# API Reference

## Blob Classes

Wrappers for various units of text.

This includes the main `TextBlobDE`, `Word`, and `WordList` classes.

Whenever possible, classes are inherited from the main TextBlob library, but in many cases, the models for German have to be initialised here in `textblob_de.blob`, resulting in a lot of duplicate code. The main reason being the `Word` objects. If they are generated from an inherited class, they will use the English models (e.g. for `pluralize`/`singularize`) used in the main library.

Example usage:

    >>> from textblob_de import TextBlobDE
    >>> b = TextBlobDE("Einfach ist besser als kompliziert.")
    >>> b.tags
    [('Einfach', 'RB'), ('ist', 'VB'), ('besser', 'RB'), ('als', 'IN'), ('kompliziert', 'JJ')]
    >>> b.noun_phrases
    WordList([])
    >>> b.words
    WordList(['Einfach', 'ist', 'besser', 'als', 'kompliziert'])

class `textblob_de.blob.``BaseBlob`(text, tokenizer=None, pos_tagger=None, np_extractor=None, analyzer=None, parser=None, classifier=None, clean_html=False)

  `BaseBlob` class initialised with German default models:

  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 (str) – A string. tokenizer – (optional) A tokenizer instance. If `None`, defaults to `NLTKPunktTokenizer()`. np_extractor – (optional) An NPExtractor instance. If `None`, defaults to `PatternParserNPExtractor()`. pos_tagger – (optional) A Tagger instance. If `None`, defaults to `PatternTagger`. analyzer – (optional) A sentiment analyzer. If `None`, defaults to `PatternAnalyzer`. classifier – (optional) 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: (`textblob`)

    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].

  `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.

  `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`

  `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=None, to='de')

    Translate the blob to another language.

  `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_de.blob.``BlobberDE`(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_de import BlobberDE
      >>> from textblob_de.taggers import PatternTagger
      >>> from textblob.tokenizers import PatternTokenizer
      >>> tb = Blobber(pos_tagger=PatternTagger(), tokenizer=PatternTokenizer())
      >>> blob1 = tb("Das ist ein Blob.")
      >>> blob2 = tb("Dieser Blob benutzt die selben Tagger und Tokenizer.")
      >>> blob1.pos_tagger is blob2.pos_tagger
      True

  Parameters: | text (str) – A string. tokenizer – (optional) A tokenizer instance. If `None`, defaults to `NLTKPunktTokenizer()`. np_extractor – (optional) An NPExtractor instance. If `None`, defaults to `PatternParserNPExtractor()`. pos_tagger – (optional) A Tagger instance. If `None`, defaults to `PatternTagger`. analyzer – (optional) A sentiment analyzer. If `None`, defaults to `PatternAnalyzer`. classifier – (optional) A classifier.

  New in version 0.4.0: (`textblob`)

class `textblob_de.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. 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: (`textblob`)

    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.

  `ngrams`(n=3)

    Return a list of n-grams (tuples of n successive words) for this blob.

  `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.

  `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=None, to='de')

    Translate the blob to another language.

  `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_de.blob.``TextBlobDE`(text, tokenizer=None, pos_tagger=None, np_extractor=None, analyzer=None, parser=None, classifier=None, clean_html=False)

  `TextBlob` class initialised with German default models:

  Parameters: | text (str) – A string. tokenizer – (optional) A tokenizer instance. If `None`, defaults to `NLTKPunktTokenizer()`. np_extractor – (optional) An NPExtractor instance. If `None`, defaults to `PatternParserNPExtractor()`. pos_tagger – (optional) A Tagger instance. If `None`, defaults to `PatternTagger`. 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: (`textblob`)

    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].

  `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.

  `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.

  `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: | named tuple of the form `Sentiment(polarity=0.0, subjectivity=0.0)`

  `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: (`textblob`)

  `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=None, to='de')

    Translate the blob to another language.

  `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_de.blob.``Word`(string, pos_tag=None)

  A simple word representation.

  Includes methods for inflection, translation, and WordNet integration.

  `capitalize`() → str

    Return a capitalized version of S, i.e. make the first character have upper case and the rest lower case.

