API Reference¶

Doc and Corpus¶

Load, process, iterate, transform, and save text content paired with metadata — a document.

class textacy.doc.Doc(content, metadata=None, lang=<function detect_language>)[source]

A text document parsed by spaCy and, optionally, paired with key metadata. Transform Doc into an easily-customized list of terms, a bag-of-words or (more general) bag-of-terms, or a semantic network; save and load parsed content and metadata to and from disk; index, slice, and iterate through tokens and sentences; and more.

Initialize from a text and (optional) metadata:

>>> content = '''
...     The apparent symmetry between the quark and lepton families of
...     the Standard Model (SM) are, at the very least, suggestive of
...     a more fundamental relationship between them. In some Beyond the
...     Standard Model theories, such interactions are mediated by
...     leptoquarks (LQs): hypothetical color-triplet bosons with both
...     lepton and baryon number and fractional electric charge.'''
...     'title': 'A Search for 2nd-generation Leptoquarks at √s = 7 TeV',
...     'author': 'Burton DeWilde',
...     'pub_date': '2012-08-01'}
>>> print(doc)
Doc(71 tokens; "The apparent symmetry between the quark and lep...")


Transform into other, common formats:

>>> doc.to_bag_of_words(lemmatize=False, as_strings=False)
{205123: 1, 21382: 1, 17929: 1, 175499: 2, 396: 1, 29774: 1, 27472: 1,
4498: 1, 1814: 1, 1176: 1, 49050: 1, 287836: 1, 1510365: 1, 6239: 2,
3553: 1, 5607: 1, 4776: 1, 49580: 1, 6701: 1, 12078: 2, 63216: 1,
6738: 1, 83061: 1, 5243: 1, 1599: 1}
>>> doc.to_bag_of_terms(ngrams=2, named_entities=True,
...                     lemmatize=True, as_strings=True)
{'apparent symmetry': 1, 'baryon number': 1, 'electric charge': 1,
'fractional electric': 1, 'fundamental relationship': 1,
'hypothetical color': 1, 'lepton family': 1, 'model theory': 1,
'standard model': 2, 'triplet boson': 1}


Doc as sequence of tokens, emulating spaCy’s “sequence API”:

>>> doc[49]  # spacy.Token
leptoquarks
>>> doc[:3]  # spacy.Span
The apparent symmetry


Save to and load from disk:

>>> doc.save('~/Desktop', name='leptoquarks')
>>> print(doc)
Doc(71 tokens; "The apparent symmetry between the quark and lep...")

Parameters: content (str or spacy.Doc) – Document content as (unicode) text or an already-parsed spacy.Doc. If str, content is processed by models loaded with a spacy.Language and assigned to spacy_doc. metadata (dict) – Dictionary of relevant information about content. This can be helpful when identifying and filtering documents, as well as when engineering features for model inputs. lang (str or spacy.Language or callable) – Language of document content. If known, pass a standard 2-letter language code (e.g. “en”), or the name of a spacy model for the desired language (e.g. “en_core_web_md”), or an already-instantiated spacy.Language object. If not known, pass a function/callable that takes unicode text as input and outputs a standard 2-letter language code. The given or detected language str is used to instantiate a corresponding spacy.Language with all models loaded by default, and the appropriate 2-letter lang code is assigned to Doc.lang. Note: The spacy.Language object parses content (if str) and sets the spacy_vocab and spacy_stringstore attributes. See https://spacy.io/docs/usage/models#available for available spacy models.
lang

str – 2-letter code for language of Doc.

metadata

dict – Dictionary of relevant information about content.

spacy_doc

spacy.Dochttps://spacy.io/docs#doc

spacy_vocab

spacy.Vocabhttps://spacy.io/docs#vocab

spacy_stringstore

spacy.StringStorehttps://spacy.io/docs#stringstore

count(term)[source]

Get the number of occurrences (i.e. count) of term in Doc.

Parameters: term (str or int or spacy.Token or spacy.Span) – The term to be counted can be given as a string, a unique integer id, a spacy token, or a spacy span. Counts for the same term given in different forms are the same! Count of term in Doc. int

Tip

Counts are cached. The first time a single word’s count is looked up, all words’ counts are saved, resulting in a slower runtime the first time but orders of magnitude faster runtime for subsequent calls for this or any other word. Similarly, if a bigram’s count is looked up, all bigrams’ counts are stored — etc. If spans are merged using Doc.merge(), all cached counts are deleted, since merging spans will invalidate many counts. Better to merge first, count second!

classmethod load(path, name=None)[source]

Load content and metadata from disk, and initialize a Doc.

Parameters: path (str) – Directory on disk where content and metadata are saved. name (str) – Identifying/uniquifying name prepended to the default filenames ‘spacy_doc.bin’ and ‘metadata.json’, used when doc was saved to disk via Doc.save(). textacy.Doc

Warning

If the spacy.Vocab object used to save this document is not the same as the one used to load it, there will be problems! Consequently, this functionality is only useful as short-term but not long-term storage.

merge(spans)[source]

Merge spans in-place within Doc so that each takes up a single token. Note: All cached counts on this doc are cleared after a merge.

Parameters: spans (Iterable[spacy.Span]) – for example, the results from extract.named_entities() or extract.pos_regex_matches()
n_sents

The number of sentences in the document.

n_tokens

The number of tokens in the document — including punctuation.

pos_tagged_text

Return text as an ordered, nested list of (token, POS) pairs per sentence.

save(path, name=None)[source]

Save Doc content and metadata to disk.

Parameters: path (str) – Directory on disk where content + metadata will be saved. name (str) – Prepend default filenames ‘spacy_doc.bin’ and ‘metadata.json’ with a name to identify/uniquify this particular document.

Warning

If the spacy.Vocab object used to save this document is not the same as the one used to load it, there will be problems! Consequently, this functionality is only useful as short-term but not long-term storage.

sents

Yield the document’s sentences, as segmented by spaCy.

text

Return the document’s raw text.

to_bag_of_terms(ngrams=(1, 2, 3), named_entities=True, normalize=u'lemma', lemmatize=None, lowercase=None, weighting=u'count', as_strings=False, **kwargs)[source]

Transform Doc into a bag-of-terms: the set of unique terms in Doc mapped to their frequency of occurrence, where “terms” includes ngrams and/or named entities.

Parameters: ngrams (int or Set[int]) – n of which n-grams to include; (1, 2, 3) (default) includes unigrams (words), bigrams, and trigrams; 2 if only bigrams are wanted; falsy (e.g. False) to not include any named_entities (bool) – if True (default), include named entities; note: if ngrams are also included, any ngrams that exactly overlap with an entity are skipped to prevent double-counting lemmatize (bool) – deprecated if True, words are lemmatized before counting; for example, ‘happy’, ‘happier’, and ‘happiest’ would be grouped together as ‘happy’, with a count of 3 lowercase (bool) – deprecated if True and lemmatize is False, words are lower- cased before counting; for example, ‘happy’ and ‘Happy’ would be grouped together as ‘happy’, with a count of 2 normalize (str or callable) – if ‘lemma’, lemmatize terms; if ‘lower’, lowercase terms; if false-y, use the form of terms as they appear in doc; if a callable, must accept a spacy.Token or spacy.Span and return a str, e.g. textacy.spacy_utils.normalized_str() weighting ({'count', 'freq', 'binary'}) – Type of weight to assign to terms. If ‘count’ (default), weights are the absolute number of occurrences (count) of term in doc. If ‘binary’, all counts are set equal to 1. If ‘freq’, term counts are normalized by the total token count, giving their relative frequency of occurrence. as_strings (bool) – if True, words are returned as strings; if False (default), words are returned as their unique integer ids kwargs – filter_stops (bool) filter_punct (bool) filter_nums (bool) include_pos (str or Set[str]) exclude_pos (str or Set[str]) min_freq (int) include_types (str or Set[str]) exclude_types (str or Set[str] drop_determiners (bool) See extract.words(), extract.ngrams(), and extract.named_entities() for more information on these parameters. mapping of a unique term id or string (depending on the value of as_strings) to its absolute, relative, or binary frequency of occurrence (depending on the value of weighting). dict
to_bag_of_words(normalize=u'lemma', lemmatize=None, lowercase=None, weighting=u'count', as_strings=False)[source]

Transform Doc into a bag-of-words: the set of unique words in Doc mapped to their absolute, relative, or binary frequency of occurrence.

Parameters: normalize (str) – if ‘lemma’, lemmatize words before counting; if ‘lower’, lowercase words before counting; otherwise, words are counted using the form with which they they appear in doc lemmatize (bool) – if True, words are lemmatized before counting; for example, ‘happy’, ‘happier’, and ‘happiest’ would be grouped together as ‘happy’, with a count of 3 (DEPRECATED) lowercase (bool) – deprecated if True and lemmatize is False, words are lower-cased before counting; for example, ‘happy’ and ‘Happy’ would be grouped together as ‘happy’, with a count of 2 weighting ({'count', 'freq', 'binary'}) – Type of weight to assign to words. If ‘count’ (default), weights are the absolute number of occurrences (count) of word in doc. If ‘binary’, all counts are set equal to 1. If ‘freq’, word counts are normalized by the total token count, giving their relative frequency of occurrence. Note: The resulting set of frequencies won’t (necessarily) sum to 1.0, since punctuation and stop words are filtered out after counts are normalized. as_strings (bool) – if True, words are returned as strings; if False (default), words are returned as their unique integer ids mapping of a unique word id or string (depending on the value of as_strings) to its absolute, relative, or binary frequency of occurrence (depending on the value of weighting). dict
to_semantic_network(nodes=u'words', normalize=u'lemma', edge_weighting=u'default', window_width=10)[source]

Transform Doc into a semantic network, where nodes are either ‘words’ or ‘sents’ and edges between nodes may be weighted in different ways.

Parameters: nodes ({'words', 'sents'}) – type of doc component to use as nodes in the semantic network normalize (str or callable) – if ‘lemma’, lemmatize terms; if ‘lower’, lowercase terms; if false-y, use the form of terms as they appear in doc; if a callable, must accept a spacy.Token or spacy.Span (if nodes = ‘words’ or ‘sents’, respectively) and return a str, e.g. textacy.spacy_utils.normalized_str() edge_weighting (str) – type of weighting to apply to edges between nodes; if nodes == 'words', options are {‘cooc_freq’, ‘binary’}, if nodes == 'sents', options are {‘cosine’, ‘jaccard’}; if ‘default’, ‘cooc_freq’ or ‘cosine’ will be automatically used window_width (int) – size of sliding window over terms that determines which are said to co-occur; only applicable if ‘words’ where nodes represent either terms or sentences in doc; edges, the relationships between them networkx.Graph ValueError – if nodes is neither ‘words’ nor ‘sents’
to_terms_list(ngrams=(1, 2, 3), named_entities=True, normalize=u'lemma', lemmatize=None, lowercase=None, as_strings=False, **kwargs)[source]

Transform Doc into a sequence of ngrams and/or named entities, which aren’t necessarily in order of appearance, where each term appears in the list with the same frequency that it appears in Doc.

Parameters: ngrams (int or Set[int]) – n of which n-grams to include; (1, 2, 3) (default) includes unigrams (words), bigrams, and trigrams; 2 if only bigrams are wanted; falsy (e.g. False) to not include any named_entities (bool) – if True (default), include named entities in the terms list; note: if ngrams are also included, named entities are added first, and any ngrams that exactly overlap with an entity are skipped to prevent double-counting lemmatize (bool) – deprecated if True (default), lemmatize all terms lowercase (bool) – deprecated if True and lemmatize is False, words are lower-cased normalize (str or callable) – if ‘lemma’, lemmatize terms; if ‘lower’, lowercase terms; if false-y, use the form of terms as they appear in doc; if a callable, must accept a spacy.Token or spacy.Span and return a str, e.g. textacy.spacy_utils.normalized_str() as_strings (bool) – if True, terms are returned as strings; if False (default), terms are returned as their unique integer ids kwargs – filter_stops (bool) filter_punct (bool) filter_nums (bool) include_pos (str or Set[str]) exclude_pos (str or Set[str]) min_freq (int) include_types (str or Set[str]) exclude_types (str or Set[str] drop_determiners (bool) see extract.words, extract.ngrams, and extract.named_entities for more information on these parameters int or str – the next term in the terms list, either as a unique integer id or as a string ValueError – if neither named_entities nor ngrams are included

Note

Despite the name, this is a generator function; to get an actual list of terms, call list(doc.to_terms_list()).

tokenized_text

Return text as an ordered, nested list of tokens per sentence.

tokens

Yield the document’s tokens, as tokenized by spaCy. Equivalent to iterating directly: for token in Doc: <do stuff>

Load, process, iterate, transform, and save a collection of documents — a corpus.

class textacy.corpus.Corpus(lang, texts=None, docs=None, metadatas=None)[source]

An ordered collection of textacy.Doc s, all of the same language and sharing the same spacy.Language models and vocabulary. Track corpus statistics; flexibly add, iterate through, filter for, and remove documents; save and load parsed content and metadata to and from disk; and more.

Initialize from a stream of texts and corresponding metadatas:

>>> cw = textacy.datasets.CapitolWords()
>>> records = cw.docs(limit=50)
...     records, 'text')
>>> corpus = textacy.Corpus(
>>> print(corpus)
Corpus(50 docs; 32163 tokens)


Index, slice, and flexibly get particular documents:

>>> corpus[0]
Doc(159 tokens; "Mr. Speaker, 480,000 Federal employees are work...")
>>> corpus[:3]
[Doc(159 tokens; "Mr. Speaker, 480,000 Federal employees are work..."),
Doc(219 tokens; "Mr. Speaker, a relationship, to work and surviv..."),
Doc(336 tokens; "Mr. Speaker, I thank the gentleman for yielding...")]
>>> match_func = lambda doc: doc.metadata['speaker_name'] == 'Bernie Sanders'
>>> for doc in corpus.get(match_func, limit=3):
...     print(doc)
Doc(159 tokens; "Mr. Speaker, 480,000 Federal employees are work...")
Doc(336 tokens; "Mr. Speaker, I thank the gentleman for yielding...")
Doc(177 tokens; "Mr. Speaker, if we want to understand why in th...")


Add and remove documents, with automatic updating of corpus statistics:

>>> records = cw.docs(congress=114, limit=25)
...     records, 'text')
>>> print(corpus)
Corpus(75 docs; 55869 tokens)
>>> corpus.remove(lambda doc: doc.metadata['speaker_name'] == 'Rick Santorum')
>>> print(corpus)
Corpus(60 docs; 48532 tokens)
>>> del corpus[:5]
>>> print(corpus)
Corpus(55 docs; 47444 tokens)


Get word and doc frequencies in absolute, relative, or binary form:

>>> counts = corpus.word_freqs(lemmatize=True, weighting='count')
>>> idf = corpus.word_doc_freqs(lemmatize=True, weighting='idf')


Save to and load from disk:

>>> corpus.save('~/Desktop', name='congress', compression='gzip')
...     '~/Desktop', name='congress', compression='gzip')
>>> print(corpus)
Corpus(55 docs; 47444 tokens)

Parameters: lang (str or spacy.Language) – Language of content for all docs in corpus. Pass a standard 2-letter language code (e.g. “en”) or the name of a spacy model for the desired language (e.g. “en_core_web_md”) or an already-instantiated spacy.Language object. If a str, the value is used to instantiate the corresponding spacy.Language with all models loaded by default, and the appropriate 2-letter lang code is assigned to Corpus.lang. Note: The spacy.Language object parses all documents contents and sets the spacy_vocab and spacy_stringstore attributes. See https://spacy.io/docs/usage/models#available for available spacy models. texts (Iterable[str]) – Stream of documents as (unicode) text, to be processed by spaCy and added to the corpus as textacy.Doc s. docs (Iterable[textacy.Doc] or Iterable[spacy.Doc]) – Stream of documents already-processed by spaCy alone or via textacy. metadatas (Iterable[dict]) – Stream of dictionaries of relevant doc metadata. Note: This stream must align exactly with texts or docs, or else metadata will be mis-assigned. More concretely, the first item in metadatas will be assigned to the first item in texts or docs, and so on from there.
lang

str – 2-letter code for language of documents in Corpus.

n_docs

int – Number of documents in Corpus.

n_tokens

int – Total number of tokens of all documents in Corpus.

n_sents

int – Total number of sentences of all documents in Corpus. If the spacy.Language used to process documents did not include a syntactic parser, upon which sentence segmentation relies, this value will be null.

docs

List[textacy.Doc] – List of documents in Corpus. In 99% of cases, you should never have to interact directly with this list; instead, index and slice directly on Corpus or use the flexible Corpus.get() and Corpus.remove() <Corpus.remove() methods.

spacy_lang

spacy.Languagehttp://spacy.io/docs/#english

spacy_vocab

spacy.Vocabhttps://spacy.io/docs#vocab

spacy_stringstore

spacy.StringStorehttps://spacy.io/docs#stringstore

add_doc(doc, metadata=None)[source]

Add an existing textacy.Doc or initialize a new one from a spacy.Doc to the corpus.

