Reuters corpus & Inaugural corpus - Deutschina's Tech Diary

O'Reilly's textbook chapter 2.1.4-2.1.5

Reuters corpus should have plenty of news documents.

>>> import nltk
>>> import sys
>>> from nltk.corpus import reuters
>>> reuters.fileids()
['test/14826', 'test/14828', 'test/14829', 'test/14832', 'test/14833', 'test/14839', 'test/14840', 'test/14841', 'test/14842', 'test/14843', 'test/14844', 'test/14849', 'test/14852', 'test/14854', 'test/14858', 'test/14859', 'test/14860', 
....
'training/9984', 'training/9985', 'training/9988', 'training/9989', 'training/999', 'training/9992', 'training/9993', 'training/9994', 'training/9995']

Each fileid starts with "test" or "training". Following code shows that each fileid has multiple "categories".

>>> reuters.categories()
['acq', 'alum', 'barley', 'bop', 'carcass', 'castor-oil', 'cocoa', 'coconut', 'coconut-oil', 'coffee', 'copper', 'copra-cake', 'corn', 'cotton', 'cotton-oil', 'cpi', 'cpu', 'crude', 'dfl', 'dlr', 'dmk', 'earn', 'fuel', 'gas', 'gnp', 'gold', 'grain', 'groundnut', 'groundnut-oil', 'heat', 'hog', 'housing', 'income', 'instal-debt', 'interest', 'ipi', 'iron-steel', 'jet', 'jobs', 'l-cattle', 'lead', 'lei', 'lin-oil', 'livestock', 'lumber', 'meal-feed', 'money-fx', 'money-supply', 'naphtha', 'nat-gas', 'nickel', 'nkr', 'nzdlr', 'oat', 'oilseed', 'orange', 'palladium', 'palm-oil', 'palmkernel', 'pet-chem', 'platinum', 'potato', 'propane', 'rand', 'rape-oil', 'rapeseed', 'reserves', 'retail', 'rice', 'rubber', 'rye', 'ship', 'silver', 'sorghum', 'soy-meal', 'soy-oil', 'soybean', 'strategic-metal', 'sugar', 'sun-meal', 'sun-oil', 'sunseed', 'tea', 'tin', 'trade', 'veg-oil', 'wheat', 'wpi', 'yen', 'zinc']
>>> reuters.categories('training/9865')
['barley', 'corn', 'grain', 'wheat']

>>> reuters.categories(['training/9865', 'training/9880'])
['barley', 'corn', 'grain', 'money-fx', 'wheat']
>>> reuters.fileids('barley')
['test/15618', 'test/15649', 'test/15676', 'test/15728', 'test/15871', 'test/15875', 'test/15952', 'test/17767', 'test/17769', 'test/18024', 'test/18263', 'test/18908', 'test/19275', 'test/19668', 'training/10175', 'training/1067', 'training/11208', 'training/11316', 'training/11885', 'training/12428', 'training/13099', 'training/13744', 'training/13795', 'training/13852', 'training/13856', 'training/1652', 'training/1970', 'training/2044', 'training/2171', 'training/2172', 'training/2191', 'training/2217', 'training/2232', 'training/3132', 'training/3324', 'training/395', 'training/4280', 'training/4296', 'training/5', 'training/501', 'training/5467', 'training/5610', 'training/5640', 'training/6626', 'training/7205', 'training/7579', 'training/8213', 'training/8257', 'training/8759', 'training/9865', 'training/9958']
>>> reuters.fileids(['barley', 'corn'])
['test/14832', 'test/14858', 'test/15033', 'test/15043', 'test/15106', 'test/15287', 'test/15341', 'test/15618', 'test/15648', 'test/15649', 
...
'training/9058', 'training/9093', 'training/9094', 'training/934', 'training/9470', 'training/9521', 'training/9667', 'training/97', 'training/9865', 'training/9958', 'training/9989']
>>> reuters.words('training/9865')[:14]
['FRENCH', 'FREE', 'MARKET', 'CEREAL', 'EXPORT', 'BIDS', 'DETAILED', 'French', 'operators', 'have', 'requested', 'licences', 'to', 'export']
>>> reuters.words(['training/9865', 'training/9880'])
['FRENCH', 'FREE', 'MARKET', 'CEREAL', 'EXPORT', ...]
>>> reuters.words(categories='barley')
['FRENCH', 'FREE', 'MARKET', 'CEREAL', 'EXPORT', ...]
>>> reuters.words(categories=['barley', 'corn'])
['THAI', 'TRADE', 'DEFICIT', 'WIDENS', 'IN', 'FIRST', ...]

Headlines at the beginning of each document are written in upper cases.

"Inaugural" is not a common word in my poor English vocabulary, but it is a corpus of First speech of the president of USA.

>>> from nltk.corpus import inaugural
>>> inaugural.fileids()
['1789-Washington.txt', '1793-Washington.txt', '1797-Adams.txt', '1801-Jefferson.txt', '1805-Jefferson.txt', '1809-Madison.txt', '1813-Madison.txt', '1817-Monroe.txt', '1821-Monroe.txt', '1825-Adams.txt', '1829-Jackson.txt', '1833-Jackson.txt', '1837-VanBuren.txt', '1841-Harrison.txt', '1845-Polk.txt', '1849-Taylor.txt', '1853-Pierce.txt', '1857-Buchanan.txt', '1861-Lincoln.txt', '1865-Lincoln.txt', '1869-Grant.txt', '1873-Grant.txt', '1877-Hayes.txt', '1881-Garfield.txt', '1885-Cleveland.txt', '1889-Harrison.txt', '1893-Cleveland.txt', '1897-McKinley.txt', '1901-McKinley.txt', '1905-Roosevelt.txt', '1909-Taft.txt', '1913-Wilson.txt', '1917-Wilson.txt', '1921-Harding.txt', '1925-Coolidge.txt', '1929-Hoover.txt', '1933-Roosevelt.txt', '1937-Roosevelt.txt', '1941-Roosevelt.txt', '1945-Roosevelt.txt', '1949-Truman.txt', '1953-Eisenhower.txt', '1957-Eisenhower.txt', '1961-Kennedy.txt', '1965-Johnson.txt', '1969-Nixon.txt', '1973-Nixon.txt', '1977-Carter.txt', '1981-Reagan.txt', '1985-Reagan.txt', '1989-Bush.txt', '1993-Clinton.txt', '1997-Clinton.txt', '2001-Bush.txt', '2005-Bush.txt', '2009-Obama.txt']

Extract the last 4 digit of each text.

>>> [fileid[:4] for fileid in inaugural.fileids()]
['1789', '1793', '1797', '1801', '1805', '1809', '1813', '1817', '1821', '1825', '1829', '1833', '1837', '1841', '1845', '1849', '1853', '1857', '1861', '1865', '1869', '1873', '1877', '1881', '1885', '1889', '1893', '1897', '1901', '1905', '1909', '1913', '1917', '1921', '1925', '1929', '1933', '1937', '1941', '1945', '1949', '1953', '1957', '1961', '1965', '1969', '1973', '1977', '1981', '1985', '1989', '1993', '1997', '2001', '2005', '2009']

Then do a plot with words 'america' and 'citizen'.

>>> cfd = nltk.ConditionalFreqDist(
...     (target, fileid[:4])
...     for fileid in inaugural.fileids()
...     for w in inaugural.words(fileid)
...     for target in ['america', 'citizen']
...     if w.lower().startswith(target))
>>> cfd.plot()

'Citizen' was most frequently used in the speech of 1841. I thought this might be by Lincoln but it was John Tyler. In this stat, the length of the speech was not considered.