  `casefold`() → str

    Return a version of S suitable for caseless comparisons.

  `center`(width[, fillchar]) → str

    Return S centered in a 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: (`textblob`)

  `count`(sub[, start[, end]]) → int

    Return the number of non-overlapping occurrences of substring sub in string S[start:end]. Optional arguments start and end are interpreted as in slice notation.

  `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: (`textblob`)

  `definitions`

    The list of definitions for this word. Each definition corresponds to a synset.

    New in version 0.7.0: (`textblob`)

  `detect_language`()

    Detect the word’s language using Google’s Translate API.

    New in version 0.5.0: (`textblob`)

  `encode`(encoding='utf-8', errors='strict') → bytes

    Encode S using the codec registered for encoding. Default encoding is ‘utf-8’. 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=8) → str

    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) → str

    Return a formatted version of S, using substitutions from args and kwargs. The substitutions are identified by braces (‘{‘ and ‘}’).

  `format_map`(mapping) → str

    Return a formatted version of S, using substitutions from mapping. 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: (`textblob`)

  `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.

  `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.

  `isidentifier`() → bool

    Return True if S is a valid identifier according to the language definition.

    Use keyword.iskeyword() to test for reserved identifiers such as “def” and “class”.

  `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.

  `isprintable`() → bool

    Return True if all characters in S are considered printable in repr() or S is empty, 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) → str

    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`(pos=None)

    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: (`textblob`)

  `ljust`(width[, fillchar]) → str

    Return S left-justified in a Unicode string of length width. Padding is done using the specified fill character (default is a space).

  `lower`() → str

    Return a copy of the string S converted to lowercase.

  `lstrip`([chars]) → str

    Return a copy of the string S with leading whitespace removed. If chars is given and not None, remove characters in chars instead.

  `maketrans`()

    Return a translation table usable for str.translate().

    If there is only one argument, it must be a dictionary mapping Unicode ordinals (integers) or characters to Unicode ordinals, strings or None. Character keys will be then converted to ordinals. If there are two arguments, they must be strings of equal length, and in the resulting dictionary, each character in x will be mapped to the character at the same position in y. If there is a third argument, it must be a string, whose characters will be mapped to None in the result.

  `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]) → str

    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.

  `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]) → str

    Return S right-justified in a 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=None, maxsplit=-1) → 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]) → str

    Return a copy of the string S with trailing whitespace removed. If chars is given and not None, remove characters in chars instead.

  `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: (`textblob`)

  `split`(sep=None, maxsplit=-1) → 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]) → 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.

  `strip`([chars]) → str

    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.

  `swapcase`() → str

    Return a copy of S with uppercase characters converted to lowercase and vice versa.

  `synsets`

    The list of Synset objects for this Word.

    Return type: | list of Synsets

    New in version 0.7.0: (`textblob`)

  `title`() → str

    Return a titlecased version of S, i.e. words start with title case characters, all remaining cased characters have lower case.

  `translate`(from_lang=None, to='de')

    Translate the word to another language using Google’s Translate API.

    New in version 0.5.0: (`textblob`)

  `upper`() → str

    Return a copy of S converted to uppercase.

  `zfill`(width) → str

    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_de.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.

  `clear`() → None -- remove all items from L
  `copy`() → list -- a shallow copy of L
  `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.

    Currently using NLTKPunktTokenizer() for all lemmatization tasks. This might cause slightly different tokenization results compared to the TextBlob.words property.

  `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`(value) → None -- remove first occurrence of value.

    Raises ValueError if the value is not present.

  `reverse`()

    L.reverse() – reverse IN PLACE

  `singularize`()

    Return the single version of each word in this WordList.

  `sort`(key=None, reverse=False) → None -- stable sort *IN PLACE*
  `upper`()

    Return a new WordList with each word upper-cased.

## Base Classes

Extensions to Abstract base classes in `textblob.base`

class `textblob_de.base.``BaseLemmatizer`

  Abstract base class from which all Lemmatizer classes inherit. Descendant classes must implement a `lemmatize(text)` method that returns a WordList of Word object with updated lemma properties.