Parameters: doc (textacy.Doc or spacy.Doc) – metadata (dict) – Dictionary of relevant document metadata. If doc is a spacy.Doc, it will be paired as usual; if doc is a textacy.Doc, it will overwrite any existing metadata.

Warning

If doc was already added to this or another Corpus, it will be deep-copied and then added as if a new document. A warning message will be logged. This is probably not a thing you should do.

add_text(text, metadata=None)[source]

Create a textacy.Doc from text and metadata, then add it to the corpus.

Parameters: text (str) – Document (text) content to add to corpus as a Doc. metadata (dict) – Dictionary of relevant document metadata.
add_texts(texts, metadatas=None, n_threads=3, batch_size=1000)[source]

Process a stream of texts (and a corresponding stream of metadata dicts, optionally) in parallel with spaCy; add as textacy.Doc s to the corpus.

Parameters: texts (Iterable[str]) – Stream of texts to add to corpus as Doc s metadatas (Iterable[dict]) – Stream of dictionaries of relevant document metadata. Note: This stream must align exactly with texts, or metadata will be mis-assigned to texts. More concretely, the first item in metadatas will be assigned to the first item in texts, and so on from there. n_threads (int) – Number of threads to use when processing texts in parallel, if available. batch_size (int) – Number of texts to process at a time.

fileio.split_record_fields() http://spacy.io/docs/#multi-threaded

get(match_func, limit=-1)[source]

Iterate over docs in Corpus and return all (or N <= limit) for which match_func(doc) is True.

Parameters: match_func (func) – Function that takes a textacy.Doc as input and returns a boolean value. For example: Corpus.get(lambda x: len(x) >= 100)  gets all docs with 100+ tokens. And: Corpus.get(lambda x: x.metadata['author'] == 'Burton DeWilde')  gets all docs whose author was given as ‘Burton DeWilde’. limit (int) – Maximum number of matched docs to return. next document passing match_func up to limit docs

Tip

To get doc(s) by index, treat Corpus as a list and use Python’s usual indexing and slicing: Corpus[0] gets the first document in the corpus; Corpus[:5] gets the first 5; etc.

classmethod load(path, name=None, compression=None)[source]

Load content and metadata from disk, and initialize a Corpus.

Parameters: path (str) – Directory on disk where content + metadata are saved. name (str) – Identifying/uniquifying name prepended to the default filenames ‘spacy_docs.bin’, ‘metadatas.json’, and ‘info.json’, used when corpus was saved to disk via Corpus.save(). compression ({'gzip', 'bz2', 'lzma'} or None) – Type of compression used to reduce size of ‘metadatas.json’ file when saved, if any. textacy.Corpus

Warning

If the spacy.Vocab object used to save this document is not the same as the one used to load it, there will be problems! Consequently, this functionality is only useful as short-term but not long-term storage.

remove(match_func, limit=-1)[source]

Remove all (or N <= limit) docs in Corpus for which match_func(doc) is True. Corpus doc/sent/token counts are adjusted accordingly, as are the Doc.corpus_index attributes on affected documents.

Parameters: match_func (func) – Function that takes a textacy.Doc and returns a boolean value. For example: Corpus.remove(lambda x: len(x) >= 100)  removes docs with 100+ tokens. And: Corpus.remove(lambda x: x.metadata['author'] == 'Burton DeWilde')  removes docs whose author was given as ‘Burton DeWilde’. limit (int) – Maximum number of matched docs to remove.

Tip

To remove doc(s) by index, treat Corpus as a list and use Python’s usual indexing and slicing: del Corpus[0] removes the first document in the corpus; del Corpus[:5] removes the first 5; etc.

save(path, name=None, compression=None)[source]

Save Corpus content and metadata to disk.

Parameters: path (str) – Directory on disk where content + metadata will be saved. name (str) – Prepend default filenames ‘spacy_docs.bin’, ‘metadatas.json’, and ‘info.json’ with a name to identify/uniquify this particular corpus. compression ({'gzip', 'bz2', 'lzma'} or None) – Type of compression used to reduce size of ‘metadatas.json’ file, if any.

Warning

If the spacy.Vocab object used to save this corpus is not the same as the one used to load it, there will be problems! Consequently, this functionality is only useful as short-term but not long-term storage.

vectors

Constituent docs’ word vectors stacked together in a matrix.

word_doc_freqs(normalize=u'lemma', lemmatize=None, lowercase=None, weighting=u'count', smooth_idf=True, as_strings=False)[source]

Map the set of unique words in Corpus to their document counts as absolute, relative, inverse, or binary frequencies of occurence.

Parameters: normalize (str) – if ‘lemma’, lemmatize words before counting; if ‘lower’, lowercase words before counting; otherwise, words are counted using the form with which they they appear in docs lemmatize (bool) – if True, words are lemmatized before counting; for example, ‘happy’, ‘happier’, and ‘happiest’ would be grouped together as ‘happy’, with a count of 3 (DEPRECATED) lowercase (bool) – if True and lemmatize is False, words are lower- cased before counting; for example, ‘happy’ and ‘Happy’ would be grouped together as ‘happy’, with a count of 2 (DEPRECATED) weighting ({'count', 'freq', 'idf', 'binary'}) – Type of weight to assign to words. If ‘count’ (default), weights are the absolute number (count) of documents in which word appears. If ‘binary’, all counts are set equal to 1. If ‘freq’, word doc counts are normalized by the total document count, giving their relative frequency of occurrence. If ‘idf’, weights are the log of the inverse relative frequencies: log(n_docs / word_doc_count) or log(1 + n_docs / word_doc_count) if smooth_idf is True. smooth_idf (bool) – if True, add 1 to all document frequencies when calculating ‘idf’ weighting, equivalent to adding a single document to the corpus containing every unique word as_strings (bool) – if True, words are returned as strings; if False (default), words are returned as their unique integer ids mapping of a unique word id or string (depending on the value of as_strings) to the number of documents in which it appears weighted as absolute, relative, or binary frequencies (depending on the value of weighting). dict

vsm.get_doc_freqs() <textacy.vsm.get_doc_freqs>()

word_freqs(normalize=u'lemma', lemmatize=None, lowercase=None, weighting=u'count', as_strings=False)[source]

Map the set of unique words in Corpus to their counts as absolute, relative, or binary frequencies of occurence. This is akin to Doc.to_bag_of_words().

Parameters: normalize (str) – if ‘lemma’, lemmatize words before counting; if ‘lower’, lowercase words before counting; otherwise, words are counted using the form with which they they appear in docs lemmatize (bool) – if True, words are lemmatized before counting; for example, ‘happy’, ‘happier’, and ‘happiest’ would be grouped together as ‘happy’, with a count of 3 (DEPRECATED) lowercase (bool) – if True and lemmatize is False, words are lower- cased before counting; for example, ‘happy’ and ‘Happy’ would be grouped together as ‘happy’, with a count of 2 (DEPRECATED) weighting ({'count', 'freq', 'binary'}) – Type of weight to assign to words. If ‘count’ (default), weights are the absolute number of occurrences (count) of word in corpus. If ‘binary’, all counts are set equal to 1. If ‘freq’, word counts are normalized by the total token count, giving their relative frequency of occurrence. Note: The resulting set of frequencies won’t (necessarily) sum to 1.0, since punctuation and stop words are filtered out after counts are normalized. as_strings (bool) – if True, words are returned as strings; if False (default), words are returned as their unique integer ids mapping of a unique word id or string (depending on the value of as_strings) to its absolute, relative, or binary frequency of occurrence (depending on the value of weighting). dict

vsm.get_term_freqs() <textacy.vsm.get_term_freqs>()

Text Preprocessing¶

Functions that modify raw text in-place, replacing contractions, URLs, emails, phone numbers, and currency symbols with standardized forms. These should be applied before processing by Spacy, but be warned: preprocessing may affect the interpretation of the text – and spacy’s processing of it.

textacy.preprocess.fix_bad_unicode(text, normalization=u'NFC')[source]

Fix unicode text that’s “broken” using ftfy; this includes mojibake, HTML entities and other code cruft, and non-standard forms for display purposes.

Parameters: text (str) – raw text normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}) – if ‘NFC’, combines characters and diacritics written using separate code points, e.g. converting “e” plus an acute accent modifier into “é”; unicode can be converted to NFC form without any change in its meaning! if ‘NFKC’, additional normalizations are applied that can change the meanings of characters, e.g. ellipsis characters will be replaced with three periods str
textacy.preprocess.normalize_whitespace(text)[source]

Given text str, replace one or more spacings with a single space, and one or more linebreaks with a single newline. Also strip leading/trailing whitespace.

textacy.preprocess.preprocess_text(text, fix_unicode=False, lowercase=False, transliterate=False, no_urls=False, no_emails=False, no_phone_numbers=False, no_numbers=False, no_currency_symbols=False, no_punct=False, no_contractions=False, no_accents=False)[source]

Normalize various aspects of a raw text doc before parsing it with Spacy. A convenience function for applying all other preprocessing functions in one go.

Parameters: text (str) – raw text to preprocess fix_unicode (bool) – if True, fix “broken” unicode such as mojibake and garbled HTML entities lowercase (bool) – if True, all text is lower-cased transliterate (bool) – if True, convert non-ascii characters into their closest ascii equivalents no_urls (bool) – if True, replace all URL strings with ‘URL‘ no_emails (bool) – if True, replace all email strings with ‘EMAIL‘ no_phone_numbers (bool) – if True, replace all phone number strings with ‘PHONE‘ no_numbers (bool) – if True, replace all number-like strings with ‘NUMBER‘ no_currency_symbols (bool) – if True, replace all currency symbols with their standard 3-letter abbreviations no_punct (bool) – if True, remove all punctuation (replace with empty string) no_contractions (bool) – if True, replace English contractions with their unshortened forms no_accents (bool) – if True, replace all accented characters with unaccented versions; NB: if transliterate is True, this option is redundant input text processed according to function args str

Warning

These changes may negatively affect subsequent NLP analysis performed on the text, so choose carefully, and preprocess at your own risk!

textacy.preprocess.remove_accents(text, method=u'unicode')[source]

Remove accents from any accented unicode characters in text str, either by transforming them into ascii equivalents or removing them entirely.

Parameters: text (str) – raw text method ({'unicode', 'ascii'}) – if ‘unicode’, remove accented char for any unicode symbol with a direct ASCII equivalent; if ‘ascii’, remove accented char for any unicode symbol NB: the ‘ascii’ method is notably faster than ‘unicode’, but less good str ValueError – if method is not in {‘unicode’, ‘ascii’}
textacy.preprocess.remove_punct(text, marks=None)[source]

Remove punctuation from text by replacing all instances of marks with an empty string.

Parameters: text (str) – raw text marks (str) – If specified, remove only the characters in this string, e.g. marks=',;:' removes commas, semi-colons, and colons. Otherwise, all punctuation marks are removed. str

Note

When marks=None, Python’s built-in str.translate() is used to remove punctuation; otherwise,, a regular expression is used instead. The former’s performance is about 5-10x faster.

textacy.preprocess.replace_currency_symbols(text, replace_with=None)[source]

Replace all currency symbols in text str with string specified by replace_with str.

