# Robin Camille Davis

# CODEX 2016

# Top 5 names from a .txt file

##This script asks for the URL to a plain-text file (Project Gutenberg).

##It outputs the top 5 person names from the text using the

##Stanford Named Entity Recognition interface in NLTK.

##

##Caveats: first/last names and honorifics not considered

import urllib2

from nltk import tree

from nltk import word_tokenize as tok

from nltk import pos_tag as postag

from nltk import ne_chunk as ne

from nltk.corpus import gutenberg as gb

from collections import Counter

theurl = raw_input("URL to .txt file: ")

sourcefile = urllib2.urlopen(theurl)

source = sourcefile.read()

#Tokenize

sourcetok = tok(source)

#Tag POS

sourcetag = postag(sourcetok)

#Outputs POS-tagged text

sourcene = ne(sourcetag, binary=False)

charsall = []

for n in sourcene:

if type(n) == tree.Tree:

if n.label() == 'PERSON':

for m in n:

charsall.append(m[0])

honorifics = ['Mr.', 'Mrs.', 'Ms.', 'Miss', 'Dr.', 'Prof.', 'Professor', 'Lord', 'Lady', 'Sir', 'Madam', 'Dame', 'Rev.', 'Rabbi']

charsallnames = []

for s in charsall:

if s in honorifics:

pass

else:

charsallnames.append(s)

counted = (word for word in charsallnames if word[:1].isupper())

c = Counter(counted)

charscommon = c.most_common(5)

chars = []

for s in charscommon:

chars.append(s[0])

print '\nMost common names:'

print '\t'.join(chars)