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German language support for TextBlob.

Project description

textblob_de - latest PyPI version Travis-CI Documentation Status Number of PyPI downloads LICENSE info

German language support for TextBlob by Steven Loria.

This python package is being developed as a TextBlob Language Extension. See Extension Guidelines for details.


  • NEW: Works with Python3.7

  • All directly accessible textblob_de classes (e.g. Sentence() or Word()) are initialized with default models for German

  • Properties or methods that do not yet work for German raise a NotImplementedError

  • German sentence boundary detection and tokenization (NLTKPunktTokenizer)

  • Consistent use of specified tokenizer for all tools (NLTKPunktTokenizer or PatternTokenizer)

  • Part-of-speech tagging (PatternTagger) with keyword include_punc=True (defaults to False)

  • Tagset conversion in PatternTagger with keyword tagset='penn'|'universal'|'stts' (defaults to penn)

  • Parsing (PatternParser) with all pattern keywords, plus pprint=True (defaults to False)

  • Noun Phrase Extraction (PatternParserNPExtractor)

  • Lemmatization (PatternParserLemmatizer)

  • Polarity detection (PatternAnalyzer) - Still EXPERIMENTAL, does not yet have information on subjectivity

  • Full API support on Python3

  • Supports Python 2 and 3

  • See working features overview for details


$ pip install -U textblob-de
$ python -m textblob.download_corpora

Or the latest development release (apparently this does not always work on Windows see issues #1744/5 for details):

$ pip install -U git+
$ python -m textblob.download_corpora


>>> from textblob_de import TextBlobDE as TextBlob
>>> text = '''Heute ist der 3. Mai 2014 und Dr. Meier feiert seinen 43. Geburtstag.
Ich muss unbedingt daran denken, Mehl, usw. für einen Kuchen einzukaufen. Aber leider
habe ich nur noch EUR 3.50 in meiner Brieftasche.'''
>>> blob = TextBlob(text)
>>> blob.sentences
[Sentence("Heute ist der 3. Mai 2014 und Dr. Meier feiert seinen 43. Geburtstag."),
 Sentence("Ich muss unbedingt daran denken, Mehl, usw. für einen Kuchen einzukaufen."),
 Sentence("Aber leider habe ich nur noch EUR 3.50 in meiner Brieftasche.")]
>>> blob.tokens
WordList(['Heute', 'ist', 'der', '3.', 'Mai', ...]
>>> blob.tags
[('Heute', 'RB'), ('ist', 'VB'), ('der', 'DT'), ('3.', 'LS'), ('Mai', 'NN'),
('2014', 'CD'), ...]
# Default: Only noun_phrases that consist of two or more meaningful parts are displayed.
# Not perfect, but a start (relies heavily on parser accuracy)
>>> blob.noun_phrases
WordList(['Mai 2014', 'Dr. Meier', 'seinen 43. Geburtstag', 'Kuchen einzukaufen',
'meiner Brieftasche'])
>>> blob = TextBlob("Das Auto ist sehr schön.")
>>> blob.parse()
'Das/DT/B-NP/O Auto/NN/I-NP/O ist/VB/B-VP/O sehr/RB/B-ADJP/O schön/JJ/I-ADJP/O'
>>> from textblob_de import PatternParser
>>> blob = TextBlobDE("Das ist ein schönes Auto.", parser=PatternParser(pprint=True, lemmata=True))
>>> blob.parse()
      WORD   TAG    CHUNK   ROLE   ID     PNP    LEMMA

       Das   DT     -       -      -      -      das
       ist   VB     VP      -      -      -      sein
       ein   DT     NP      -      -      -      ein
   schönes   JJ     NP ^    -      -      -      schön
      Auto   NN     NP ^    -      -      -      auto
         .   .      -       -      -      -      .
>>> from textblob_de import PatternTagger
>>> blob = TextBlob(text, pos_tagger=PatternTagger(include_punc=True))
[('Das', 'DT'), ('Auto', 'NN'), ('ist', 'VB'), ('sehr', 'RB'), ('schön', 'JJ'), ('.', '.')]
>>> blob = TextBlob("Das Auto ist sehr schön.")
>>> blob.sentiment
Sentiment(polarity=1.0, subjectivity=0.0)
>>> blob = TextBlob("Das ist ein hässliches Auto.")
>>> blob.sentiment
Sentiment(polarity=-1.0, subjectivity=0.0)
>>> blob.words.lemmatize()
WordList(['das', 'sein', 'ein', 'hässlich', 'Auto'])
>>> from textblob_de.lemmatizers import PatternParserLemmatizer
>>> _lemmatizer = PatternParserLemmatizer()
>>> _lemmatizer.lemmatize("Das ist ein hässliches Auto.")
[('das', 'DT'), ('sein', 'VB'), ('ein', 'DT'), ('hässlich', 'JJ'), ('Auto', 'NN')]

Access to pattern API in Python3

>>> from textblob_de.packages import pattern_de as pd
>>> print(pd.attributive("neugierig", gender=pd.FEMALE, role=pd.INDIRECT, article="die"))

Documentation and API Reference


  • Python >= 2.6 or >= 3.3


  • Planned Extensions

  • Additional PoS tagging options, e.g. NLTK tagging (NLTKTagger)

  • Improve noun phrase extraction (e.g. based on RFTagger output)

  • Improve sentiment analysis (find suitable subjectivity scores)

  • Improve functionality of Sentence() and Word() objects

  • Adapt more tests from the main TextBlob library (esp. for TextBlobDE() in


MIT licensed. See the bundled LICENSE file for more details.