Parameters: text (str) – raw text replace_with (str) – if None (default), replace symbols with their standard 3-letter abbreviations (e.g. ‘$’ with ‘USD’, ‘£’ with ‘GBP’); otherwise, pass in a string with which to replace all symbols (e.g. “CURRENCY”) str textacy.preprocess.replace_emails(text, replace_with=u'*EMAIL*')[source] Replace all emails in text str with replace_with str. textacy.preprocess.replace_numbers(text, replace_with=u'*NUMBER*')[source] Replace all numbers in text str with replace_with str. textacy.preprocess.replace_phone_numbers(text, replace_with=u'*PHONE*')[source] Replace all phone numbers in text str with replace_with str. textacy.preprocess.replace_urls(text, replace_with=u'*URL*')[source] Replace all URLs in text str with replace_with str. textacy.preprocess.transliterate_unicode(text)[source] Try to represent unicode data in ascii characters similar to what a human with a US keyboard would choose. Works great for languages of Western origin, worse the farther the language gets from Latin-based alphabets. It’s based on hand-tuned character mappings that also contain ascii approximations for symbols and non-Latin alphabets. textacy.preprocess.unpack_contractions(text)[source] Replace English contractions in text str with their unshortened forms. N.B. The “‘d” and “‘s” forms are ambiguous (had/would, is/has/possessive), so are left as-is. Information Extraction¶ Functions to extract various elements of interest from documents already parsed by spaCy, such as n-grams, named entities, subject-verb-object triples, and acronyms. textacy.extract.acronyms_and_definitions(doc, known_acro_defs=None)[source] Extract a collection of acronyms and their most likely definitions, if available, from a spacy-parsed doc. If multiple definitions are found for a given acronym, only the most frequently occurring definition is returned. Parameters: doc (textacy.Doc or spacy.Doc or spacy.Span) – known_acro_defs (dict, optional) – if certain acronym/definition pairs are known, pass them in as {acronym (str): definition (str)}; algorithm will not attempt to find new definitions unique acronyms (keys) with matched definitions (values) dict References Taghva, Kazem, and Jeff Gilbreth. “Recognizing acronyms and their definitions.” International Journal on Document Analysis and Recognition 1.4 (1999): 191-198. textacy.extract.direct_quotations(doc)[source] Baseline, not-great attempt at direction quotation extraction (no indirect or mixed quotations) using rules and patterns. English only. Parameters: doc (textacy.Doc or spacy.Doc) – (spacy.Span, spacy.Token, spacy.Span) – next quotation in doc represented as a (speaker, reporting verb, quotation) 3-tuple Notes Loosely inspired by Krestel, Bergler, Witte. “Minding the Source: Automatic Tagging of Reported Speech in Newspaper Articles”. TODO: Better approach would use ML, but needs a training dataset. textacy.extract.named_entities(doc, include_types=None, exclude_types=None, drop_determiners=True, min_freq=1)[source] Extract an ordered sequence of named entities (PERSON, ORG, LOC, etc.) from a spacy-parsed doc, optionally filtering by entity types and frequencies. Parameters: doc (textacy.Doc or spacy.Doc) – include_types (str or Set[str]) – remove named entities whose type IS NOT in this param; if “NUMERIC”, all numeric entity types (“DATE”, “MONEY”, “ORDINAL”, etc.) are included exclude_types (str or Set[str]) – remove named entities whose type IS in this param; if “NUMERIC”, all numeric entity types (“DATE”, “MONEY”, “ORDINAL”, etc.) are excluded drop_determiners (bool) – remove leading determiners (e.g. “the”) from named entities (e.g. “the United States” => “United States”) min_freq (int) – remove named entities that occur in doc fewer than min_freq times spacy.Span – the next named entity from doc passing all specified filters in order of appearance in the document Raise: TypeError: if include_types or exclude_types is not a str, a set of str, or a falsy value textacy.extract.ngrams(doc, n, filter_stops=True, filter_punct=True, filter_nums=False, include_pos=None, exclude_pos=None, min_freq=1)[source] Extract an ordered sequence of n-grams (n consecutive words) from a spacy-parsed doc, optionally filtering n-grams by the types and parts-of-speech of the constituent words. Parameters: doc (textacy.Doc, spacy.Doc, or spacy.Span) – n (int) – number of tokens per n-gram; 2 => bigrams, 3 => trigrams, etc. filter_stops (bool) – if True, remove ngrams that start or end with a stop word filter_punct (bool) – if True, remove ngrams that contain any punctuation-only tokens filter_nums (bool) – if True, remove ngrams that contain any numbers or number-like tokens (e.g. 10, ‘ten’) include_pos (str or Set[str]) – remove ngrams if any of their constituent tokens’ part-of-speech tags ARE NOT included in this param exclude_pos (str or Set[str]) – remove ngrams if any of their constituent tokens’ part-of-speech tags ARE included in this param min_freq (int, optional) – remove ngrams that occur in doc fewer than min_freq times spacy.Span – the next ngram from doc passing all specified filters, in order of appearance in the document ValueError – if n < 1 TypeError – if include_pos or exclude_pos is not a str, a set of str, or a falsy value Note Filtering by part-of-speech tag uses the universal POS tag set, http://universaldependencies.org/u/pos/ textacy.extract.noun_chunks(doc, drop_determiners=True, min_freq=1)[source] Extract an ordered sequence of noun chunks from a spacy-parsed doc, optionally filtering by frequency and dropping leading determiners. Parameters: doc (textacy.Doc or spacy.Doc) – drop_determiners (bool) – remove leading determiners (e.g. “the”) from phrases (e.g. “the quick brown fox” => “quick brown fox”) min_freq (int) – remove chunks that occur in doc fewer than min_freq times spacy.Span – the next noun chunk from doc in order of appearance in the document textacy.extract.pos_regex_matches(doc, pattern)[source] Extract sequences of consecutive tokens from a spacy-parsed doc whose part-of-speech tags match the specified regex pattern. Parameters: doc (textacy.Doc or spacy.Doc or spacy.Span) – pattern (str) – Pattern of consecutive POS tags whose corresponding words are to be extracted, inspired by the regex patterns used in NLTK’s nltk.chunk.regexp. Tags are uppercase, from the universal tag set; delimited by < and >, which are basically converted to parentheses with spaces as needed to correctly extract matching word sequences; white space in the input doesn’t matter. Examples (see constants.POS_REGEX_PATTERNS): noun phrase: r’? (+ )* +’ compound nouns: r’+’ verb phrase: r’?*+’ prepositional phrase: r’ ? (+)* +’ spacy.Span – the next span of consecutive tokens from doc whose parts-of-speech match pattern, in order of apperance textacy.extract.semistructured_statements(doc, entity, cue=u'be', ignore_entity_case=True, min_n_words=1, max_n_words=20)[source] Extract “semi-structured statements” from a spacy-parsed doc, each as a (entity, cue, fragment) triple. This is similar to subject-verb-object triples. Parameters: doc (textacy.Doc or spacy.Doc) – entity (str) – a noun or noun phrase of some sort (e.g. “President Obama”, “global warming”, “Python”) cue (str, optional) – verb lemma with which entity is associated (e.g. “talk about”, “have”, “write”) ignore_entity_case (bool, optional) – if True, entity matching is case-independent min_n_words (int, optional) – min number of tokens allowed in a matching fragment max_n_words (int, optional) – max number of tokens allowed in a matching fragment (spacy.Span or spacy.Token, spacy.Span or spacy.Token, spacy.Span) – where each element is a matching (entity, cue, fragment) triple Notes Inspired by N. Diakopoulos, A. Zhang, A. Salway. Visual Analytics of Media Frames in Online News and Blogs. IEEE InfoVis Workshop on Text Visualization. October, 2013. Which itself was inspired by by Salway, A.; Kelly, L.; Skadiņa, I.; and Jones, G. 2010. Portable Extraction of Partially Structured Facts from the Web. In Proc. ICETAL 2010, LNAI 6233, 345-356. Heidelberg, Springer. textacy.extract.subject_verb_object_triples(doc)[source] Extract an ordered sequence of subject-verb-object (SVO) triples from a spacy-parsed doc. Note that this only works for SVO languages. Parameters: doc (textacy.Doc or spacy.Doc or spacy.Span) – Tuple[spacy.Span, spacy.Span, spacy.Span] – the next 3-tuple of spans from doc representing a (subject, verb, object) triple, in order of appearance textacy.extract.words(doc, filter_stops=True, filter_punct=True, filter_nums=False, include_pos=None, exclude_pos=None, min_freq=1)[source] Extract an ordered sequence of words from a document processed by spaCy, optionally filtering words by part-of-speech tag and frequency. Parameters: doc (textacy.Doc, spacy.Doc, or spacy.Span) – filter_stops (bool) – if True, remove stop words from word list filter_punct (bool) – if True, remove punctuation from word list filter_nums (bool) – if True, remove number-like words (e.g. 10, ‘ten’) from word list include_pos (str or Set[str]) – remove words whose part-of-speech tag IS NOT included in this param exclude_pos (str or Set[str]) – remove words whose part-of-speech tag IS in the specified tags min_freq (int) – remove words that occur in doc fewer than min_freq times spacy.Token – the next token from doc passing specified filters in order of appearance in the document TypeError – if include_pos or exclude_pos is not a str, a set of str, or a falsy value Note Filtering by part-of-speech tag uses the universal POS tag set, http://universaldependencies.org/u/pos/ Functions for unsupervised automatic key term extraction, both specific algorithms (SGRank, TextRank, SingleRank) and a generalization of semantic network-based methods. Also includes a function to aggregate common key term variants of the same idea. textacy.keyterms.aggregate_term_variants(terms, acro_defs=None, fuzzy_dedupe=True)[source] Take a set of unique terms and aggregate terms that are symbolic, lexical, and ordering variants of each other, as well as acronyms and fuzzy string matches. Parameters: terms (Set[str]) – set of unique terms with potential duplicates acro_defs (dict) – if not None, terms that are acronyms will be aggregated with their definitions and terms that are definitions will be aggregated with their acronyms fuzzy_dedupe (bool) – if True, fuzzy string matching will be used to aggregate similar terms of a sufficient length each item is a set of aggregated terms List[Set[str]] Notes Partly inspired by aggregation of variants discussed in Park, Youngja, Roy J. Byrd, and Branimir K. Boguraev. “Automatic glossary extraction: beyond terminology identification.” Proceedings of the 19th international conference on Computational linguistics-Volume 1. Association for Computational Linguistics, 2002. textacy.keyterms.key_terms_from_semantic_network(doc, normalize=u'lemma', window_width=2, edge_weighting=u'binary', ranking_algo=u'pagerank', join_key_words=False, n_keyterms=10, **kwargs)[source] Extract key terms from a document by ranking nodes in a semantic network of terms, connected by edges and weights specified by parameters. Parameters: doc (textacy.Doc or spacy.Doc) – normalize (str or callable) – if ‘lemma’, lemmatize terms; if ‘lower’, lowercase terms; if None, use the form of terms as they appeared in doc; if a callable, must accept a spacy.Token and return a str, e.g. textacy.spacy_utils.normalized_str() window_width (int) – width of sliding window in which term co-occurrences are said to occur edge_weighting ('binary', 'cooc_freq'}) – method used to determine weights of edges between nodes in the semantic network; if ‘binary’, edge weight is set to 1 for any two terms co-occurring within window_width terms; if ‘cooc_freq’, edge weight is set to the number of times that any two terms co-occur ranking_algo ({'pagerank', 'divrank', 'bestcoverage'}) – algorithm with which to rank nodes in the semantic network; pagerank is the canonical (and default) algorithm, but it prioritizes node centrality at the expense of node diversity; the other two attempt to balance centrality with diversity join_key_words (bool) – if True, join consecutive key words together into longer key terms, taking the sum of the constituent words’ scores as the joined key term’s combined score n_keyterms (int or float) – if int, number of top-ranked terms to return as keyterms; if float, must be in the open interval (0, 1), is converted to an integer by round(len(doc) * n_keyterms) sorted list of top n_keyterms key terms and their corresponding ranking scores List[Tuple[str, float]] ValueError – if n_keyterms is a float but not in (0.0, 1.0] textacy.keyterms.most_discriminating_terms(terms_lists, bool_array_grp1, max_n_terms=1000, top_n_terms=25)[source] Given a collection of documents assigned to 1 of 2 exclusive groups, get the top_n_terms most discriminating terms for group1-and-not-group2 and group2-and-not-group1. Parameters: terms_lists (Iterable[Iterable[str]]) – a sequence of documents, each as a sequence of (str) terms; used as input to doc_term_matrix() bool_array_grp1 (Iterable[bool]) – an ordered sequence of True/False values, where True corresponds to documents falling into “group 1” and False corresponds to those in “group 2” max_n_terms (int) – only consider terms whose document frequency is within the top max_n_terms out of all distinct terms; must be > 0 top_n_terms (int or float) – if int (must be > 0), the total number of most discriminating terms to return for each group; if float (must be in the interval (0, 1)), the fraction of max_n_terms to return for each group top top_n_terms most discriminating terms for grp1-not-grp2 List[str]: top top_n_terms most discriminating terms for grp2-not-grp1 List[str] References King, Gary, Patrick Lam, and Margaret Roberts. “Computer-Assisted Keyword and Document Set Discovery from Unstructured Text.” (2014). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.458.1445&rep=rep1&type=pdf textacy.keyterms.rank_nodes_by_bestcoverage(graph, k, c=1, alpha=1.0)[source] Rank nodes in a network using the [BestCoverage] algorithm that attempts to balance between node centrality and diversity. Parameters: graph (networkx.Graph) – k (int) – number of results to return for top-k search c (int) – l parameter for l-step expansion; best if 1 or 2 alpha (float) – float in [0.0, 1.0] specifying how much of central vertex’s score to remove from its l-step neighbors; smaller value puts more emphasis on centrality, larger value puts more emphasis on diversity top k nodes as ranked by bestcoverage algorithm; keys as node identifiers, values as corresponding ranking scores dict References  [BestCoverage] Küçüktunç, O., Saule, E., Kaya, K., & Çatalyürek, Ü. V. (2013, May). Diversified recommendation on graphs: pitfalls, measures, and algorithms. In Proceedings of the 22nd international conference on World Wide Web (pp. 715-726). International World Wide Web Conferences Steering Committee. http://www2013.wwwconference.org/proceedings/p715.pdf textacy.keyterms.rank_nodes_by_divrank(graph, r=None, lambda_=0.5, alpha=0.5)[source] Rank nodes in a network using the [DivRank] algorithm that attempts to balance between node centrality and diversity. Parameters: graph (networkx.Graph) – r (numpy.array,) – the “personalization vector”; by default, r = ones(1, n)/n lambda (float) – must be in [0.0, 1.0] alpha (float) – controls the strength of self-links; must be in [0.0, 1.0] list of (node, score) tuples ordered by desc. divrank score List[Tuple[str, float]] References  [DivRank] Mei, Q., Guo, J., & Radev, D. (2010, July). Divrank: the interplay of prestige and diversity in information networks. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1009-1018). ACM. http://clair.si.umich.edu/~radev/papers/SIGKDD2010.pdf textacy.keyterms.sgrank(doc, ngrams=(1, 2, 3, 4, 5, 6), normalize=u'lemma', window_width=1500, n_keyterms=10, idf=None)[source] Extract key terms from a document using the [SGRank] algorithm. Parameters: doc (textacy.Doc or spacy.Doc) – ngrams (int or Set[int]) – n of which n-grams to include; (1, 2, 3, 4, 5, 6) (default) includes all ngrams from 1 to 6; 2 if only bigrams are wanted normalize (str or callable) – If ‘lemma’, lemmatize terms; if ‘lower’, lowercase terms; if None, use the form of terms as they appeared in doc; if a callable, must accept a spacy.Span and return a str, e.g. textacy.spacy_utils.normalized_str() window_width (int) – Width of sliding window in which term co-occurrences are determined to occur. Note: Larger values may dramatically increase runtime, owing to the larger number of co-occurrence combinations that must be counted. n_keyterms (int or float) – Number of top-ranked terms to return as keyterms. If int, represents the absolute number; if float, must be in the open interval (0.0, 1.0), and is converted to an integer by int(round(len(doc) * n_keyterms)) idf (dict) – Mapping of normalize(term) to inverse document frequency for re-weighting of unigrams (n-grams with n > 1 have df assumed = 1). NOTE: Results are typically better with idf information. sorted list of top n_keyterms key terms and their corresponding SGRank scores List[Tuple[str, float]] ValueError – If n_keyterms is a float but not in (0.0, 1.0] or window_width < 2. References  [SGRank] Danesh, Sumner, and Martin. “SGRank: Combining Statistical and Graphical Methods to Improve the State of the Art in Unsupervised Keyphrase Extraction”. Lexical and Computational Semantics (* SEM 2015) (2015): 117. textacy.keyterms.singlerank(doc, normalize=u'lemma', n_keyterms=10)[source] Convenience function for calling key_terms_from_semantic_network with the parameter values used in the [SingleRank] algorithm. Parameters: doc (textacy.Doc or spacy.Doc) – normalize (str or callable) – if ‘lemma’, lemmatize terms; if ‘lower’, lowercase terms; if None, use the form of terms as they appeared in doc; if a callable, must accept a spacy.Token and return a str, e.g. textacy.spacy_utils.normalized_str() n_keyterms (int or float) – if int, number of top-ranked terms to return as keyterms; if float, must be in the open interval (0, 1), representing the fraction of top-ranked terms to return as keyterms References  [SingleRank] Hasan, K. S., & Ng, V. (2010, August). Conundrums in unsupervised keyphrase extraction: making sense of the state-of-the-art. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters (pp. 365-373). Association for Computational Linguistics. textacy.keyterms.textrank(doc, normalize=u'lemma', n_keyterms=10)[source] Convenience function for calling key_terms_from_semantic_network with the parameter values used in the [TextRank] algorithm. Parameters: doc (textacy.Doc or spacy.Doc) – normalize (str or callable) – if ‘lemma’, lemmatize terms; if ‘lower’, lowercase terms; if None, use the form of terms as they appeared in doc; if a callable, must accept a spacy.Token and return a str, e.g. textacy.spacy_utils.normalized_str() n_keyterms (int or float) – if int, number of top-ranked terms to return as keyterms; if float, must be in the open interval (0, 1), representing the fraction of top-ranked terms to return as keyterms References  [TextRank] Mihalcea, R., & Tarau, P. (2004, July). TextRank: Bringing order into texts. Association for Computational Linguistics. Topic Modeling¶ Convenient and consolidated topic-modeling, built on scikit-learn. class textacy.tm.topic_model.TopicModel(model, n_topics=10, **kwargs)[source] Train and apply a topic model to vectorized texts using scikit-learn’s implementations of LSA, LDA, and NMF models. Inspect and visualize results. Save and load trained models to and from disk. Prepare a vectorized corpus (i.e. document-term matrix) and corresponding vocabulary (i.e. mapping of term strings to column indices in the matrix). See textacy.vsm.Vectorizer for details. In short: >>> vectorizer = Vectorizer( ... weighting='tfidf', normalize=True, smooth_idf=True, ... min_df=3, max_df=0.95, max_n_terms=100000) >>> doc_term_matrix = vectorizer.fit_transform(terms_list)  Initialize and train a topic model: >>> model = textacy.tm.TopicModel('nmf', n_topics=20) >>> model.fit(doc_term_matrix) >>> model TopicModel(n_topics=10, model=NMF)  Transform the corpus and interpret our model: >>> doc_topic_matrix = model.transform(doc_term_matrix) >>> for topic_idx, top_terms in model.top_topic_terms(vectorizer.id_to_term, topics=[0,1]): ... print('topic', topic_idx, ':', ' '.join(top_terms)) topic 0 : people american go year work think$   today   money   america
topic 1 : rescind   quorum   order   unanimous   consent   ask   president   mr.   madam   absence
>>> for topic_idx, top_docs in model.top_topic_docs(doc_topic_matrix, topics=[0,1], top_n=2):
...     print(topic_idx)
...     for j in top_docs:
0
THE MOST IMPORTANT ISSUES FACING THE AMERICAN PEOPLE
55TH ANNIVERSARY OF THE BATTLE OF CRETE
1
CHEMICAL WEAPONS CONVENTION
MFN STATUS FOR CHINA
>>> for doc_idx, topics in model.top_doc_topics(doc_topic_matrix, docs=range(5), top_n=2):
JOIN THE SENATE AND PASS A CONTINUING RESOLUTION : (9, 0)
MEETING THE CHALLENGE : (2, 0)
DISPOSING OF SENATE AMENDMENT TO H.R. 1643, EXTENSION OF MOST-FAVORED- NATION TREATMENT FOR BULGARIA : (0, 9)
EXAMINING THE SPEAKER'S UPCOMING TRAVEL SCHEDULE : (0, 9)
FLOODING IN PENNSYLVANIA : (0, 9)
>>> for i, val in enumerate(model.topic_weights(doc_topic_matrix)):
...     print(i, val)
0 0.302796022302
1 0.0635617650602
2 0.0744927472417
3 0.0905778808867
4 0.0521162262192
5 0.0656303769725
6 0.0973516532757
7 0.112907245542
8 0.0680659204364
9 0.0725001620636


