features() (textblob.classifiers.NaiveBayesClassifier
E
method), 44
encode() (textblob.blob.Word method), 32
insert() (textblob.blob.WordList method), 35
end (textblob.blob.Sentence attribute), 25
isalnum() (textblob.blob.Word method), 32
end_index (textblob.blob.Sentence attribute), 25
isalpha() (textblob.blob.Word method), 32
ends_with() (textblob.blob.BaseBlob method), 21
isdecimal() (textblob.blob.Word method), 32
ends_with() (textblob.blob.Sentence method), 25
isdigit() (textblob.blob.Word method), 33
ends_with() (textblob.blob.TextBlob method), 28
islower() (textblob.blob.Word method), 33
endswith() (textblob.blob.BaseBlob method), 21
isnumeric() (textblob.blob.Word method), 33
endswith() (textblob.blob.Sentence method), 25
isspace() (textblob.blob.Word method), 33
endswith() (textblob.blob.TextBlob method), 28
istitle() (textblob.blob.Word method), 33
endswith() (textblob.blob.Word method), 32
isupper() (textblob.blob.Word method), 33
expandtabs() (textblob.blob.Word method), 32
itokenize() (textblob.base.BaseTokenizer method), 36
extend() (textblob.blob.WordList method), 35
itokenize()
(textblob.tokenizers.SentenceTokenizer
extract() (textblob.base.BaseNPExtractor method), 36
method), 37
extract()
(textblob.en.np_extractors.ConllExtractor itokenize() (textblob.tokenizers.WordTokenizer method),
method), 38
37
extract()
(textblob.en.np_extractors.FastNPExtractor
J
method), 38
extract_features()
(textblob.classifiers.BaseClassifier join() (textblob.blob.BaseBlob method), 22
method), 40
join() (textblob.blob.Sentence method), 25
extract_features() (textblob.classifiers.DecisionTreeClassifierjoin() (textblob.blob.TextBlob method), 29
method), 41
join() (textblob.blob.Word method), 33
extract_features() (textblob.classifiers.MaxEntClassifier JSON (class in textblob.formats), 48
method), 42
json (textblob.blob.TextBlob attribute), 29
extract_features() (textblob.classifiers.NaiveBayesClassifier
L
method), 44
extract_features()
(textblob.classifiers.NLTKClassifier labels() (textblob.classifiers.BaseClassifier method), 40
method), 43
labels()
(textblob.classifiers.DecisionTreeClassifier
extract_features() (textblob.classifiers.PositiveNaiveBayesClassifier method), 41
method), 45
labels() (textblob.classifiers.MaxEntClassifier method),
42
F
labels()
(textblob.classifiers.NaiveBayesClassifier
FastNPExtractor (class in textblob.en.np_extractors), 38
method), 44
find() (textblob.blob.BaseBlob method), 21
labels() (textblob.classifiers.NLTKClassifier method), 43
find() (textblob.blob.Sentence method), 25
labels() (textblob.classifiers.PositiveNaiveBayesClassifier
find() (textblob.blob.TextBlob method), 28
method), 46
find() (textblob.blob.Word method), 32
lemma (textblob.blob.Word attribute), 33
format() (textblob.blob.BaseBlob method), 22
lemmatize() (textblob.blob.Word method), 33
format() (textblob.blob.Sentence method), 25
lemmatize() (textblob.blob.WordList method), 35
format() (textblob.blob.TextBlob method), 28
ljust() (textblob.blob.Word method), 33
64
Index
textblob Documentation, Release 0.13.0
lower() (textblob.blob.BaseBlob method), 22
lower() (textblob.blob.Sentence method), 25
lower() (textblob.blob.TextBlob method), 29
lower() (textblob.blob.Word method), 33
lower() (textblob.blob.WordList method), 35
lstrip() (textblob.blob.Word method), 33
MaxEntClassifier (class in textblob.classifiers), 41
MissingCorpusError, 49
(textblob.classifiers.DecisionTreeClassifier
method), 41
pretty_format() (textblob.classifiers.DecisionTreeClassifier
method), 41
prob_classify()
(textblob.classifiers.MaxEntClassifier
method), 42
prob_classify() (textblob.classifiers.NaiveBayesClassifier
method), 44
pseudocode() (textblob.classifiers.DecisionTreeClassifier
method), 41
N
R
NaiveBayesAnalyzer (class in textblob.en.sentiments), 38
NaiveBayesClassifier (class in textblob.classifiers), 43
ngrams() (textblob.blob.BaseBlob method), 22
ngrams() (textblob.blob.Sentence method), 25
