#!\/usr\/bin\/env python2\r\n\r\n# dispersion.py - illustrate where common words appear in a text\r\n#\r\n# usage: .\/dispersion.py <file>\r\n\r\n# Eric Lease Morgan <emorgan@nd.edu>\r\n# November 19, 2014 - my second real python script; \"Thanks for the idioms, Don!\"\r\n\r\n\r\n# configure\r\nMAXIMUM = 25\r\nPOS = 'NN'\r\n\r\n# require\r\nimport nltk\r\nimport operator\r\nimport sys\r\n\r\n# sanity check\r\nif len( sys.argv ) != 2 :\r\n print \"Usage:\", sys.argv[ 0 ], \"<file>\"\r\n quit()\r\n \r\n# get input\r\nfile = sys.argv[ 1 ]\r\n\r\n# initialize\r\nwith open( file, 'r' ) as handle : text = handle.read()\r\nsentences = nltk.sent_tokenize( text )\r\npos = {}\r\n\r\n# process each sentence\r\nfor sentence in sentences : \r\n \r\n # POS the sentence and then process each of the resulting words\r\n for word in nltk.pos_tag( nltk.word_tokenize( sentence ) ) :\r\n \r\n # check for configured POS, and increment the dictionary accordingly\r\n if word[ 1 ] == POS : pos[ word[ 0 ] ] = pos.get( word[ 0 ], 0 ) + 1\r\n\r\n# sort the dictionary\r\npos = sorted( pos.items(), key = operator.itemgetter( 1 ), reverse = True )\r\n\r\n# do the work; create a dispersion chart of the MAXIMUM most frequent pos words\r\ntext = nltk.Text( nltk.word_tokenize( text ) )\r\ntext.dispersion_plot( [ p[ 0 ] for p in pos[ : MAXIMUM ] ] )\r\n\r\n# done\r\nquit()<\/code><\/pre>\n<\/blockquote>\n\nI used the program to analyze two works: 1) Thoreau’s Walden<\/cite>, and 2) Emerson’s Representative Men<\/cite>. From the dispersion plots displayed below, we can conclude a few things:\n<\/p>\n\n- The words “man”, “life”, “day”, and “world” are common between both works.<\/li>\n
- Thoreau discusses water, ponds, shores, and surfaces together.<\/li>\n
- While Emerson seemingly discussed man and nature in the same breath, but none of his core concepts are discussed as densely as Thoreau’s.<\/li>\n<\/ul>\n
![\"Thoreau's](\"http:\/\/blogs.nd.edu\/emorgan\/files\/2014\/11\/thoreau.png\")
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