Visualize the model:

>>> model.termite_plot(doc_term_matrix, vectorizer.id_to_term,
...                    topics=-1,  n_terms=25, sort_terms_by='seriation')


Persist our topic model to disk:

>>> model.save('nmf-10topics.pkl')

Parameters: model ({‘nmf’, ‘lda’, ‘lsa’} or sklearn.decomposition.) – n_topics (int) – number of topics in the model to be initialized **kwargs – variety of parameters used to initialize the model; see individual sklearn pages for full details ValueError – if model not in {'nmf', 'lda', 'lsa'} or is not an NMF, LatentDirichletAllocation, or TruncatedSVD instance
get_doc_topic_matrix(doc_term_matrix, normalize=True)[source]

Transform a document-term matrix into a document-topic matrix, where rows correspond to documents and columns to the topics in the topic model.

Parameters: doc_term_matrix (array-like or sparse matrix) – corpus represented as a document-term matrix with shape (n_docs, n_terms); NOTE: LDA expects tf-weighting, while NMF and LSA may do better with tfidf-weighting! normalize (bool) – if True, the values in each row are normalized, i.e. topic weights on each document sum to 1 document-topic matrix with shape (n_docs, n_topics) numpy.ndarray
termite_plot(doc_term_matrix, id2term, topics=-1, sort_topics_by=u'index', highlight_topics=None, n_terms=25, rank_terms_by=u'topic_weight', sort_terms_by=u'seriation', save=False)[source]

Make a “termite” plot for assessing topic models using a tabular layout to promote comparison of terms both within and across topics.

Parameters: doc_term_matrix (np.ndarray-like or sparse matrix) – corpus represented as a document-term matrix with shape (n_docs, n_terms); may have tf- or tfidf-weighting id2term (List[str] or dict) – object that returns the term string corresponding to term id i through id2term[i]; could be a list of strings where the index represents the term id, such as that returned by sklearn.feature_extraction.text.CountVectorizer.get_feature_names(), or a mapping of term id: term string topics (int or Sequence[int]) – topic(s) to include in termite plot; if -1, all topics are included sort_topics_by ({'index', 'weight'}) – highlight_topics (int or Sequence[int]) – indices for up to 6 topics to visually highlight in the plot with contrasting colors n_terms (int) – number of top terms to include in termite plot rank_terms_by ({'topic_weight', 'corpus_weight'}) – value used to rank terms; the top-ranked n_terms are included in the plot sort_terms_by ({'seriation', 'weight', 'index', 'alphabetical'}) – method used to vertically sort the selected top n_terms terms; the default (“seriation”) groups similar terms together, which facilitates cross-topic assessment save (str) – give the full /path/to/fname on disk to save figure axis on which termite plot is plotted matplotlib.axes.Axes.axis ValueError – if more than 6 topics are selected for highlighting, or an invalid value is passed for the sort_topics_by, rank_terms_by, and/or sort_terms_by params

References

viz.termite_plot

TODO: rank_terms_by other metrics, e.g. topic salience or relevance

top_doc_topics(doc_topic_matrix, docs=-1, top_n=3, weights=False)[source]

Get the top top_n topics by weight per doc for docs in doc_topic_matrix.

Parameters: doc_topic_matrix (numpy.ndarray) – document-topic matrix with shape (n_docs, n_topics), the result of calling get_doc_topic_matrix() docs (int or Sequence[int]) – docs for which to return top topics; if -1, all docs’ top topics are returned top_n (int) – number of top topics to return per doc weights (bool) – if True, docs are returned with their corresponding (normalized) topic weights; otherwise, docs are returned without weights Tuple[int, Tuple[int]] or Tuple[int, Tuple[Tuple[int, float]]] – next tuple corresponding to a doc; the first element is the doc’s index; if weights is False, the second element is a tuple of ints representing the top top_n related topics; otherwise, the second is a tuple of (int, float) pairs representing the top top_n related topics and their associated weights wrt the doc; for example: >>> list(TopicModel.top_doc_topics(dtm, docs=(0, 1), top_n=2, weights=False)) [(0, (1, 4)), (1, (3, 2))] >>> list(TopicModel.top_doc_topics(dtm, docs=0, top_n=2, weights=True)) [(0, ((1, 0.2855), (4, 0.2412)))] 
top_topic_docs(doc_topic_matrix, topics=-1, top_n=10, weights=False)[source]

Get the top top_n docs by weight per topic in doc_topic_matrix.

Parameters: doc_topic_matrix (numpy.ndarray) – document-topic matrix with shape (n_docs, n_topics), the result of calling get_doc_topic_matrix() topics (int or Sequence[int]) – topic(s) for which to return top docs; if -1, all topics’ docs are returned top_n (int) – number of top docs to return per topic weights (bool) – if True, docs are returned with their corresponding (normalized) topic weights; otherwise, docs are returned without weights Tuple[int, Tuple[int]] or Tuple[int, Tuple[Tuple[int, float]]] – next tuple corresponding to a topic; the first element is the topic’s index; if weights is False, the second element is a tuple of ints representing the top top_n related docs; otherwise, the second is a tuple of (int, float) pairs representing the top top_n related docs and their associated weights wrt the topic; for example: >>> list(TopicModel.top_doc_terms(dtm, topics=(0, 1), top_n=2, weights=False)) [(0, (4, 2)), (1, (1, 3))] >>> list(TopicModel.top_doc_terms(dtm, topics=0, top_n=2, weights=True)) [(0, ((4, 0.3217), (2, 0.2154)))] 
top_topic_terms(id2term, topics=-1, top_n=10, weights=False)[source]

Get the top top_n terms by weight per topic in model.

Parameters: id2term (list(str) or dict) – object that returns the term string corresponding to term id i through id2term[i]; could be a list of strings where the index represents the term id, such as that returned by sklearn.feature_extraction.text.CountVectorizer.get_feature_names(), or a mapping of term id: term string topics (int or Sequence[int]) – topic(s) for which to return top terms; if -1 (default), all topics’ terms are returned top_n (int) – number of top terms to return per topic weights (bool) – if True, terms are returned with their corresponding topic weights; otherwise, terms are returned without weights Tuple[int, Tuple[str]] or Tuple[int, Tuple[Tuple[str, float]]] – next tuple corresponding to a topic; the first element is the topic’s index; if weights is False, the second element is a tuple of str representing the top top_n related terms; otherwise, the second is a tuple of (str, float) pairs representing the top top_n related terms and their associated weights wrt the topic; for example: >>> list(TopicModel.top_topic_terms(id2term, topics=(0, 1), top_n=2, weights=False)) [(0, ('foo', 'bar')), (1, ('bat', 'baz'))] >>> list(TopicModel.top_topic_terms(id2term, topics=0, top_n=2, weights=True)) [(0, (('foo', 0.1415), ('bar', 0.0986)))] 
topic_weights(doc_topic_matrix)[source]

Get the overall weight of topics across an entire corpus. Note: Values depend on whether topic weights per document in doc_topic_matrix were normalized, or not. I suppose either way makes sense... o_O

Parameters: doc_topic_matrix (numpy.ndarray) – document-topic matrix with shape (n_docs, n_topics), the result of calling get_doc_topic_matrix() the ith element is the ith topic’s overall weight numpy.ndarray

Representations¶

Represent documents as semantic networks, where nodes are individual terms or whole sentences.

textacy.network.sents_to_semantic_network(sents, normalize='lemma', edge_weighting='cosine')[source]

Convert a list of sentences into a semantic network, where each sentence is represented by a node with edges linking it to other sentences weighted by the (cosine or jaccard) similarity of their constituent words.

Parameters: sents (List[str] or List[spacy.Span]) – normalize (str or callable) – if ‘lemma’, lemmatize words in sents; if ‘lower’, lowercase word in sents; if false-y, use the form of words as they appear in sents; if a callable, must accept a spacy.Token and return a str, e.g. textacy.spacy_utils.normalized_str(); only applicable if sents is a List[spacy.Span] edge_weighting (str {'cosine', 'jaccard'}, optional) – similarity metric to use for weighting edges between sentences; if ‘cosine’, use the cosine similarity between sentences represented as tf-idf word vectors; if ‘jaccard’, use the set intersection divided by the set union of all words in a given sentence pair nodes are the integer indexes of the sentences in the input sents list, not the actual text of the sentences! networkx.Graph

Notes

• If passing sentences as strings, be sure to filter out stopwords, punctuation, certain parts of speech, etc. beforehand
• Consider normalizing the strings so that like terms are counted together (see normalized_str())
textacy.network.terms_to_semantic_network(terms, normalize='lemma', window_width=10, edge_weighting='cooc_freq')[source]

Convert an ordered list of non-overlapping terms into a semantic network, where each term is represented by a node with edges linking it to other terms that co-occur within window_width terms of itself.

Parameters: terms (List[str] or List[spacy.Token]) – normalize (str or callable) – if ‘lemma’, lemmatize terms; if ‘lower’, lowercase terms; if false-y, use the form of terms as they appear in doc; if a callable, must accept a spacy.Token and return a str, e.g. textacy.spacy_utils.normalized_str(); only applicable if terms is a List[spacy.Token] window_width (int, optional) – size of sliding window over terms that determines which are said to co-occur; if = 2, only adjacent terms will have edges in network edge_weighting (str {'cooc_freq', 'binary'}, optional) – if ‘binary’, all co-occurring terms will have network edges with weight = 1; if ‘cooc_freq’, edges will have a weight equal to the number of times that the connected nodes co-occur in a sliding window nodes are terms, edges are for co-occurrences of terms networkx.Graph

Notes

• Be sure to filter out stopwords, punctuation, certain parts of speech, etc. from the terms list before passing it to this function
• Multi-word terms, such as named entities and compound nouns, must be merged into single strings or spacy.Tokens beforehand
• If terms are already strings, be sure to have normalized them so that like terms are counted together; for example, by applying normalized_str()

Represent a collection of spacy-processed texts as a document-term matrix of shape (# docs, # unique terms), with a variety of filtering, normalization, and term weighting schemes for the values.

class textacy.vsm.Vectorizer(weighting=u'tf', normalize=False, sublinear_tf=False, smooth_idf=True, vocabulary=None, min_df=1, max_df=1.0, min_ic=0.0, max_n_terms=None)[source]

Transform one or more tokenized documents into a document-term matrix of shape (# docs, # unique terms), with tf-, tf-idf, or binary-weighted values.

Stream a corpus with metadata from disk:

>>> cw = textacy.datasets.CapitolWords()
...     cw.records(limit=1000), 'text', itemwise=False)
>>> corpus
Corpus(1000 docs; 537742 tokens)


Tokenize and vectorize (the first half of) a corpus:

>>> terms_list = (doc.to_terms_list(ngrams=1, named_entities=True, as_strings=True)
for doc in corpus[:500])
>>> vectorizer = Vectorizer(
...     weighting='tfidf', normalize=True, smooth_idf=True,
...     min_df=3, max_df=0.95, max_n_terms=100000)
>>> doc_term_matrix = vectorizer.fit_transform(terms_list)
>>> doc_term_matrix
<500x3811 sparse matrix of type '<class 'numpy.float64'>'
with 54530 stored elements in Compressed Sparse Row format>


Tokenize and vectorize (the other half of) a corpus, using only the terms and weights learned in the previous step:

>>> terms_list = (doc.to_terms_list(ngrams=1, named_entities=True, as_strings=True)
...               for doc in corpus[:500])
>>> doc_term_matrix = vectorizer.transform(terms_list)
>>> doc_term_matrix
<500x3811 sparse matrix of type '<class 'numpy.float64'>'
with 44788 stored elements in Compressed Sparse Row format>

Parameters: weighting ({'tf', 'tfidf', 'binary'}) – Weighting to assign to terms in the doc-term matrix. If ‘tf’, matrix values (i, j) correspond to the number of occurrences of term j in doc i; if ‘tfidf’, term frequencies (tf) are multiplied by their corresponding inverse document frequencies (idf); if ‘binary’, all non-zero values are set equal to 1. normalize (bool) – If True, normalize term frequencies by the L2 norms of the vectors. binarize (bool) – If True, set all term frequencies > 0 equal to 1. sublinear_tf (bool) – If True, apply sub-linear term-frequency scaling, i.e. tf => 1 + log(tf). smooth_idf (bool) – If True, add 1 to all document frequencies, equivalent to adding a single document to the corpus containing every unique term. vocabulary (Dict[str, int] or Iterable[str]) – Mapping of unique term string (str) to unique term id (int) or an iterable of term strings (which gets converted into a suitable mapping). min_df (float or int) – If float, value is the fractional proportion of the total number of documents, which must be in [0.0, 1.0]. If int, value is the absolute number. Filter terms whose document frequency is less than min_df. max_df (float or int) – If float, value is the fractional proportion of the total number of documents, which must be in [0.0, 1.0]. If int, value is the absolute number. Filter terms whose document frequency is greater than max_df. min_ic (float) – Filter terms whose information content is less than min_ic; value must be in [0.0, 1.0]. max_n_terms (int) – Only include terms whose document frequency is within the top max_n_terms.
vocabulary

Dict[str, int]

is_fixed_vocabulary

bool

id_to_term

Dict[int, str]

feature_names

List[str]

feature_names

Array mapping from feature integer indices to feature name.

fit(terms_list)[source]

Count terms and build up a vocabulary based on the terms found in the terms_list.

Parameters: terms_list (Iterable[Iterable[str]]) – A sequence of tokenized documents, where each document is a sequence of (str) terms. For example: >>> ([tok.lemma_ for tok in spacy_doc]  ... for spacy_doc in spacy_docs) >>> ((ne.text for ne in extract.named_entities(doc)) ... for doc in corpus) >>> (tuple(ng.text for ng in itertools.chain.from_iterable(extract.ngrams(doc, i) for i in range(1, 3))) ... for doc in docs) The instance that has just been fit. Vectorizer
fit_transform(terms_list)[source]

Count terms and build up a vocabulary based on the terms found in the terms_list, then transform the terms_list into a document-term matrix with values weighted according to the parameters specified in Vectorizer initialization.

