Conjunction based started

Hand tagged the brown dataset for the conjunction approach. My two gloss
approaches seem to be in git hell, will hopefully get to optimize the
jwb edits.

cblexicon.py

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+import math
+import nltk
+from nltk.corpus import wordnet as wn
+from nltk.corpus import brown as sc
+from collections import Counter
+from sets import Set
+
+class cblexicon:
+
+    def genSets(self):
+        f = open('words.txt', 'r+')
+        content = f.readlines()
+        positive = Set([])
+        negative = Set([])
+
+        for pair in content:
+            current = pair.split(' ')
+            if (current[1][0] == 'p'):
+                positive.add(current[0])
+            elif (current[1][0] == 'n'):
+                negative.add(current[0])
+
+        print len(positive)
+        print len(negative)
+
+cblexicon().genSets()

getAdjectives.py

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+import math
+import nltk
+from nltk.corpus import brown as sc
+from collections import Counter
+
+
+f = open('words.txt', 'r+')
+list1 = []
+for word in sc.tagged_sents():
+    for w in word:
+        if(w[1] == "JJ" or w[1] == "JJR" or w[1] == "JJS" or w[1] == "JJT"):
+            list1.append(w[0])
+counts = Counter(list1)
+d = dict(counts)
+
+for n in d:
+    if( d[n] >= 20):
+        f.write(n+" \n")