Overriding Models and the Blobber Class — TextBlob 0.19.0 documentation (original) (raw)

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.

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 its result as a namedtuple of the form: Sentiment(classification, p_pos, p_neg).

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 fromthis 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 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:

blob1 = tb("This is a blob.") blob2 = tb("This is another blob.") blob1.pos_tagger is blob2.pos_tagger True