textacy is a Python library for performing higher-level natural language processing (NLP) tasks, built on the high-performance spaCy library. With the basics --- tokenization, part-of-speech tagging, dependency parsing, etc. --- offloaded to another library, textacy focuses on tasks facilitated by the ready availability of tokenized, POS-tagged, and parsed text.
- Stream text, json, csv, and spaCy binary data to and from disk
- Clean and normalize raw text, before analyzing it
- Explore a variety of included datasets, with both text data and metadata from Congressional speeches to historical literature to Reddit comments
- Access and filter basic linguistic elements, such as words and ngrams, noun chunks and sentences
- Extract named entities, acronyms and their definitions, direct quotations, key terms, and more from documents
- Compare strings, sets, and documents by a variety of similarity metrics
- Transform documents and corpora into vectorized and semantic network representations
- Train, interpret, visualize, and save
sklearn-style topic models using LSA, LDA, or NMF methods - Identify a text's language, display key words in context (KWIC), true-case words, and navigate a parse tree
... and more!
The simple way to install textacy is via pip:
$ pip install textacyor conda:
$ conda install -c conda-forge textacyNote: If you use pip, some dependencies have been made optional, because they can be difficult to install and/or are only needed in certain uses cases. To use visualization functions, you'll need matplotlib installed; you can do so via pip install textacy[viz]. For automatic language detection, you'll need cld2-cffi installed; do pip install textacy[lang]. To install all optional dependencies:
$ pip install textacy[all]Otherwise, you can download and unzip the source tar.gz from PyPi, then install manually:
$ python setup.py installFor most uses of textacy, language-specific model data for spacy must first be downloaded. Follow the directions here.
Currently available language models are listed here.
>>> import textacy
>>> import textacy.datasetsEfficiently stream documents from disk and into a processed corpus:
>>> cw = textacy.datasets.CapitolWords()
>>> records = cw.records(speaker_name={'Hillary Clinton', 'Barack Obama'})
>>> text_stream, metadata_stream = textacy.fileio.split_record_fields(
... records, 'text')
>>> corpus = textacy.Corpus('en', texts=text_stream, metadatas=metadata_stream)
>>> corpus
Corpus(1241 docs; 857058 tokens)Represent corpus as a document-term matrix, with flexible weighting and filtering:
>>> vectorizer = textacy.Vectorizer(
... weighting='tfidf', normalize=True, smooth_idf=True,
... min_df=2, max_df=0.95)
>>> doc_term_matrix = vectorizer.fit_transform(
... (doc.to_terms_list(ngrams=1, named_entities=True, as_strings=True)
... for doc in corpus))
>>> print(repr(doc_term_matrix))
<1241x11708 sparse matrix of type '<class 'numpy.float64'>'
with 215182 stored elements in Compressed Sparse Row format>Train and interpret a topic model:
>>> model = textacy.TopicModel('nmf', n_topics=10)
>>> model.fit(doc_term_matrix)
>>> doc_topic_matrix = model.transform(doc_term_matrix)
>>> doc_topic_matrix.shape
(1241, 10)
>>> for topic_idx, top_terms in model.top_topic_terms(vectorizer.id_to_term, top_n=10):
... print('topic', topic_idx, ':', ' '.join(top_terms))
topic 0 : new people 's american senate need iraq york americans work
topic 1 : rescind quorum order consent unanimous ask president mr. madam aside
topic 2 : dispense reading amendment unanimous consent ask president mr. pending aside
topic 3 : health care child mental quality patient medical program information family
topic 4 : student school education college child teacher high program loan year
topic 5 : senators desiring chamber vote 4,600 amtrak rail airline litigation expedited
topic 6 : senate thursday wednesday session unanimous consent authorize p.m. committee ask
topic 7 : medicare drug senior medicaid prescription benefit plan cut cost fda
topic 8 : flu vaccine avian pandemic roberts influenza seasonal outbreak health cdc
topic 9 : virginia west virginia west senator yield question thank objection inquiry massachusettsBasic indexing as well as flexible selection of documents in a corpus:
>>> obama_docs = list(corpus.get(
... lambda doc: doc.metadata['speaker_name'] == 'Barack Obama'))