  New in version 0.2.3: (`textblob_de`)

  `lemmatize`(text)

    Return a list of (lemma, tag) tuples.

## Tokenizers

Various tokenizer implementations.

class `textblob_de.tokenizers.``NLTKPunktTokenizer`

  Tokenizer included in `nltk.tokenize.punkt` package.

  This is the default tokenizer in `textblob-de`

  PROs:

  - trained model available for German
  - deals with many abbreviations and common German tokenization problems oob

  CONs:

  - not very flexible (model has to be re-trained on your own corpus)
  `itokenize`(text, *args, **kwargs)

    Return a generator that generates tokens “on-demand”.

    New in version 0.6.0.

    Return type: | generator

  `sent_tokenize`(text, **kwargs)

    NLTK’s sentence tokenizer (currently PunktSentenceTokenizer).

    Uses an unsupervised algorithm to build a model for abbreviation words, collocations, and words that start sentences, then uses that to find sentence boundaries.

  `tokenize`(text, include_punc=True, nested=False)

    Return a list of word tokens.

    Parameters: | text – string of text. include_punc – (optional) whether to include punctuation as separate tokens. Default to True. nested – (optional) whether to return tokens as nested lists of sentences. Default to False.

  `word_tokenize`(text, include_punc=True)

    NLTK’s PunktWordTokenizer uses a regular expression to divide a text into tokens, leaving all periods attached to words, but separating off other punctuation.

class `textblob_de.tokenizers.``PatternTokenizer`

  Tokenizer included in `pattern.de` package.

  PROs:

  - handling of emoticons
  - flexible implementations of abbreviations
  - can be adapted very easily

  CONs:

  - ordinal numbers cause sentence breaks
  - indices of Sentence() objects cannot be computed
  `itokenize`(text, *args, **kwargs)

    Return a generator that generates tokens “on-demand”.

    New in version 0.6.0.

    Return type: | generator

  `sent_tokenize`(text, **kwargs)

    Returns a list of sentences.

    Each sentence is a space-separated string of tokens (words). Handles common cases of abbreviations (e.g., etc., ...). Punctuation marks are split from other words. Periods (or ?!) mark the end of a sentence. Headings without an ending period are inferred by line breaks.

  `tokenize`(text, include_punc=True, nested=False)

    Return a list of word tokens.

    Parameters: | text – string of text. include_punc – (optional) whether to include punctuation as separate tokens. Default to True.

class `textblob_de.tokenizers.``SentenceTokenizer`(tokenizer=None, *args, **kwargs)

  Generic sentence tokenization class, using tokenizer specified in TextBlobDE() instance.

  Enables SentenceTokenizer().itokenize generator that would be lost otherwise.

  Aim: Not to break core API of the main TextBlob library.

  Parameters: | tokenizer – (optional) A tokenizer instance. If `None`, defaults to `NLTKPunktTokenizer()`.

  `itokenize`(text, *args, **kwargs)

    Return a generator that generates tokens “on-demand”.

    New in version 0.6.0.

    Return type: | generator

  `sent_tokenize`(text, **kwargs)

    Compatibility method to tokenizers included in `textblob-de`

  `tokenize`(text, **kwargs)

    Return a list of word tokens.

    Parameters: | text – string of text. include_punc – (optional) whether to include punctuation as separate tokens. Default to True.

class `textblob_de.tokenizers.``WordTokenizer`(tokenizer=None, *args, **kwargs)

  Generic word tokenization class, using tokenizer specified in TextBlobDE() instance.

  You can also submit the tokenizer as keyword argument: `WordTokenizer(tokenizer=NLTKPunktTokenizer())`

  Enables WordTokenizer().itokenize generator that would be lost otherwise.

  Default: NLTKPunktTokenizer().word_tokenize(text, include_punc=True)

  Aim: Not to break core API of the main TextBlob library.

  Parameters: | tokenizer – (optional) A tokenizer instance. If `None`, defaults to `NLTKPunktTokenizer()`.

  `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, **kwargs)

    Return a list of word tokens.

    Parameters: | text – string of text. include_punc – (optional) whether to include punctuation as separate tokens. Default to True.