Parameters: terms_list (Iterable[Iterable[str]]) – A sequence of tokenized documents, where each document is a sequence of (str) terms. For example: >>> ([tok.lemma_ for tok in spacy_doc]  ... for spacy_doc in spacy_docs) >>> ((ne.text for ne in extract.named_entities(doc)) ... for doc in corpus) >>> (tuple(ng.text for ng in itertools.chain.from_iterable(extract.ngrams(doc, i) for i in range(1, 3))) ... for doc in docs) The transformed document-term matrix. Rows correspond to documents and columns correspond to terms. scipy.sparse.csr_matrix
id_to_term

dict – Mapping of unique term id (int) to unique term string (str), i.e. the inverse of Vectorizer.vocabulary. This attribute is only generated if needed, and it is automatically kept in sync with the corresponding vocabulary.

transform(terms_list)[source]

Transform the terms_list into a document-term matrix with values weighted according to the parameters specified in Vectorizer initialization.

Parameters: terms_list (Iterable[Iterable[str]]) – A sequence of tokenized documents, where each document is a sequence of (str) terms. For example: >>> ([tok.lemma_ for tok in spacy_doc]  ... for spacy_doc in spacy_docs) >>> ((ne.text for ne in extract.named_entities(doc)) ... for doc in corpus) >>> (tuple(ng.text for ng in itertools.chain.from_iterable(extract.ngrams(doc, i) for i in range(1, 3))) ... for doc in docs) The transformed document-term matrix. Rows correspond to documents and columns correspond to terms. scipy.sparse.csr_matrix

Note

This requires an existing vocabulary, either built when calling Vectorizer.fit() or provided in Vectorizer initialization.

textacy.vsm.apply_idf_weighting(doc_term_matrix, smooth_idf=True)[source]

Apply inverse document frequency (idf) weighting to a term-frequency (tf) weighted document-term matrix, optionally smoothing idf values.

Parameters: doc_term_matrix (scipy.sparse.csr_matrix
textacy.vsm.filter_terms_by_df(doc_term_matrix, term_to_id, max_df=1.0, min_df=1, max_n_terms=None)[source]

Filter out terms that are too common and/or too rare (by document frequency), and compactify the top max_n_terms in the id_to_term mapping accordingly. Borrows heavily from the sklearn.feature_extraction.text module.

Parameters: doc_term_matrix (scipy.sparse.csr_matrix) – M X N matrix, where M is the # of docs and N is the # of unique terms. term_to_id (Dict[str, int]) – Mapping of term string to unique term id, e.g. Vectorizer.vocabulary. min_df (float or int) – if float, value is the fractional proportion of the total number of documents and must be in [0.0, 1.0]; if int, value is the absolute number; filter terms whose document frequency is less than min_df max_df (float or int) – if float, value is the fractional proportion of the total number of documents and must be in [0.0, 1.0]; if int, value is the absolute number; filter terms whose document frequency is greater than max_df max_n_terms (int) – only include terms whose term frequency is within the top max_n_terms sparse matrix of shape (# docs, # unique filtered terms), where value (i, j) is the weight of term j in doc i dict: id to term mapping, where keys are unique filtered integers as term ids and values are corresponding strings scipy.sparse.csr_matrix ValueError – if max_df or min_df or max_n_terms < 0
textacy.vsm.filter_terms_by_ic(doc_term_matrix, term_to_id, min_ic=0.0, max_n_terms=None)[source]

Filter out terms that are too common and/or too rare (by information content), and compactify the top max_n_terms in the id_to_term mapping accordingly. Borrows heavily from the sklearn.feature_extraction.text module.

Parameters: doc_term_matrix (scipy.sparse.csr_matrix) – M X N matrix, where M is the # of docs and N is the # of unique terms. term_to_id (Dict[str, int]) – Mapping of term string to unique term id, e.g. Vectorizer.vocabulary. min_ic (float) – filter terms whose information content is less than this value; must be in [0.0, 1.0] max_n_terms (int) – only include terms whose information content is within the top max_n_terms sparse matrix of shape (# docs, # unique filtered terms), where value (i, j) is the weight of term j in doc i dict: id to term mapping, where keys are unique filtered integers as term ids and values are corresponding strings scipy.sparse.csr_matrix ValueError – if min_ic not in [0.0, 1.0] or max_n_terms < 0
textacy.vsm.get_doc_freqs(doc_term_matrix, normalized=True)[source]

Compute absolute or relative document frequencies for all terms in a term-document matrix.

Parameters: doc_term_matrix (scipy.sparse.csr_matrix
textacy.vsm.get_information_content(doc_term_matrix)[source]

Compute information content for all terms in a term-document matrix. IC is a float in [0.0, 1.0], defined as -df * log2(df) - (1 - df) * log2(1 - df), where df is a term’s normalized document frequency.

Parameters: doc_term_matrix (scipy.sparse.csr_matrix
textacy.vsm.get_term_freqs(doc_term_matrix, normalized=True)[source]

Compute absolute or relative term frequencies for all terms in a document-term matrix.

Parameters: doc_term_matrix (scipy.sparse.csr_matrix

Datasets¶

Capitol Words¶

A collection of ~11k (almost all) speeches given by the main protagonists of the 2016 U.S. Presidential election that had previously served in the U.S. Congress — including Hillary Clinton, Bernie Sanders, Barack Obama, Ted Cruz, and John Kasich — from January 1996 through June 2016.

Records include the following fields:

• text: full text of the Congressperson’s remarks
• title: title of the speech, in all caps
• date: date on which the speech was given, as an ISO-standard string
• speaker_name: first and last name of the speaker
• speaker_party: political party of the speaker (‘R’ for Republican, ‘D’ for Democrat, and ‘I’ for Independent)
• congress: number of the Congress in which the speech was given; ranges continuously between 104 and 114
• chamber: chamber of Congress in which the speech was given; almost all are either ‘House’ or ‘Senate’, with a small number of ‘Extensions’

This dataset was derived from data provided by the (now defunct) Sunlight Foundation’s Capitol Words API.

class textacy.datasets.capitol_words.CapitolWords(data_dir=u'/home/docs/checkouts/readthedocs.org/user_builds/textacy/envs/stable/local/lib/python2.7/site-packages/textacy-0.4.1-py2.7.egg/textacy/data/capitol_words')[source]

Stream Congressional speeches from a compressed json file on disk, either as texts (str) or records (dict) with both text content and metadata.

>>> cw = CapitolWords()
>>> cw.info
{'data_dir': 'path/to/textacy/data/capitolwords',
'description': 'Collection of ~11k speeches in the Congressional Record given by notable U.S. politicians between Jan 1996 and Jun 2016.',
'name': 'capitolwords',
'site_url': 'http://sunlightlabs.github.io/Capitol-Words/'}


Iterate over speeches as plain texts or records with both text and metadata:

>>> for text in cw.texts(limit=5):
...     print(text)
>>> for record in cw.records(limit=5):
...     print(record['title'], record['date'])
...     print(record['text'])


Filter speeches by a variety of metadata fields and text length:

>>> for record in cw.records(speaker_name='Bernie Sanders', limit=1):
...     print(record['date'], record['text'])
>>> for record in cw.records(speaker_party='D', congress={110, 111, 112},
...                          chamber='Senate', limit=5):
...     print(record['speaker_name'], record['title'])
>>> for record in cw.records(speaker_name={'Barack Obama', 'Hillary Clinton'},
...                          date_range=('2002-01-01', '2002-12-31')):
...     print(record['speaker_name'], record['title'], record['date'])
>>> for text in cw.texts(min_len=50000):
...     print(len(text))


Stream speeches into a textacy.Corpus:

>>> text_stream, metadata_stream = textacy.fileio.split_record_fields(
...     cw.records(limit=100), 'text')
>>> c
Corpus(100 docs; 70500 tokens)

Parameters: data_dir (str) – Path to directory on disk under which compressed json files are stored.
min_date

str – Earliest date for which speeches are available, as an ISO-formatted string (YYYY-MM-DD).

max_date

str – Latest date for which speeches are available, as an ISO-formatted string (YYYY-MM-DD).

speaker_names

Set[str] – full names of all speakers included in corpus, e.g. ‘Bernie Sanders’

speaker_parties

Set[str] – all distinct political parties of speakers, e.g. ‘R’

chambers

Set[str] – all distinct chambers in which speeches were given, e.g. ‘House’

congresses

Set[int] – all distinct numbers of the congresses in which speeches were given, e.g. 114

download(force=False)[source]

Download a Python version-specific compressed json file from s3, and save it to disk under the data_dir directory.

filename

str – Full path on disk for CapitolWords data as compressed json file. None if file is not found, e.g. has not yet been downloaded.

records(speaker_name=None, speaker_party=None, chamber=None, congress=None, date_range=None, min_len=None, limit=-1)[source]

Iterate over records (including text and metadata) in this dataset, optionally filtering by a variety of metadata and/or text length, in chronological order.

Parameters: speaker_name (str or Set[str]) – Filter records by the speakers’ name; see speaker_names. speaker_party (str or Set[str]) – Filter records by the speakers’ party; see speaker_parties. chamber (str or Set[str]) – Filter records by the chamber in which they were given; see chambers. congress (int or Set[int]) – Filter records by the congress in which they were given; see congresses. date_range (List[str] or Tuple[str]) – Filter records by the date on which they were given. Both start and end date must be specified, but a null value for either will be replaced by the min/max date available in the dataset. min_len (int) – Filter records by the length (number of characters) of their text content. limit (int) – Return no more than limit records, in chronological order. dict – Full text and metadata of next (by chronological order) record in dataset passing all filter params. ValueError – If any filtering options are invalid.
texts(speaker_name=None, speaker_party=None, chamber=None, congress=None, date_range=None, min_len=None, limit=-1)[source]

Iterate over texts in this dataset, optionally filtering by a variety of metadata and/or text length, in chronological order.

Parameters: speaker_name (str or Set[str]) – Filter texts by the speakers’ name; see speaker_names. speaker_party (str or Set[str]) – Filter texts by the speakers’ party; see speaker_parties. chamber (str or Set[str]) – Filter texts by the chamber in which they were given; see chambers. congress (int or Set[int]) – Filter texts by the congress in which they were given; see congresses. date_range (List[str] or Tuple[str]) – Filter texts by the date on which they were given. Both start and end date must be specified, but a null value for either will be replaced by the min/max date available in the dataset. min_len (int) – Filter texts by the length (number of characters) of their text content. limit (int) – Return no more than limit texts, chronological order. str – Full text of next (by chronological order) text in dataset passing all filter params. ValueError – If any filtering options are invalid.

Supreme Court Decisions¶

A collection of ~8.4k (almost all) decisions issued by the U.S. Supreme Court from November 1946 through June 2016 — the “modern” era.

Records include the following fields:

• text: full text of the Court’s decision
• case_name: name of the court case, in all caps
• argument_date: date on which the case was argued before the Court, as a string with format ‘YYYY-MM-DD’
• decision_date: date on which the Court’s decision was announced, as a string with format ‘YYYY-MM-DD’
• decision_direction: ideological direction of the majority decision; either ‘conservative’, ‘liberal’, or ‘unspecifiable’
• maj_opinion_author: name of the majority opinion’s author, if available and identifiable, as an integer code whose mapping is given in SupremeCourt.opinion_author_codes
• n_maj_votes: number of justices voting in the majority
• n_min_votes: number of justices voting in the minority
• issue: subject matter of the case’s core disagreement (e.g. affirmative action) rather than its legal basis (e.g. the equal protection clause), as a string code whose mapping is given in SupremeCourt.issue_codes
• issue_area: higher-level categorization of the issue (e.g. Civil Rights), as an integer code whose mapping is given in SupremeCourt.issue_area_codes
• us_cite_id: citation identifier for each case according to the official United States Reports; Note: There are ~300 cases with duplicate ids, and it’s not clear if that’s “correct” or a data quality problem

The text in this dataset was derived from FindLaw’s searchable database of court cases: http://caselaw.findlaw.com/court/us-supreme-court

The metadata was extracted without modification from the Supreme Court Database: Harold J. Spaeth, Lee Epstein, et al. 2016 Supreme Court Database, Version 2016 Release 1. http://supremecourtdatabase.org. Its license is CC BY-NC 3.0 US: https://creativecommons.org/licenses/by-nc/3.0/us/

This corpus’ creation was inspired by a blog post by Emily Barry: http://www.emilyinamillion.me/blog/2016/7/13/visualizing-supreme-court-topics-over-time

NOTE: The two datasets were merged through much munging and a carefully trained model using the dedupe package. The model’s duplicate threshold was set so as to maximize the F-score where precision had twice as much weight as recall. Still, given occasionally baffling inconsistencies in case naming, citation ids, and decision dates, a very small percentage of texts may be incorrectly matched to metadata. Sorry.

class textacy.datasets.supreme_court.SupremeCourt(data_dir=u'/home/docs/checkouts/readthedocs.org/user_builds/textacy/envs/stable/local/lib/python2.7/site-packages/textacy-0.4.1-py2.7.egg/textacy/data/supreme_court')[source]

Stream U.S. Supreme Court decisions from a compressed json file on disk, either as texts (str) or records (dict) with both text content and metadata.

>>> sc = SupremeCourt()
>>> sc.info
{'data_dir': 'path/to/textacy/data/supreme_court',
'description': 'Collection of ~8.4k decisions issued by the U.S. Supreme Court between November 1946 and June 2016.',
'name': 'supreme_court',
'site_url': 'http://caselaw.findlaw.com/court/us-supreme-court'}


Iterate over decisions as plain texts or records with both text and metadata:

>>> for text in sc.texts(limit=1):
...     print(text)
>>> for record in sc.records(limit=1):
...     print(record['case_name'], record['decision_date'])
...     print(record['text'])


Filter decisions by a variety of metadata fields and text length:

>>> for record in sc.records(opinion_author=109, limit=1):  # Notorious RBG!
>>> for record in sc.records(decision_direction='liberal',
...                          issue_area={1, 9, 10}, limit=10):
>>> for record in sc.records(opinion_author=102,
...                          date_range=('1990-01-01', '1999-12-31')):
...     print(record['case_name'], record['decision_date'])
...     print(sc.issue_codes[record['issue']])
>>> for text in sc.texts(min_len=50000):
...     print(len(text))


Stream decisions into a textacy.Corpus:

>>> text_stream, metadata_stream = textacy.fileio.split_record_fields(
...     sc.records(limit=100), 'text')
>>> c
Corpus(100 docs; 615135 tokens)

Parameters: data_dir (str) – path on disk containing corpus data; if None, textacy’s default data_dir is used
min_date

str – Earliest date for which decisions are available, as an ISO-formatted string (YYYY-MM-DD).

max_date

str – Latest date for which decisions are available, as an ISO-formatted string (YYYY-MM-DD).

decision_directions

set[str] – all distinct decision directions, e.g. ‘liberal’

opinion_author_codes

dict – mapping of majority opinion authors from integer code to (str) full name

issue_area_codes

dict – mapping of high-level issue area of the case’s core disagreement from integer code to (str) description

issue_codes

dict – mapping of specific issue of the case’s core disagreement from integer code to (str) description

download(force=False)[source]

Download a Python version-specific compressed json file from s3, and save it to disk under the data_dir directory.

filename

str – Full path on disk for SupremeCourt data as compressed json file. None if file is not found, e.g. has not yet been downloaded.

records(opinion_author=None, issue_area=None, decision_direction=None, date_range=None, min_len=None, limit=-1)[source]

Iterate over documents (including text and metadata) in the SupremeCourt corpus, optionally filtering by a variety of metadata and/or text length, in order of decision date.

Parameters: opinion_author (int or set[int]) – filter cases by the name(s) of the majority opinion’s author, coded as an integer whose mapping is given in opinion_author_codes issue_area (int or set[int]) – filter cases by the issue area of the case’s subject matter, coded as an integer whose mapping is given in issue_area_codes decision_direction (str or set[str]) – filter cases by the ideological direction of the majority decision; see decision_directions date_range (list[str] or tuple[str]) – filter cases by the date on which they were decided; both start and end date must be specified, but a null value for either will be replaced by the min/max date available in the corpus min_len (int) – filter cases by the length (number of characters) in their text content limit (int) – return no more than limit cases, in order of decision date dict – full text and metadata of next (by chronological order) court case in corpus passing all filter params ValueError – If any filtering options are invalid.
texts(opinion_author=None, issue_area=None, decision_direction=None, date_range=None, min_len=None, limit=-1)[source]

Iterate over texts in the SupremeCourt corpus, optionally filtering by a variety of metadata and/or text length, in order of decision date.