Coded with Wing IDE (free open source developer license)

Python IDE for Python -


0.4.3 (03/01/2019)

  • Added support for Python3.7 (StopIteration --> return) Pull Request #18 (thanks @andrewmfiorillo)

  • Fixed tests for Google translation examples

  • Updated tox/Travis-CI config files to include latest Python & pypy versions

  • Updated sphinx_rtd_theme to version 0.4.2 to fix rendering problems on RTD

  • Updated publish commands, Makefile & to new PiPy (using twine)

0.4.2 (02/05/2015)

  • Removed dependency on NLTK, as it already is a TextBlob dependency

  • Temporary workaround for NLTK Issue #824 for tox/Travis-CI

  • (update 13/01/2015) NLTK Issue #824 fixed, workaround removed

  • Enabled pattern tagset conversion ('penn'|'universal'|'stts') for PatternTagger

  • Added tests for tagset conversion

  • Fixed test for Arabic translation example (Google translation has changed)

  • Added tests for lemmatizer

  • Bugfix: PatternAnalyzer no longer breaks on subsequent ocurrences of the same (word, tag) pairs on Python3 see comments to Pull Request #11

  • Bugfix/performance enhancement: Sentiment dictionary in PatternAnalyzer no longer reloaded for every sentence Pull Request #11 (thanks @Arttii)

0.4.1 (03/10/2014)

  • Docs hosted on RTD

  • Removed dependency on nltk’s depricated PunktWordTokenizer and replaced it with TreebankWordTokenizer see nltk/nltk#746 (comment) for details

0.4.0 (17/09/2014)

  • Fixed Issue #7 (restore textblob>=0.9.0 compatibility)

  • Depend on nltk3. Vendorized nltk was removed in textblob>=0.9.0

  • Fixed ImportError on Python2 (unicodecsv)

0.3.1 (29/08/2014)

  • Improved PatternParserNPExtractor (less false positives in verb filter)

  • Made sure that all keyword arguments with default None are checked with is not None

  • Fixed shortcut to in vendorized library

  • Added Makefile to facilitate development process

  • Added docs and API reference

0.3.0 (14/08/2014)

  • Fixed Issue #5 (text + space + period)

0.2.9 (14/08/2014)

  • Fixed tokenization in PatternParser (if initialized manually, punctuation was not always separated from words)

  • Improved handling of empty strings (Issue #3) and of strings containing single punctuation marks (Issue #4) in PatternTagger and PatternParser

  • Added tests for empty strings and for strings containing single punctuation marks

0.2.8 (14/08/2014)

0.2.7 (13/08/2014)

  • Fixed Issue #1 lemmatization of strings containing a forward slash (/)

  • Enhancement Issue #2 use the same rtype as textblob for sentiment detection.

  • Fixed tokenization in PatternParserLemmatizer

0.2.6 (04/08/2014)

  • Fixed for package data in sdist

0.2.5 (04/08/2014)

  • sdist is non-functional as important files are missing due to a misconfiguration in - does not affect wheels

  • Major internal refactoring (but no backwards-incompatible API changes) with the aim of restoring complete compatibility to original pattern>=2.6 library on Python2

  • Separation of textblob and pattern code

  • On Python2 the vendorized version of is only used if original is not installed (same as nltk)

  • Made function and all parser keywords accessible to customise parser output

  • Access to complete API on Python2 and Python3 from textblob_de.packages import pattern_de as pd

  • tox passed on all major platforms (Win/Linux/OSX)

0.2.3 (26/07/2014)

  • Lemmatizer: PatternParserLemmatizer() extracts lemmata from Parser output

  • Improved polarity analysis through look-up of lemmatised word forms

0.2.2 (22/07/2014)

  • Option: Include punctuation in tags/pos_tags properties (b = TextBlobDE(text, tagger=PatternTagger(include_punc=True)))

  • Added BlobberDE() class initialized with German models

  • TextBlobDE(), Sentence(), WordList() and Word() classes are now all initialized with German models

  • Restored complete API compatibility with textblob.tokenizers module of the main TextBlob library

0.2.1 (20/07/2014)

  • Noun Phrase Extraction: PatternParserNPExtractor() extracts NPs from Parser output

  • Refactored the way TextBlobDE() passes on arguments and keyword arguments to individual tools

  • Backwards-incompatible: Deprecate parser_show_lemmata=True keyword in TextBlob(). Use parser=PatternParser(lemmata=True) instead.

0.2.0 (18/07/2014)

  • vastly improved tokenization (NLTKPunktTokenizer and PatternTokenizer with tests)

  • consistent use of specified tokenizer for all tools

  • TextBlobDE with initialized default models for German

  • Parsing (PatternParser) plus

  • EXPERIMENTAL implementation of Polarity detection (PatternAnalyzer)

  • first attempt at extracting German Polarity clues into de-sentiment.xml

  • tox tests passing for py26, py27, py33 and py34

0.1.3 (09/07/2014)

  • First release on PyPI

0.1.0 - 0.1.2 (09/07/2014)

  • First release on github

  • A number of experimental releases for testing purposes

  • Adapted version badges, tests & travis-ci config

  • Code adapted from sample extension textblob-fr

  • Language specific linguistic resources copied from pattern-de

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