>>> len(obama_docs)
411
>>> doc = corpus[-1]
>>> doc
Doc(2999 tokens; "In the Federalist Papers, we often hear the ref...")Preprocess plain text, or highlight particular terms in it:
>>> textacy.preprocess_text(doc.text, lowercase=True, no_punct=True)[:70]
'in the federalist papers we often hear the reference to the senates ro'
>>> textacy.text_utils.keyword_in_context(doc.text, 'America', window_width=35)
g on this tiny piece of Senate and America n history. Some 10 years ago, I ask
o do the hard work in New York and America , who get up every day and do the v
say: You know, you never can count America out. Whenever the chips are down,
what we know will give our fellow America ns a better shot at the kind of fut
aith in this body and in my fellow America ns. I remain an optimist, that Amer
ricans. I remain an optimist, that America 's best days are still ahead of us.Extract various elements of interest from parsed documents:
>>> list(textacy.extract.ngrams(
... doc, 2, filter_stops=True, filter_punct=True, filter_nums=False))[:15]
[Federalist Papers,
Senate's,
's role,
violent passions,
pernicious resolutions,
everlasting credit,
common ground,
8 years,
tiny piece,
American history,
10 years,
years ago,
New York,
fellow New,
New Yorkers]
>>> list(textacy.extract.ngrams(
... doc, 3, filter_stops=True, filter_punct=True, min_freq=2))
[fellow New Yorkers,
World Trade Center,
Senator from New,
World Trade Center,
Senator from New,
lot of fun,
fellow New Yorkers,
lot of fun]
>>> list(textacy.extract.named_entities(
... doc, drop_determiners=True, exclude_types='numeric'))[:10]
[Senate,
Senate,
American,
New York,
New Yorkers,
Senate,
Barbara Mikulski,
Senate,
Pennsylvania Avenue,
Senate]
>>> pattern = textacy.constants.POS_REGEX_PATTERNS['en']['NP']
>>> pattern
<DET>? <NUM>* (<ADJ> <PUNCT>? <CONJ>?)* (<NOUN>|<PROPN> <PART>?)+
>>> list(textacy.extract.pos_regex_matches(doc, pattern))[:10]
[the Federalist Papers,
the reference,
the Senate's role,
the consequences,
sudden and violent passions,
intemperate and pernicious resolutions,
the everlasting credit,
wisdom,
our Founders,
an effort]
>>> list(textacy.extract.semistructured_statements(doc, 'I', cue='be'))
[(I, was, on the other end of Pennsylvania Avenue),
(I, was, , a very new Senator, and my city and my State had been devastated),
(I, am, grateful to have had Senator Schumer as my partner and my ally),
(I, am, very excited about what can happen in the next 4 years),
(I, been, a New Yorker, but I know I always will be one)]
>>> textacy.keyterms.textrank(doc, n_keyterms=10)
[('day', 0.01608508275877894),
('people', 0.015079868730811194),
('year', 0.012330783590843065),
('way', 0.011732786337383587),
('colleague', 0.010794482493897155),
('new', 0.0104941198408241),
('time', 0.010016582029543003),
('work', 0.0096498231660789),
('lot', 0.008960478625039818),
('great', 0.008552318032915361)]Compute basic counts and readability statistics for a given text:
>>> ts = textacy.TextStats(doc)
>>> ts.n_unique_words
1107
>>> ts.basic_counts
{'n_chars': 11498,
'n_long_words': 512,
'n_monosyllable_words': 1785,
'n_polysyllable_words': 222,
'n_sents': 99,
'n_syllables': 3525,
'n_unique_words': 1107,
'n_words': 2516}
>>> ts.flesch_kincaid_grade_level
10.853709110179697
>>> ts.readability_stats
{'automated_readability_index': 12.801546064781363,
'coleman_liau_index': 9.905629258346586,
'flesch_kincaid_grade_level': 10.853709110179697,
'flesch_readability_ease': 62.51222198133965,
'gulpease_index': 55.10492845786963,
'gunning_fog_index': 13.69506833036245,
'lix': 45.76390294037353,
'smog_index': 11.683781121521076,
'wiener_sachtextformel': 5.401029023140788}Count terms individually, and represent documents as a bag-of-terms with flexible weighting and inclusion criteria:
>>> doc.count('America')
3
>>> bot = doc.to_bag_of_terms(ngrams={2, 3}, as_strings=True)
>>> sorted(bot.items(), key=lambda x: x[1], reverse=True)[:10]
[('new york', 18),
('senate', 8),
('first', 6),
('state', 4),
('9/11', 3),
('look forward', 3),
('america', 3),
('new yorkers', 3),
('chuck', 3),
('lot of fun', 2)]Note: In almost all cases, textacy expects to be working with unicode text. Docstrings indicate this as str, which is clear and correct for Python 3 but not Python 2. In the latter case, users should cast str bytes to unicode, as needed.
- Burton DeWilde (<[email protected]>)