Parameters: opinion_author (int or set[int]) – filter cases by the name(s) of the majority opinion’s author, coded as an integer whose mapping is given in opinion_author_codes issue_area (int or set[int]) – filter cases by the issue area of the case’s subject matter, coded as an integer whose mapping is given in issue_area_codes decision_direction (str or set[str]) – filter cases by the ideological direction of the majority decision; see decision_directions date_range (list[str] or tuple[str]) – filter cases by the date on which they were decided; both start and end date must be specified, but a null value for either will be replaced by the min/max date available in the corpus min_len (int) – filter cases by the length (number of characters) in their text content limit (int) – return no more than limit cases, in order of decision date str – full text of next (by chronological order) court case in corpus passing all filter params ValueError – If any filtering options are invalid.

Wikipedia¶

All articles for a given language- and version-specific Wikipedia site snapshot, as either texts (str) or records (dict) with both text and metadata.

Records include the following fields:

• text: text content of the article, with markup stripped out
• title: title of the Wikipedia article
• page_id: unique identifier of the page, usable in Wikimedia APIs
• wiki_links: a list of other article pages linked to from this page
• ext_links: a list of external URLs linked to from this page
• categories: a list of Wikipedia categories to which this page belongs
class textacy.datasets.wikipedia.Wikipedia(data_dir=u'/home/docs/checkouts/readthedocs.org/user_builds/textacy/envs/stable/local/lib/python2.7/site-packages/textacy-0.4.1-py2.7.egg/textacy/data/wikipedia', lang=u'en', version=u'latest')[source]

Stream Wikipedia articles from versioned, language-specific database dumps, either as texts (str) or records (dict) with both text content and metadata.

>>> wp = Wikipedia(lang='en', version='latest')
>>> wp.info
{'data_dir': 'path/to/textacy/data/wikipedia',
'description': 'All articles for a given Wikimedia wiki, including wikitext source and metadata, as a single database dump in XML format.',
'name': 'wikipedia',
'site_url': 'https://meta.wikimedia.org/wiki/Data_dumps'}


Iterate over articles as plain texts or records with both text and metadata:

>>> for text in wp.texts(limit=5):
...     print(text)
>>> for record in wp.records(limit=5):
...     print(record['title'], record['text'][:500])


Filter articles by text length:

>>> for text in wp.texts(min_len=1000, limit=1):
...     print(text)

Parameters: data_dir (str) – Path to directory on disk under which database dump files are stored. Each file is expected at {lang}wiki/{version}/{lang}wiki-{version}-pages-articles.xml.bz2 immediately under this directory. lang (str) – Standard two-letter language code, e.g. “en” => “English”, “de” => “German”. https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes version (str) – Database dump version to use. Either “latest” for the most recently available version or a date formatted as “YYYYMMDD”. Dumps are produced intermittently; check for available versions at https://meta.wikimedia.org/wiki/Data_dumps.
lang

str – Standard two-letter language code used in instantiation.

version

str – Database dump version used in instantiation.

filestub

str – The component of filename that is unique to this lang- and version-specific database dump.

filename

str – Full path on disk for the lang- and version-specific Wikipedia database dump, found under the data_dir directory.

download(force=False)[source]

Download the Wikipedia database dump corresponding to the lang and version used in instantiation, and save it to disk under the data_dir directory.

filename

str – Full path on disk for Wikipedia database dump corresponding to the lang and version used in instantiation. None if file not found.

records(min_len=100, limit=-1, fast=False)[source]

Iterate over the pages in a Wikipedia articles database dump (*articles.xml.bz2), yielding one page whose structure and content have been parsed, as a dict.

Parameters: min_len (int) – minimum length in chars that a page must have for it to be returned; too-short pages are skipped limit (int) – maximum number of pages (passing min_len) to yield; if -1, all pages in the db dump are iterated over (optional) fast (bool) – If True, text is extracted using a faster method but which gives lower quality results. Otherwise, a slower but better method is used to extract article text. dict – the next page’s parsed content, including key:value pairs for ‘title’, ‘page_id’, ‘text’, ‘categories’, ‘wiki_links’, ‘ext_links’

Notes

This function requires mwparserfromhell

texts(min_len=100, limit=-1)[source]

Iterate over the pages in a Wikipedia articles database dump (*articles.xml.bz2), yielding the text of a page, one at a time.

Parameters: min_len (int) – minimum length in chars that a page must have for it to be returned; too-short pages are skipped (optional) limit (int) – maximum number of pages (passing min_len) to yield; if -1, all pages in the db dump are iterated over (optional) str – plain text for the next page in the wikipedia database dump

Notes

Page and section titles appear immediately before the text content
that they label, separated by an empty line.
textacy.datasets.wikipedia.get_delimited_spans(wikitext, open_delim=u'[[', close_delim=u']]')[source]
Parameters: wikitext (str) – open_delim (str) – close_delim (str) – Tuple[int, int] – start and end index of next span delimited by open_delim on the left and close_delim on the right
textacy.datasets.wikipedia.remove_templates(wikitext)[source]

Return wikitext with all wikimedia markup templates removed, where templates are identified by opening ‘{{‘ and closing ‘}}’.

Replace external links of the form [URL text] with just text if present or just URL if not.

Replace internal links of the form [[title |...|label]]trail with just label.

textacy.datasets.wikipedia.strip_markup(wikitext)[source]

Strip Wikimedia markup from the text content of a Wikipedia page and return the page as plain-text.

Parameters: wikitext (str) – str

Supreme Court Decisions¶

A collection of ~8.4k (almost all) decisions issued by the U.S. Supreme Court from November 1946 through June 2016 — the “modern” era.

Records include the following fields:

• text: full text of the Court’s decision
• case_name: name of the court case, in all caps
• argument_date: date on which the case was argued before the Court, as a string with format ‘YYYY-MM-DD’
• decision_date: date on which the Court’s decision was announced, as a string with format ‘YYYY-MM-DD’
• decision_direction: ideological direction of the majority decision; either ‘conservative’, ‘liberal’, or ‘unspecifiable’
• maj_opinion_author: name of the majority opinion’s author, if available and identifiable, as an integer code whose mapping is given in SupremeCourt.opinion_author_codes
• n_maj_votes: number of justices voting in the majority
• n_min_votes: number of justices voting in the minority
• issue: subject matter of the case’s core disagreement (e.g. affirmative action) rather than its legal basis (e.g. the equal protection clause), as a string code whose mapping is given in SupremeCourt.issue_codes
• issue_area: higher-level categorization of the issue (e.g. Civil Rights), as an integer code whose mapping is given in SupremeCourt.issue_area_codes
• us_cite_id: citation identifier for each case according to the official United States Reports; Note: There are ~300 cases with duplicate ids, and it’s not clear if that’s “correct” or a data quality problem

The text in this dataset was derived from FindLaw’s searchable database of court cases: http://caselaw.findlaw.com/court/us-supreme-court

The metadata was extracted without modification from the Supreme Court Database: Harold J. Spaeth, Lee Epstein, et al. 2016 Supreme Court Database, Version 2016 Release 1. http://supremecourtdatabase.org. Its license is CC BY-NC 3.0 US: https://creativecommons.org/licenses/by-nc/3.0/us/

This corpus’ creation was inspired by a blog post by Emily Barry: http://www.emilyinamillion.me/blog/2016/7/13/visualizing-supreme-court-topics-over-time

NOTE: The two datasets were merged through much munging and a carefully trained model using the dedupe package. The model’s duplicate threshold was set so as to maximize the F-score where precision had twice as much weight as recall. Still, given occasionally baffling inconsistencies in case naming, citation ids, and decision dates, a very small percentage of texts may be incorrectly matched to metadata. Sorry.

class textacy.datasets.supreme_court.SupremeCourt(data_dir=u'/home/docs/checkouts/readthedocs.org/user_builds/textacy/envs/stable/local/lib/python2.7/site-packages/textacy-0.4.1-py2.7.egg/textacy/data/supreme_court')[source]

Stream U.S. Supreme Court decisions from a compressed json file on disk, either as texts (str) or records (dict) with both text content and metadata.

>>> sc = SupremeCourt()
>>> sc.info
{'data_dir': 'path/to/textacy/data/supreme_court',
'description': 'Collection of ~8.4k decisions issued by the U.S. Supreme Court between November 1946 and June 2016.',
'name': 'supreme_court',
'site_url': 'http://caselaw.findlaw.com/court/us-supreme-court'}


Iterate over decisions as plain texts or records with both text and metadata:

>>> for text in sc.texts(limit=1):
...     print(text)
>>> for record in sc.records(limit=1):
...     print(record['case_name'], record['decision_date'])
...     print(record['text'])


Filter decisions by a variety of metadata fields and text length:

>>> for record in sc.records(opinion_author=109, limit=1):  # Notorious RBG!
>>> for record in sc.records(decision_direction='liberal',
...                          issue_area={1, 9, 10}, limit=10):
>>> for record in sc.records(opinion_author=102,
...                          date_range=('1990-01-01', '1999-12-31')):
...     print(record['case_name'], record['decision_date'])
...     print(sc.issue_codes[record['issue']])
>>> for text in sc.texts(min_len=50000):
...     print(len(text))


Stream decisions into a textacy.Corpus:

>>> text_stream, metadata_stream = textacy.fileio.split_record_fields(
...     sc.records(limit=100), 'text')
>>> c
Corpus(100 docs; 615135 tokens)

Parameters: data_dir (str) – path on disk containing corpus data; if None, textacy’s default data_dir is used
min_date

str – Earliest date for which decisions are available, as an ISO-formatted string (YYYY-MM-DD).

max_date

str – Latest date for which decisions are available, as an ISO-formatted string (YYYY-MM-DD).

decision_directions

set[str] – all distinct decision directions, e.g. ‘liberal’

opinion_author_codes

dict – mapping of majority opinion authors from integer code to (str) full name

issue_area_codes

dict – mapping of high-level issue area of the case’s core disagreement from integer code to (str) description

issue_codes

dict – mapping of specific issue of the case’s core disagreement from integer code to (str) description

download(force=False)[source]

Download a Python version-specific compressed json file from s3, and save it to disk under the data_dir directory.

filename

str – Full path on disk for SupremeCourt data as compressed json file. None if file is not found, e.g. has not yet been downloaded.

records(opinion_author=None, issue_area=None, decision_direction=None, date_range=None, min_len=None, limit=-1)[source]

Iterate over documents (including text and metadata) in the SupremeCourt corpus, optionally filtering by a variety of metadata and/or text length, in order of decision date.

Parameters: opinion_author (int or set[int]) – filter cases by the name(s) of the majority opinion’s author, coded as an integer whose mapping is given in opinion_author_codes issue_area (int or set[int]) – filter cases by the issue area of the case’s subject matter, coded as an integer whose mapping is given in issue_area_codes decision_direction (str or set[str]) – filter cases by the ideological direction of the majority decision; see decision_directions date_range (list[str] or tuple[str]) – filter cases by the date on which they were decided; both start and end date must be specified, but a null value for either will be replaced by the min/max date available in the corpus min_len (int) – filter cases by the length (number of characters) in their text content limit (int) – return no more than limit cases, in order of decision date dict – full text and metadata of next (by chronological order) court case in corpus passing all filter params ValueError – If any filtering options are invalid.
texts(opinion_author=None, issue_area=None, decision_direction=None, date_range=None, min_len=None, limit=-1)[source]

Iterate over texts in the SupremeCourt corpus, optionally filtering by a variety of metadata and/or text length, in order of decision date.

Parameters: opinion_author (int or set[int]) – filter cases by the name(s) of the majority opinion’s author, coded as an integer whose mapping is given in opinion_author_codes issue_area (int or set[int]) – filter cases by the issue area of the case’s subject matter, coded as an integer whose mapping is given in issue_area_codes decision_direction (str or set[str]) – filter cases by the ideological direction of the majority decision; see decision_directions date_range (list[str] or tuple[str]) – filter cases by the date on which they were decided; both start and end date must be specified, but a null value for either will be replaced by the min/max date available in the corpus min_len (int) – filter cases by the length (number of characters) in their text content limit (int) – return no more than limit cases, in order of decision date str – full text of next (by chronological order) court case in corpus passing all filter params ValueError – If any filtering options are invalid.

Oxford Text Archive¶

A collection of ~2.7k Creative Commons texts from the Oxford Text Archive, containing primarily English-language 16th-20th century literature and history.

Record include the following fields:

• text: full text of the literary work
• title: title of the literary work
• author: author(s) of the literary work
• year: year that the literary work was published
• url: url at which literary work can be found online via the OTA

This dataset was compiled by [DAVID?] Mimno from the Oxford Text Archive and stored in his GitHub repo to avoid unnecessary scraping of the OTA site. It is downloaded from that repo, and excluding some light cleaning of its metadata, is reproduced exactly here.

class textacy.datasets.oxford_text_archive.OxfordTextArchive(data_dir=u'/home/docs/checkouts/readthedocs.org/user_builds/textacy/envs/stable/local/lib/python2.7/site-packages/textacy-0.4.1-py2.7.egg/textacy/data/oxford_text_archive')[source]

Stream literary works from a zip file on disk, either as texts (str) or records (dict) with both text content and metadata.

>>> ota = OxfordTextArchive()
>>> ota.info
{'data_dir': 'path/to/textacy/data/oxford_text_archive',
'description': 'Collection of ~2.7k Creative Commons texts from the Oxford Text Archive, containing primarily English-language 16th-20th century literature and history.',
'name': 'oxford_text_archive',
'site_url': 'https://ota.ox.ac.uk/'}


Iterate over literary works as plain texts or records with both text and metadata:

>>> for text in ota.texts(limit=5):
...     print(text[:400])
>>> for record in ota.records(limit=5):
...     print(record['title'], record['year'])
...     print(record['text'][:400])


Filter literary works by a variety of metadata fields and text length:

>>> for record in ota.records(author='Shakespeare, William', limit=1):
...     print(record['year'], record['text'])
>>> for record in ota.records(date_range=('1900-01-01', '2000-01-01'), limit=5):
...     print(record['year'], record['author'])
>>> for text in ota.texts(min_len=4000000):
...     print(len(text))
...     print(text[:200], '...')


Stream literary works into a textacy.Corpus:

>>> text_stream, metadata_stream = textacy.fileio.split_record_fields(
...     ota.records(limit=10), 'text')
>>> c
Corpus(10 docs; 686881 tokens)

Parameters: data_dir (str) – Path to directory on disk under which dataset’s file is stored.
min_date

str – Earliest date for which speeches are available, as an ISO-formatted string (YYYY-MM-DD).

max_date

str – Latest date for which speeches are available, as an ISO-formatted string (YYYY-MM-DD).

authors

Set[str] – Full names of all distinct authors included in this dataset, e.g. 'Shakespeare, William'.

download(force=False)[source]

Download dataset from DOWNLOAD_ROOT and save it to disk under the OxfordTextArchive.data_dir directory.

filename

str – Full path on disk for OxfordTextArchive data as a zip archive file. None if file is not found, e.g. has not yet been downloaded.

records(author=None, date_range=None, min_len=None, limit=-1)[source]

Iterate over records (including text and metadata) in this dataset, optionally filtering by a variety of metadata and/or text length.

