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import math
import string
import nltk
from nltk.corpus import brown as sc
from collections import Counter
from nltk.corpus import movie_reviews
from nltk.corpus import wordnet as wn
import MPQALexicon
# NLTK's pos tagger kinda sucks sometimes. Let's double check with Wordnet.
def isAdj(word):
global tagger, mpqa_words
#return tagger.tag([word])[0][1].startswith("JJ")
#return len(wn.synsets(word, wn.ADJ)) > 0
#return tagger.tag([word])[0][1].startswith("JJ") and len(wn.synsets(word, wn.ADJ)) > 0 and word in mpqa_words
return word in mpqa_words
def genConj():
conj = open('movieconj.txt', 'w')
global tagger
tagger = nltk.tag.perceptron.PerceptronTagger()
num_lines = 0
for review in sorted(movie_reviews.fileids()): #For every review
tokens = movie_reviews.words(fileids=[review])
for i in range(0,len(tokens)-2):
if isAdj(tokens[i]) and isAdj(tokens[i+2]):
line = tokens[i]+ " " + tokens[i+2] + " " + tokens[i+1] + "\n"
print line.strip()
if (tokens[i+1] == "and"):
conj.write(line)
num_lines += 1
elif (tokens[i+1] == "or"):
conj.write(line)
num_lines += 1
elif (tokens[i+1] == "but"+ "\n"):
conj.write(line)
num_lines += 1
elif (tokens[i+1] == "either-or"):
conj.write(line)
num_lines += 1
elif (tokens[i+1] == "neither-nor"):
conj.write(line)
num_lines += 1
print num_lines
def doBrown():
f = open('movieconj.txt', 'w')
list1 = []
for word in sc.tagged_sents():
for w in word:
if w[1].startswith("JJ"):
list1.append(w[0])
counts = Counter(list1)
d = dict(counts)
for n in d:
if( d[n] >= 15):
f.write(n+" \n")
f.close()
(mpqa_words, mpqa_labels) = MPQALexicon.load()
genConj()
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