Simple, Pythonic text processing. Sentiment analysis, part-of-speech tagging, noun phrase parsing, and more.
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
TextBlob stands on the giant shoulders of NLTK and pattern, and plays nicely with both.
Noun phrase extraction
Classification (Naive Bayes, Decision Tree)
Tokenization (splitting text into words and sentences)
Word and phrase frequencies
Word inflection (pluralization and singularization) and lemmatization
Add new models or languages through extensions
Get it now
$ pip install -U textblob $ python -m textblob.download_corpora
See more examples at the Quickstart guide.
Full documentation is available at https://textblob.readthedocs.io/.
Python >= 2.7 or >= 3.5
MIT licensed. See the bundled LICENSE file for more details.
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