Parameters: author (str or Set[str]) – Filter records by the authors’ name; see authors. date_range (List[str] or Tuple[str]) – Filter records by the date on which it was published; both start and end date must be specified, but a null value for either will be replaced by the min/max date available in the dataset. min_len (int) – Filter records by the length (number of characters) of their text content. limit (int) – Return no more than limit records. dict – Text and metadata of next document in dataset passing all filter params. ValueError – If any filtering options are invalid.
texts(author=None, date_range=None, min_len=None, limit=-1)[source]

Iterate over texts in the dataset, optionally filtering by a variety of metadata and/or text length.

Parameters: author (str or Set[str]) – Filter texts by the authors’ name; see authors. date_range (List[str] or Tuple[str]) – Filter texts by the date on which it was published; both start and end date must be specified, but a null value for either will be replaced by the min/max date available in the dataset. min_len (int) – Filter texts by the length (number of characters) of their text content. limit (int) – Return no more than limit texts. str – Full text of next document in dataset passing all filter params. ValueError – If any filtering options are invalid.

File IO¶

Module with functions for reading content from disk in common formats.

textacy.fileio.read.read_csv(filepath, encoding=None, dialect=u'excel', delimiter=u', ')[source]

Iterate over a stream of rows, where each row is an iterable of strings and/or numbers with individual values separated by delimiter.

Parameters: filepath (str) – /path/to/file on disk from which rows will be streamed encoding (str) – dialect (str) – a grouping of formatting parameters that determine how the tabular data is parsed when reading/writing; if ‘infer’, the first 1024 bytes of the file is analyzed, producing a best guess for the correct dialect delimiter (str) – 1-character string used to separate fields in a row List[obj] – next row, whose elements are strings and/or numbers
textacy.fileio.read.read_file(filepath, mode=u'rt', encoding=None)[source]

Read the full contents of a file. Files compressed with gzip, bz2, or lzma are handled automatically.

textacy.fileio.read.read_file_lines(filepath, mode=u'rt', encoding=None)[source]

Read the contents of a file, line by line. Files compressed with gzip, bz2, or lzma are handled automatically.

textacy.fileio.read.read_json(filepath, mode=u'rt', encoding=None, prefix=u'')[source]

Iterate over JSON objects matching the field given by prefix. Useful for reading a large JSON array one item (with prefix='item') or sub-item (prefix='item.fieldname') at a time.

Parameters: filepath (str) – /path/to/file on disk from which json items will be streamed, such as items in a JSON array; for example: [ {"title": "Harrison Bergeron", "text": "The year was 2081, and everybody was finally equal."}, {"title": "2BR02B", "text": "Everything was perfectly swell."} ]  mode (str, optional) – encoding (str, optional) – prefix (str, optional) – if ‘’, the entire JSON object will be read in at once; if ‘item’, each item in a top-level array will be read in successively; if ‘item.text’, each array item’s ‘text’ value will be read in successively next matching JSON object; could be a dict, list, int, float, str, depending on the value of prefix

Notes

Refer to ijson at https://pypi.python.org/pypi/ijson/ for usage details.

textacy.fileio.read.read_json_lines(filepath, mode=u'rt', encoding=None)[source]

Iterate over a stream of JSON objects, where each line of file filepath is a valid JSON object but no JSON object (e.g. array) exists at the top level.

Parameters: filepath (str) – /path/to/file on disk from which json objects will be streamed, where each line in the file must be its own json object; for example: {"title": "Harrison Bergeron", "text": "The year was 2081, and everybody was finally equal."} {"title": "2BR02B", "text": "Everything was perfectly swell."}  mode (str, optional) – encoding (str, optional) – dict – next valid JSON object, converted to native Python equivalent
textacy.fileio.read.read_json_mash(filepath, mode=u'rt', encoding=None, buffersize=2048)[source]

Iterate over a stream of JSON objects, all of them mashed together, end-to-end, on a single line of a file. Bad form, but still manageable.

Parameters: filepath (str) – /path/to/file on disk from which json objects will be streamed, where all json objects are mashed together, end-to-end, on a single line,; for example: {"title": "Harrison Bergeron", "text": "The year was 2081, and everybody was finally equal."}{"title": "2BR02B", "text": "Everything was perfectly swell."}  mode (str, optional) – encoding (str, optional) – buffersize (int, optional) – number of bytes to read in as a chunk dict – next valid JSON object, converted to native Python equivalent
textacy.fileio.read.read_spacy_docs(spacy_vocab, filepath)[source]

Stream spacy.Doc s from disk at filepath where they were serialized using Spacy’s spacy.Doc.to_bytes() functionality.

Parameters: spacy_vocab (spacy.Vocab) – the spacy vocab object used to serialize the docs in filepath filepath (str) – /path/to/file on disk from which spacy docs will be streamed the next deserialized spacy.Doc
textacy.fileio.read.read_sparse_csc_matrix(filepath)[source]

Read the data, indices, indptr, and shape arrays from a .npz file on disk at filepath, and return an instantiated scipy.sparse.csc_matrix.

textacy.fileio.read.read_sparse_csr_matrix(filepath)[source]

Read the data, indices, indptr, and shape arrays from a .npz file on disk at filepath, and return an instantiated scipy.sparse.csr_matrix.

Functions for writing content to disk in common formats.

textacy.fileio.write.write_csv(rows, filepath, encoding=None, auto_make_dirs=False, dialect=u'excel', delimiter=u', ')[source]

Iterate over a sequence of rows, where each row is an iterable of strings and/or numbers, writing each to a separate line in file filepath with individual values separated by delimiter.

Parameters: rows (Iterable[Iterable]) – iterable of iterables of strings and/or numbers to write to disk; for example: [['That was a great movie!', 0.9], ['The movie was okay, I guess.', 0.2], ['Worst. Movie. Ever.', -1.0]]  filepath (str) – /path/to/file on disk where rows will be written encoding (str) – auto_make_dirs (bool) – dialect (str) – a grouping of formatting parameters that determine how the tabular data is parsed when reading/writing delimiter (str) – 1-character string used to separate fields in a row

Note

Here, CSV is used as a catch-all term for any delimited file format, and delimiter=',' is merely the function’s default value. Other common delimited formats are TSV (tab-separated-value, with delimiter='\t') and PSV (pipe-separated-value, with delimiter='|'.

textacy.fileio.write.write_file(content, filepath, mode=u'wt', encoding=None, auto_make_dirs=False)[source]

Write content to disk at filepath. Files with appropriate extensions are compressed with gzip or bz2 automatically. Any intermediate folders not found on disk may automatically be created.

textacy.fileio.write.write_file_lines(lines, filepath, mode=u'wt', encoding=None, auto_make_dirs=False)[source]

Write the content in lines to disk at filepath, line by line. Files with appropriate extensions are compressed with gzip or bz2 automatically. Any intermediate folders not found on disk may automatically be created.

textacy.fileio.write.write_json(json_object, filepath, mode=u'wt', encoding=None, auto_make_dirs=False, ensure_ascii=False, indent=None, separators=(u', ', u':'), sort_keys=False)[source]

Write JSON object all at once to disk at filepath.

Parameters: json_object (json) – valid JSON object to be written filepath (str) – /path/to/file on disk to which json object will be written, such as a JSON array; for example: [ {"title": "Harrison Bergeron", "text": "The year was 2081, and everybody was finally equal."}, {"title": "2BR02B", "text": "Everything was perfectly swell."} ]  mode (str) – encoding (str) – auto_make_dirs (bool) – indent (int or str) – ensure_ascii (bool) – separators (tuple[str]) – sort_keys (bool) –
textacy.fileio.write.write_json_lines(json_objects, filepath, mode=u'wt', encoding=None, auto_make_dirs=False, ensure_ascii=False, separators=(u', ', u':'), sort_keys=False)[source]

Iterate over a stream of JSON objects, writing each to a separate line in file filepath but without a top-level JSON object (e.g. array).

Parameters: json_objects (iterable[json]) – iterable of valid JSON objects to be written filepath (str) – /path/to/file on disk to which JSON objects will be written, where each line in the file is its own json object; for example: {"title": "Harrison Bergeron", "text": "The year was 2081, and everybody was finally equal."} {"title": "2BR02B", "text": "Everything was perfectly swell."}  mode (str) – encoding (str) – auto_make_dirs (bool) – ensure_ascii (bool) – separators (tuple[str]) – sort_keys (bool) –
textacy.fileio.write.write_spacy_docs(spacy_docs, filepath, auto_make_dirs=False)[source]

Serialize a sequence of spacy.Doc s to disk at filepath using Spacy’s spacy.Doc.to_bytes() functionality.

Parameters: spacy_docs (spacy.Doc or iterable(spacy.Doc)) – a single spacy doc or a sequence of spacy docs to serialize to disk at filepath filepath (str) – /path/to/file on disk to which spacy docs will be streamed auto_make_dirs (bool) –
textacy.fileio.write.write_sparse_matrix(matrix, filepath, compressed=True)[source]

Write a scipy.sparse.csr_matrix or scipy.sparse.csc_matrix to disk at filepath, optionally compressed.

Parameters: matrix (scipy.sparse.csr_matrix or scipy.sparse.csr_matrix) – filepath (str) – /path/to/file on disk to which matrix objects will be written; if filepath does not end in .npz, that extension is automatically appended to the name compressed (bool) – if True, save arrays into a single file in compressed .npz format
textacy.fileio.write.write_streaming_download_file(url, filepath, mode=u'wt', encoding=None, auto_make_dirs=False, chunk_size=1024)[source]

Download content from url in a stream; write successive chunks of size chunk_size bytes to disk at filepath. Files with appropriate extensions are compressed with gzip or bz2 automatically. Any intermediate folders not found on disk may automatically be created.

textacy.fileio.utils.coerce_content_type(content, file_mode)[source]

If the content to be written to file and the file_mode used to open it are incompatible (either bytes with text mode or unicode with bytes mode), try to coerce the content type so it can be written.

textacy.fileio.utils.get_filenames(dirname, match_substr=None, ignore_substr=None, match_regex=None, ignore_regex=None, extension=None, ignore_invisible=True, recursive=False)[source]

Yield full paths of files on disk under directory dirname, optionally filtering for or against particular substrings or file extensions and crawling all subdirectories.

Parameters: dirname (str) – /path/to/dir on disk where files to read are saved match_substr (str) – match only files with given substring (DEPRECATED; use match_regex) ignore_substr (str) – match only files without given substring (DEPRECATED; use ignore_regex) match_regex (str) – include files whose names match this regex pattern ignore_regex (str) – include files whose names do not match this regex pattern extension (str) – if files only of a certain type are wanted, specify the file extension (e.g. ”.txt”) ignore_invisible (bool) – if True, ignore invisible files, i.e. those that begin with a period recursive (bool) – if True, iterate recursively through all files in subdirectories; otherwise, only return files directly under dirname str – next file’s name, including the full path on disk OSError – if dirname is not found on disk
textacy.fileio.utils.make_dirs(filepath, mode)[source]

If writing filepath to a directory that doesn’t exist, all intermediate directories will be created as needed.

textacy.fileio.utils.open_sesame(filepath, mode=u'rt', encoding=None, auto_make_dirs=False, errors=None, newline=None)[source]

Open file filepath. Compression (if any) is inferred from the file extension (‘.gz’, ‘.bz2’, or ‘.xz’) and handled automatically; ‘~’, ‘.’, and/or ‘..’ in paths are automatically expanded; if writing to a directory that doesn’t exist, all intermediate directories can be created automatically, as needed.

open_sesame may be used as a drop-in replacement for the built-in open.

Parameters: filepath (str) – path on disk (absolute or relative) of the file to open mode (str) – optional string specifying the mode in which filepath is opened encoding (str) – optional name of the encoding used to decode or encode filepath; only applicable in text mode errors (str) – optional string specifying how encoding/decoding errors are handled; only applicable in text mode newline (str) – optional string specifying how universal newlines mode works; only applicable in text mode auto_make_dirs (bool) – if True, automatically create (sub)directories if not already present in order to write filepath file object
textacy.fileio.utils.split_record_fields(items, content_field, itemwise=False)[source]

Split records’ content (text) field from associated metadata fields, but keep them paired together for convenient loading into a textacy.Doc <textacy.doc.Doc> (with itemwise = True) or textacy.Corpus <textacy.corpus.Corpus> (with itemwise = False). Output format depends on the form of the input items (dicts vs. lists) and the value for itemwise.

Parameters: items (Iterable[dict] or Iterable[list]) – an iterable of dicts, e.g. as read from disk by read_json_lines(), or an iterable of lists, e.g. as read from disk by read_csv() content_field (str or int) – if str, key in each dict item whose value is the item’s content (text); if int, index of the value in each list item corresponding to the item’s content (text) itemwise (bool) – if True, content + metadata are paired item-wise as an iterable of (content, metadata) 2-tuples; if False, content + metadata are paired by position in two parallel iterables in the form of a (iterable(content), iterable(metadata)) 2-tuple if itemwise is True and items is an iterable of dicts; the first element in each tuple is the item’s content, the second element is its metadata as a dictionary generator(Tuple[str, list]): if itemwise is True and items is an iterable of lists; the first element in each tuple is the item’s content, the second element is its metadata as a list Tuple[Iterable[str], Iterable[dict]]: if itemwise is False and items is an iterable of dicts; the first element of the tuple is an iterable of items’ contents, the second is an iterable of their metadata dicts Tuple[Iterable[str], Iterable[list]]: if itemwise is False and items is an iterable of lists; the first element of the tuple is an iterable of items’ contents, the second is an iterable of their metadata lists generator(Tuple[str, dict])
textacy.fileio.utils.unzip(seq)[source]

Borrowed from toolz.sandbox.core.unzip, but using cytoolz instead of toolz to avoid the additional dependency.

Visualization¶

textacy.viz.termite.draw_termite_plot(values_mat, col_labels, row_labels, highlight_cols=None, highlight_colors=None, save=False)[source]

Make a “termite” plot, typically used for assessing topic models with a tabular layout that promotes comparison of terms both within and across topics.

Parameters: values_mat (np.ndarray or matrix) – matrix of values with shape (# row labels, # col labels) used to size the dots on the grid col_labels (seq[str]) – labels used to identify x-axis ticks on the grid row_labels (seq[str]) – labels used to identify y-axis ticks on the grid highlight_cols (int or seq[int], optional) – indices for columns to visually highlight in the plot with contrasting colors highlight_colors (tuple of 2-tuples) – each 2-tuple corresponds to a pair of (light/dark) matplotlib-friendly colors used to highlight a single column; if not specified (default), a good set of 6 pairs are used save (str, optional) – give the full /path/to/fname on disk to save figure axis on which termite plot is plotted matplotlib.axes.Axes.axis ValueError – if more columns are selected for highlighting than colors or if any of the inputs’ dimensions don’t match

References

TopicModel.termite_plot

textacy.viz.network.draw_semantic_network(graph, node_weights=None, spread=3.0, draw_nodes=False, base_node_size=300, node_alpha=0.25, line_width=0.5, line_alpha=0.1, base_font_size=12, save=False)[source]

Draw a semantic network with nodes representing either terms or sentences, edges representing coocurrence or similarity, and positions given by a force- directed layout.

Parameters: graph (networkx.Graph) – node_weights (dict) – mapping of node: weight, used to size node labels (and, optionally, node circles) according to their weight spread (float) – number that drives the spread of the network; higher values give more spread-out networks draw_nodes (bool) – if True, circles are drawn under the node labels base_node_size (int) – if node_weights not given and draw_nodes is True, this is the size of all nodes in the network; if node_weights _is_ given, node sizes will be scaled against this value based on their weights compared to the max weight node_alpha (float) – alpha of the circular nodes drawn behind labels if draw_nodes is True line_width (float) – width of the lines (edges) drawn between nodes line_alpha (float) – alpha of the lines (edges) drawn between nodes base_font_size (int) – if node_weights not given, this is the font size used to draw all labels; otherwise, font sizes will be scaled against this value based on the corresponding node weights compared to the max save (str) – give the full /path/to/fname on disk to save figure (optional) axis on which network plot is drawn matplotlib.axes.Axes.axis

Utilities¶

Set of small utility functions that take text strings as input.

textacy.text_utils.KWIC(text, keyword, ignore_case=True, window_width=50, print_only=True)

Alias of keyword_in_context.

textacy.text_utils.clean_terms(terms)[source]

Clean up a sequence of single- or multi-word strings: strip leading/trailing junk chars, handle dangling parens and odd hyphenation, etc.