  `word_tokenize`(text, include_punc=True)

    Compatibility method to tokenizers included in `textblob-de`

`textblob_de.tokenizers.``sent_tokenize`(text, tokenizer=None)

  Convenience function for tokenizing sentences (not iterable).

  If tokenizer is not specified, the default tokenizer NLTKPunktTokenizer() is used (same behaviour as in the main TextBlob library).

  This function returns the sentences as a generator object.

`textblob_de.tokenizers.``word_tokenize`(text, tokenizer=None, 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.

  This function returns an itertools chain object (generator).

## POS Taggers

Default taggers for German.

    >>> from textblob_de.taggers import PatternTagger

or

    >>> from textblob_de import PatternTagger

class `textblob_de.taggers.``PatternTagger`(tokenizer=None, include_punc=False)

  Tagger that uses the implementation in Tom de Smedt’s pattern library (http://www.clips.ua.ac.be/pattern).

  Parameters: | tokenizer – (optional) A tokenizer instance. If `None`, defaults to `PatternTokenizer()`. include_punc – (optional) whether to include punctuation as separate tokens. Default to `False`.

  `tag`(sentence, tokenize=True)

    Tag a string sentence.

    Parameters: | or list sentence (str) – A string or a list of sentence strings. tokenize – (optional) If `False` string has to be tokenized before (space separated string).

## Noun Phrase Extractors

Various noun phrase extractor implementations.

# `PatternParserNPExtractor()`.

class `textblob_de.np_extractors.``PatternParserNPExtractor`(tokenizer=None)

  Extract noun phrases (NP) from PatternParser() output.

  Very naïve and resource hungry approach:

  - get parser output
  - try to correct as many obvious parser errors as you can (e.g. eliminate wrongly tagged verbs)
  - filter insignificant words

  Parameters: | tokenizer – (optional) A tokenizer instance. If `None`, defaults to `PatternTokenizer()`.

  `extract`(text)

    Return a list of noun phrases (strings) for a body of text.

    Parameters: | text (str) – A string.

## Sentiment Analyzers

German sentiment analysis implementations.

Main resource for `de-sentiment.xml`:

- German Polarity Lexicon
- See xml comment section in `de-sentiment.xml` for details

class `textblob_de.sentiments.``PatternAnalyzer`(tokenizer=None, lemmatizer=None, lemmatize=True)

  Sentiment analyzer that uses the same implementation as the pattern library. Returns results as a tuple of the form:

  `(polarity, subjectivity)`

  `RETURN_TYPE`

    Return type declaration

    alias of `Sentiment`

  `analyze`(text)

    Return the sentiment as a tuple of the form: `(polarity, subjectivity)`

    Parameters: | text (str) – A string.

  `kind` = 'co'

    adapted from ‘textblob.en.sentiments.py’

## Parsers

Default parsers for German.

    >>> from textblob_de.parsers import PatternParser

or

    >>> from textblob_de import PatternParser

class `textblob_de.parsers.``PatternParser`(tokenizer=None, tokenize=True, pprint=False, tags=True, chunks=True, relations=False, lemmata=False, encoding='utf-8', tagset=None)

  Parser that uses the implementation in Tom de Smedt’s pattern library. http://www.clips.ua.ac.be/pages/pattern-de#parser

  Parameters: | tokenizer – (optional) A tokenizer instance. If `None`, defaults to `PatternTokenizer()`. tokenize – (optional) Split punctuation marks from words? (Default `True`) pprint – (optional) Use `pattern`‘s `pprint` function to display parse trees (Default `False`) tags – (optional) Parse part-of-speech tags? (NN, JJ, ...) (Default `True`) chunks – (optional) Parse chunks? (NP, VP, PNP, ...) (Default `True`) relations – (optional) Parse chunk relations? (-SBJ, -OBJ, ...) (Default `False`) lemmata – (optional) Parse lemmata? (schönes => schön) (Default `False`) encoding – (optional) Input string encoding. (Default `utf-8`) tagset – (optional) Penn Treebank II (default) or (‘penn’|’universal’|’stts’).

  `parse`(text)

    Parses the text.