ngrams() (textblob.blob.TextBlob method), 29
nltk_class (textblob.classifiers.MaxEntClassifier attribute), 42
nltk_class (textblob.classifiers.NaiveBayesClassifier attribute), 44
nltk_class (textblob.classifiers.NLTKClassifier attribute),
43
NLTKClassifier (class in textblob.classifiers), 42
NLTKTagger (class in textblob.en.taggers), 37
NotTranslated, 49
noun_phrases (textblob.blob.BaseBlob attribute), 22
noun_phrases (textblob.blob.Sentence attribute), 26
noun_phrases (textblob.blob.TextBlob attribute), 29
np_counts (textblob.blob.BaseBlob attribute), 22
np_counts (textblob.blob.Sentence attribute), 26
np_counts (textblob.blob.TextBlob attribute), 29
raw_sentences (textblob.blob.TextBlob attribute), 29
register() (in module textblob.formats), 48
remove() (textblob.blob.WordList method), 35
replace() (textblob.blob.BaseBlob method), 22
replace() (textblob.blob.Sentence method), 26
replace() (textblob.blob.TextBlob method), 29
replace() (textblob.blob.Word method), 33
RETURN_TYPE (textblob.en.sentiments.NaiveBayesAnalyzer
attribute), 38
RETURN_TYPE (textblob.en.sentiments.PatternAnalyzer
attribute), 39
reverse() (textblob.blob.WordList method), 35
rfind() (textblob.blob.BaseBlob method), 22
rfind() (textblob.blob.Sentence method), 26
rfind() (textblob.blob.TextBlob method), 29
rfind() (textblob.blob.Word method), 33
rindex() (textblob.blob.BaseBlob method), 22
rindex() (textblob.blob.Sentence method), 26
rindex() (textblob.blob.TextBlob method), 30
rindex() (textblob.blob.Word method), 34
rjust() (textblob.blob.Word method), 34
rpartition() (textblob.blob.Word method), 34
rsplit() (textblob.blob.Word method), 34
rstrip() (textblob.blob.Word method), 34
M
P
pprint()
parse() (textblob.base.BaseParser method), 36
parse() (textblob.blob.BaseBlob method), 22
parse() (textblob.blob.Sentence method), 26
S
parse() (textblob.blob.TextBlob method), 29
parse() (textblob.en.parsers.PatternParser method), 39
sent_tokenize (in module textblob.tokenizers), 37
partition() (textblob.blob.Word method), 33
Sentence (class in textblob.blob), 24
PatternAnalyzer (class in textblob.en.sentiments), 38
sentences (textblob.blob.TextBlob attribute), 30
PatternParser (class in textblob.en.parsers), 39
SentenceTokenizer (class in textblob.tokenizers), 37
PatternTagger (class in textblob.en.taggers), 38
sentiment (textblob.blob.BaseBlob attribute), 22
pluralize() (textblob.blob.Word method), 33
sentiment (textblob.blob.Sentence attribute), 26
pluralize() (textblob.blob.WordList method), 35
sentiment (textblob.blob.TextBlob attribute), 30
polarity (textblob.blob.BaseBlob attribute), 22
serialized (textblob.blob.TextBlob attribute), 30
polarity (textblob.blob.Sentence attribute), 26
show_informative_features()
polarity (textblob.blob.TextBlob attribute), 29
(textblob.classifiers.NaiveBayesClassifier
pop() (textblob.blob.WordList method), 35
method), 44
pos_tags (textblob.blob.BaseBlob attribute), 22
singularize() (textblob.blob.Word method), 34
pos_tags (textblob.blob.Sentence attribute), 26
singularize() (textblob.blob.WordList method), 35
pos_tags (textblob.blob.TextBlob attribute), 29
sort() (textblob.blob.WordList method), 36
PositiveNaiveBayesClassifier
(class
in spellcheck() (textblob.blob.Word method), 34
textblob.classifiers), 44
split() (textblob.blob.BaseBlob method), 23
Index
65
textblob Documentation, Release 0.13.0
split() (textblob.blob.Sentence method), 26
split() (textblob.blob.TextBlob method), 30
split() (textblob.blob.Word method), 34
splitlines() (textblob.blob.Word method), 34
start (textblob.blob.Sentence attribute), 26
start_index (textblob.blob.Sentence attribute), 26
starts_with() (textblob.blob.BaseBlob method), 23
starts_with() (textblob.blob.Sentence method), 26
starts_with() (textblob.blob.TextBlob method), 30
startswith() (textblob.blob.BaseBlob method), 23
startswith() (textblob.blob.Sentence method), 26
startswith() (textblob.blob.TextBlob method), 30
startswith() (textblob.blob.Word method), 34