Parameters: terms (Iterable[str]) – sequence of terms such as “presidency”, “epic failure”, or “George W. Bush” that may be _unclean_ for whatever reason str – next term in terms but with the cruft cleaned up, excluding terms that were _entirely_ cruft

Warning

Terms with (intentionally) unusual punctuation may get “cleaned” into a form that changes or obscures the original meaning of the term.

textacy.text_utils.detect_language(text)[source]

Detect the most likely language of a text and return its 2-letter code (see https://cloud.google.com/translate/v2/using_rest#language-params). Uses the cld2-cffi package; to take advantage of optional params, call cld2.detect() directly.

Parameters: text (str) – str
textacy.text_utils.is_acronym(token, exclude=None)[source]

Pass single token as a string, return True/False if is/is not valid acronym.

Parameters: token (str) – single word to check for acronym-ness exclude (Set[str]) – if technically valid but not actually good acronyms are known in advance, pass them in as a set of strings; matching tokens will return False bool
textacy.text_utils.keyword_in_context(text, keyword, ignore_case=True, window_width=50, print_only=True)[source]

Search for keyword in text via regular expression, return or print strings spanning window_width characters before and after each occurrence of keyword.

Parameters: text (str) – text in which to search for keyword keyword (str) – technically, any valid regular expression string should work, but usually this is a single word or short phrase: “spam”, “spam and eggs”; to account for variations, use regex: “[Ss]pam (and|&) [Ee]ggs?” N.B. If keyword contains special characters, be sure to escape them!!! ignore_case (bool) – if True, ignore letter case in keyword matching window_width (int) – number of characters on either side of keyword to include as “context” print_only (bool) – if True, print out all results with nice formatting; if False, return all (pre, kw, post) matches as generator of raw strings generator(Tuple[str, str, str]), or None

Set of small utility functions that take Spacy objects as input.

textacy.spacy_utils.get_main_verbs_of_sent(sent)[source]

Return the main (non-auxiliary) verbs in a sentence.

textacy.spacy_utils.get_objects_of_verb(verb)[source]

Return all objects of a verb according to the dependency parse, including open clausal complements.

textacy.spacy_utils.get_span_for_compound_noun(noun)[source]

Return document indexes spanning all (adjacent) tokens in a compound noun.

textacy.spacy_utils.get_span_for_verb_auxiliaries(verb)[source]

Return document indexes spanning all (adjacent) tokens around a verb that are auxiliary verbs or negations.

textacy.spacy_utils.get_subjects_of_verb(verb)[source]

Return all subjects of a verb according to the dependency parse.

textacy.spacy_utils.is_negated_verb(token)[source]

Returns True if verb is negated by one of its (dependency parse) children, False otherwise.

Parameters: token (spacy.Token) – parent document must have parse information bool

TODO: generalize to other parts of speech; rule-based is pretty lacking, so will probably require training a model; this is an unsolved research problem

textacy.spacy_utils.is_plural_noun(token)[source]

Returns True if token is a plural noun, False otherwise.

Parameters: token (spacy.Token) – parent document must have POS information bool
textacy.spacy_utils.merge_spans(spans)[source]

Merge spans in-place within parent doc so that each takes up a single token.

Parameters: spans (Iterable[spacy.Span]) –
textacy.spacy_utils.normalized_str(token)[source]

Return as-is text for tokens that are proper nouns or acronyms, lemmatized text for everything else.

Parameters: token (spacy.Token or spacy.Span) – str
textacy.spacy_utils.preserve_case(token)[source]

Returns True if token is a proper noun or acronym, False otherwise.

Parameters: token (spacy.Token) – parent document must have POS information bool

Set of small utility functions that do mathy stuff.

textacy.math_utils.cosine_similarity(vec1, vec2)[source]

Return the cosine similarity between two vectors.

Parameters: vec1 (numpy.array) – vec2 (numpy.array) – float

Other Stuff!¶

Collection of semantic similarity metrics.

textacy.similarity.hamming(str1, str2)[source]

Measure the similarity between two strings using Hamming distance, which simply gives the number of characters in the strings that are different i.e. the number of substitution edits needed to change one string into the other.

Parameters: str1 (str) – str2 (str) – similarity between str1 and str2 in the interval [0.0, 1.0], where larger values correspond to more similar strings float

Note

This uses a modified Hamming distance in that it permits strings of different lengths to be compared.

textacy.similarity.jaccard(obj1, obj2, fuzzy_match=False, match_threshold=0.8)[source]

Measure the semantic similarity between two strings or sequences of strings using Jaccard distance, with optional fuzzy matching of not-identical pairs when obj1 and obj2 are sequences of strings.

Parameters: obj1 (str or Sequence[str]) – obj2 (str or Sequence[str]) – if str, both inputs are treated as sequences of characters, in which case fuzzy matching is not permitted fuzzy_match (bool) – if True, allow for fuzzy matching in addition to the usual identical matching of pairs between input vectors match_threshold (float) – value in the interval [0.0, 1.0]; fuzzy comparisons with a score >= this value will be considered matches similarity between obj1 and obj2 in the interval [0.0, 1.0], where larger values correspond to more similar strings or sequences of strings float ValueError – if fuzzy_match is True but obj1 and obj2 are strings
textacy.similarity.jaro_winkler(str1, str2, prefix_weight=0.1)[source]

Measure the similarity between two strings using Jaro-Winkler similarity metric, a modification of Jaro metric giving more weight to a shared prefix.

Parameters: str1 (str) – str2 (str) – prefix_weight (float) – the inverse value of common prefix length needed to consider the strings identical similarity between str1 and str2 in the interval [0.0, 1.0], where larger values correspond to more similar strings float
textacy.similarity.levenshtein(str1, str2)[source]

Measure the similarity between two strings using Levenshtein distance, which gives the minimum number of character insertions, deletions, and substitutions needed to change one string into the other.

Parameters: str1 (str) – str2 (str) – normalize (bool) – if True, divide Levenshtein distance by the total number of characters in the longest string; otherwise leave the distance as-is similarity between str1 and str2 in the interval [0.0, 1.0], where larger values correspond to more similar strings float
textacy.similarity.token_sort_ratio(str1, str2)[source]

Measure of similarity between two strings based on minimal edit distance, where ordering of words in each string is normalized before comparing.

Parameters: str1 (str) – str2 (str) – similarity between str1 and str2 in the interval [0.0, 1.0], where larger values correspond to more similar strings. float
textacy.similarity.word2vec(obj1, obj2)[source]

Measure the semantic similarity between one Doc or spacy Doc, Span, Token, or Lexeme and another like object using the cosine distance between the objects’ (average) word2vec vectors.

Parameters: obj1 (textacy.Doc, spacy.Doc, spacy.Span, spacy.Token, or spacy.Lexeme) – obj2 (textacy.Doc, spacy.Doc, spacy.Span, spacy.Token, or spacy.Lexeme) –
Returns
float: similarity between obj1 and obj2 in the interval [0.0, 1.0],
where larger values correspond to more similar objects
textacy.similarity.word_movers(doc1, doc2, metric=u'cosine')[source]

Measure the semantic similarity between two documents using Word Movers Distance.

Parameters: doc1 (textacy.Doc or spacy.Doc) – doc2 (textacy.Doc or spacy.Doc) – metric ({'cosine', 'euclidean', 'l1', 'l2', 'manhattan'}) – similarity between doc1 and doc2 in the interval [0.0, 1.0], where larger values correspond to more similar documents float

References

Ofir Pele and Michael Werman, “A linear time histogram metric for improved
SIFT matching,” in Computer Vision - ECCV 2008, Marseille, France, 2008.
Ofir Pele and Michael Werman, “Fast and robust earth mover’s distances,”
in Proc. 2009 IEEE 12th Int. Conf. on Computer Vision, Kyoto, Japan, 2009.
Kusner, Matt J., et al. “From word embeddings to document distances.”
Proceedings of the 32nd International Conference on Machine Learning (ICML 2015). 2015. http://jmlr.org/proceedings/papers/v37/kusnerb15.pdf

Calculations of basic counts and readability statistics for text documents.

class textacy.text_stats.TextStats(doc)[source]

Compute a variety of basic counts and readability statistics for a given text document. For example:

>>> text = list(textacy.datasets.CapitolWords().texts(limit=1))[0]
>>> doc = textacy.Doc(text)
>>> ts = TextStats(doc)
>>> ts.n_words
136
11.817647058823532
>>> ts.basic_counts
{'n_chars': 685,
'n_long_words': 43,
'n_monosyllable_words': 90,
'n_polysyllable_words': 24,
'n_sents': 6,
'n_syllables': 214,
'n_unique_words': 80,
'n_words': 136}
'coleman_liau_index': 12.509300816176474,
'gulpease_index': 51.86764705882353,
'gunning_fog_index': 16.12549019607843,
'lix': 54.28431372549019,
'smog_index': 14.554592549557764,
'wiener_sachtextformel': 8.266410784313727}

Parameters: doc (textacy.Doc or SpacyDoc) – A text document processed by spacy. Need only be tokenized.
n_sents

int – Number of sentences in doc.

n_words

int – Number of words in doc, including numbers + stop words but excluding punctuation.

n_chars

int – Number of characters for all words in doc.

n_syllables

int – Number of syllables for all words in doc.

n_unique_words

int – Number of unique (lower-cased) words in doc.

n_long_words

int – Number of words in doc with 7 or more characters.

n_monosyllable_words

int – Number of words in doc with 1 syllable only.

n_polysyllable_words

int – Number of words in doc with 3 or more syllables. Note: Since this excludes words with exactly 2 syllables, it’s likely that n_monosyllable_words + n_polysyllable_words != n_words.

flesch_kincaid_grade_level
flesch_readability_ease
smog_index
gunning_fog_index
coleman_liau_index
automated_readability_index
lix
gulpease_index
wiener_sachtextformel

floathttps://de.wikipedia.org/wiki/Lesbarkeitsindex#Wiener_Sachtextformel Note: This always returns variant #1.

basic_counts

Dict[str, int] – Mapping of basic count names to values, where basic counts are the attributes listed above between n_sents and n_polysyllable_words.

readability_stats

Dict[str, float] – Mapping of readability statistic names to values, where readability stats are the attributes listed above between flesch_kincaid_grade_level and wiener_sachtextformel.

Raises: ValueError – If doc is not a textacy.Doc or SpacyDoc.
textacy.text_stats.automated_readability_index(n_chars, n_words, n_sents)[source]

textacy.text_stats.coleman_liau_index(n_chars, n_words, n_sents)[source]

https://en.wikipedia.org/wiki/Coleman%E2%80%93Liau_index

textacy.text_stats.flesch_kincaid_grade_level(n_syllables, n_words, n_sents)[source]

textacy.text_stats.flesch_readability_ease(n_syllables, n_words, n_sents)[source]

textacy.text_stats.gulpease_index(n_chars, n_words, n_sents)[source]

https://it.wikipedia.org/wiki/Indice_Gulpease

textacy.text_stats.gunning_fog_index(n_words, n_polysyllable_words, n_sents)[source]

https://en.wikipedia.org/wiki/Gunning_fog_index

textacy.text_stats.lix(n_words, n_long_words, n_sents)[source]

https://en.wikipedia.org/wiki/LIX

textacy.text_stats.readability_stats(doc)[source]

Get calculated values for a variety of statistics related to the “readability” of a text: Flesch-Kincaid Grade Level, Flesch Reading Ease, SMOG Index, Gunning-Fog Index, Coleman-Liau Index, and Automated Readability Index.

Also includes constituent values needed to compute the stats, e.g. word count.

DEPRECATED

Parameters: doc (textacy.Doc) – mapping of readability statistic name (str) to value (int or float) dict NotImplementedError – if doc is not English language. sorry.
textacy.text_stats.smog_index(n_polysyllable_words, n_sents)[source]

https://en.wikipedia.org/wiki/SMOG

textacy.text_stats.wiener_sachtextformel(n_words, n_polysyllable_words, n_monosyllable_words, n_long_words, n_sents, variant=1)[source]

https://de.wikipedia.org/wiki/Lesbarkeitsindex#Wiener_Sachtextformel

Functions to load and cache language data and other NLP resources.

textacy.data.load_depechemood(*args, **kwargs)[source]

Load DepecheMood lexicon text file from disk, munge into nested dictionary for convenient lookup by lemma#POS. NB: English only!

Each version of DepecheMood is built starting from word-by-document matrices either using raw frequencies (DepecheMood_freq.txt), normalized frequencies (DepecheMood_normfreq.txt) or tf-idf (DepecheMood_tfidf.txt). The files are tab-separated; each row contains one Lemma#PoS followed by the scores for the following emotions: AFRAID, AMUSED, ANGRY, ANNOYED, DONT_CARE, HAPPY, INSPIRED, SAD.

Parameters: data_dir (str, optional) – directory on disk where DepecheMood lexicon text files are stored, i.e. the location of the ‘DepecheMood_V1.0’ directory created when unzipping the DM dataset download_if_missing (bool, optional) – if True and data not found on disk, it will be automatically downloaded and saved to disk weighting (str {'freq', 'normfreq', 'tfidf'}, optional) – type of word weighting used in building DepecheMood matrix top-level keys are Lemma#POS strings, values are nested dicts with emotion names as keys and weights as floats dict[dict]

References

Staiano, J., & Guerini, M. (2014). “DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News”. Proceedings of ACL-2014. (arXiv:1405.1605) Data available at https://github.com/marcoguerini/DepecheMood/releases .

textacy.data.load_hyphenator(*args, **kwargs)[source]

Load an object that hyphenates words at valid points, as used in LaTex typesetting.

Note that while hyphenation points always fall on syllable divisions, not all syllable divisions are valid hyphenation points. But it’s decent.

Parameters: lang (str, optional) – standard 2-letter language abbreviation; to get list of valid values: >>> import pyphen; pyphen.LANGUAGES  pyphen.Pyphen()
textacy.data.load_spacy(*args, **kwargs)[source]

Load a language-specific spaCy pipeline (collection of data, models, and resources) for tokenizing, tagging, parsing, etc. text. The most recent result is cached.

Parameters: name (str) – Standard 2-letter language abbreviation for a language. Currently, spaCy supports English (‘en’) and German (‘de’). path (str) – path/to/directory on disk where spaCy models are saved. If None, spaCy’s default data path is used. create_pipeline (func) – Callable that takes a spaCy Language instance as its argument and returns a sequence of callables. Each callable takes a SpacyDoc as its sole positional argument and modifies the document in place. **kwargs – Keyword arguments passed to spacy.load(). vocab tokenizer tagger parser matcher entity add_vectors create_make_doc spacy. RuntimeError – if package can’t be loaded

Collection of lexicon-based methods for characterizing texts by sentiment, emotional valence, etc.

textacy.lexicon_methods.emotional_valence(words, threshold=0.0, dm_data_dir=None, dm_weighting='normfreq')[source]

Get average emotional valence over all words for the following emotions: AFRAID, AMUSED, ANGRY, ANNOYED, DONT_CARE, HAPPY, INSPIRED, SAD.

Parameters: words (List[spacy.Token]) – list of words for which to get average emotional valence; note that only nouns, adjectives, adverbs, and verbs will be counted threshold (float) – minimum emotional valence score for which to count a given word for a given emotion; value must be in [0.0, 1.0) dm_data_dir (str) – full path to directory where DepecheMood data is saved on disk dm_weighting ({'freq', 'normfreq', 'tfidf'}) – type of word weighting used in building DepecheMood matrix mapping of emotion (str) to average valence score (float) dict

References

 [DepecheMood] Staiano and Guerini. DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News. 2014. Data available at https://github.com/marcoguerini/DepecheMood/releases