    `pattern.de.parse(**kwargs)` can be passed to the parser instance and are documented in the main docstring of `PatternParser()`.

    Parameters: | text (str) – A string.

  `parsetree`(text)

    Returns a parsed `pattern` Text object from the given string.

## Classifiers (from TextBlob main package)

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 at 0x00000000065D58C8>, format=None)

  Abstract classifier class from which all classifers inherit. At a minimum, descendant classes must implement a `classify` method and have a `classifier` property.

  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 (function) – 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.

  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 at 0x00000000065D58C8>, format=None)

  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 filename. 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.

  `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

  `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 at 0x00000000065D58C8>, format=None)

  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 filename. 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 basic_extractor at 0x00000000065D58C8>, format=None)

  An abstract class that wraps around the nltk.classify module.

  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

  `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 filename. 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 at 0x00000000065D58C8>, format=None)

  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: | test_set – A list of tuples of the form `(text, label)`, or a filename. 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)`.

class `textblob.classifiers.``PositiveNaiveBayesClassifier`(positive_set, unlabeled_set, feature_extractor=<function contains_extractor at 0x00000000065D5950>, positive_prob_prior=0.5)

  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 filename. 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.

  `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, positive_prob_prior=0.5, *args, **kwargs)

    Update the classifier with new data and re-trains the classifier.

    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 – Training data set, a list of tuples of the form `(words, label)`.

`textblob.classifiers.``contains_extractor`(document)

  A basic document feature extractor that returns a dict of words that the document contains.

## Blobber

class `textblob_de.blob.``BlobberDE`(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_de import BlobberDE
      >>> from textblob_de.taggers import PatternTagger
      >>> from textblob.tokenizers import PatternTokenizer
      >>> tb = Blobber(pos_tagger=PatternTagger(), tokenizer=PatternTokenizer())
      >>> blob1 = tb("Das ist ein Blob.")
      >>> blob2 = tb("Dieser Blob benutzt die selben Tagger und Tokenizer.")
      >>> blob1.pos_tagger is blob2.pos_tagger
      True

  Parameters: | text (str) – A string. tokenizer – (optional) A tokenizer instance. If `None`, defaults to `NLTKPunktTokenizer()`. np_extractor – (optional) An NPExtractor instance. If `None`, defaults to `PatternParserNPExtractor()`. pos_tagger – (optional) A Tagger instance. If `None`, defaults to `PatternTagger`. analyzer – (optional) A sentiment analyzer. If `None`, defaults to `PatternAnalyzer`. classifier – (optional) A classifier.

  New in version 0.4.0: (`textblob`)

  `__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 (from TextBlob main package)

File formats for training and testing data.

class `textblob.formats.``BaseFormat`(fname)

  Interface for format classes.

  Parameters: | f – A filename.

  static `detect`(stream)

    Detect the file format given a filename. Return True if a stream is this file format.

  `to_iterable`()

    Return an iterable object from the data.

class `textblob.formats.``CSV`(fname)

  CSV format. Assumes each row is of the form `text,label`.

      Today is a good day,pos
      I hate this car.,pos

  static `detect`(stream)

    Return True if stream is valid CSV.

  `to_iterable`()

    Return an iterable object from the data.

class `textblob.formats.``DelimitedFormat`(fname)

  A general character-delimited format.

  static `detect`(stream)

    Return True if stream is valid.

  `to_iterable`()

    Return an iterable object from the data.

class `textblob.formats.``JSON`(fname)

  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"}
      ]

  static `detect`(stream)

    Return True if stream is valid JSON.

  `to_iterable`()

    Return an iterable object from the JSON data.

class `textblob.formats.``TSV`(fname)

  TSV format. Assumes each row is of the form `text label`.

  static `detect`(stream)

    Return True if stream is valid CSV.

  `to_iterable`()

    Return an iterable object from the data.

`textblob.formats.``detect`(filename, 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`.

## Exceptions (from TextBlob main package)

exception `textblob.exceptions.``MissingCorpusException`(message="nLooks like you are missing some required data for this feature.nnTo download the necessary data, simply runnn curl https://raw.github.com/sloria/TextBlob/master/download_corpora.py | pythonnnOr 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.