stem() (textblob.blob.Word method), 34
stem() (textblob.blob.WordList method), 36
strip() (textblob.blob.BaseBlob method), 23
strip() (textblob.blob.Sentence method), 27
strip() (textblob.blob.TextBlob method), 30
strip() (textblob.blob.Word method), 34
subjectivity (textblob.blob.BaseBlob attribute), 23
subjectivity (textblob.blob.Sentence attribute), 27
subjectivity (textblob.blob.TextBlob attribute), 30
swapcase() (textblob.blob.Word method), 34
synsets (textblob.blob.Word attribute), 34
T
tag() (textblob.base.BaseTagger method), 36
tag() (textblob.en.taggers.NLTKTagger method), 38
tag() (textblob.en.taggers.PatternTagger method), 38
tags (textblob.blob.BaseBlob attribute), 23
tags (textblob.blob.Sentence attribute), 27
tags (textblob.blob.TextBlob attribute), 30
TextBlob (class in textblob.blob), 28
textblob.base (module), 36
textblob.blob (module), 9, 20
textblob.classifiers (module), 39
textblob.en.np_extractors (module), 38
textblob.en.parsers (module), 39
textblob.en.sentiments (module), 38
textblob.en.taggers (module), 37
textblob.exceptions (module), 49
textblob.formats (module), 47
textblob.tokenizers (module), 37
TextBlobError, 49
title() (textblob.blob.BaseBlob method), 23
title() (textblob.blob.Sentence method), 27
title() (textblob.blob.TextBlob method), 30
title() (textblob.blob.Word method), 35
to_iterable() (textblob.formats.BaseFormat method), 47
to_iterable() (textblob.formats.CSV method), 48
to_iterable() (textblob.formats.DelimitedFormat method),
48
to_iterable() (textblob.formats.JSON method), 48
to_iterable() (textblob.formats.TSV method), 48
66
to_json() (textblob.blob.TextBlob method), 30
tokenize() (textblob.base.BaseTokenizer method), 36
tokenize() (textblob.blob.BaseBlob method), 23
tokenize() (textblob.blob.Sentence method), 27
tokenize() (textblob.blob.TextBlob method), 30
tokenize()
(textblob.tokenizers.SentenceTokenizer
method), 37
tokenize() (textblob.tokenizers.WordTokenizer method),
37
tokens (textblob.blob.BaseBlob attribute), 23
tokens (textblob.blob.Sentence attribute), 27
tokens (textblob.blob.TextBlob attribute), 30
train() (textblob.classifiers.BaseClassifier method), 40
train()
(textblob.classifiers.DecisionTreeClassifier
method), 41
train() (textblob.classifiers.MaxEntClassifier method), 42
train()
(textblob.classifiers.NaiveBayesClassifier
method), 44
train() (textblob.classifiers.NLTKClassifier method), 43
train() (textblob.classifiers.PositiveNaiveBayesClassifier
method), 46
train()
(textblob.en.sentiments.NaiveBayesAnalyzer
method), 38
translate() (textblob.blob.BaseBlob method), 23
translate() (textblob.blob.Sentence method), 27
translate() (textblob.blob.TextBlob method), 31
translate() (textblob.blob.Word method), 35
TranslatorError, 49
TSV (class in textblob.formats), 48
U
update()
(textblob.classifiers.DecisionTreeClassifier
method), 41
update() (textblob.classifiers.MaxEntClassifier method),
42
update()
(textblob.classifiers.NaiveBayesClassifier
method), 44
update() (textblob.classifiers.NLTKClassifier method), 43
update() (textblob.classifiers.PositiveNaiveBayesClassifier
method), 46
upper() (textblob.blob.BaseBlob method), 24
upper() (textblob.blob.Sentence method), 27
upper() (textblob.blob.TextBlob method), 31
upper() (textblob.blob.Word method), 35
upper() (textblob.blob.WordList method), 36
W
Word (class in textblob.blob), 31
word_counts (textblob.blob.BaseBlob attribute), 24
word_counts (textblob.blob.Sentence attribute), 27
word_counts (textblob.blob.TextBlob attribute), 31
word_tokenize() (in module textblob.tokenizers), 37
WordList (class in textblob.blob), 35
words (textblob.blob.BaseBlob attribute), 24
Index
textblob Documentation, Release 0.13.0
words (textblob.blob.Sentence attribute), 28
words (textblob.blob.TextBlob attribute), 31
WordTokenizer (class in textblob.tokenizers), 37
Z
zfill() (textblob.blob.Word method), 35
Index
67
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