Added SentiWordNet Approach.

SentiWordLex.py

@@ -0,0 +1,44 @@
+from __future__ import division
+import sys
+import time
+
+import nltk
+from nltk.corpus import movie_reviews
+from nltk.corpus import sentiwordnet as swn
+from nltk.corpus import wordnet as wn
+
+start_time = time.time()
+count = 0.00
+correct = 0.00
+ids = sorted(movie_reviews.fileids())
+
+for reviews in ids:     #For every review
+    score = 0.0
+    positive = 0.0
+    negative = 0.0
+    tokens = nltk.pos_tag(nltk.word_tokenize(movie_reviews.raw(fileids=[reviews])))     #Tokenize all words with POS
+    for token in tokens:
+        if (token[1]== "JJ" or token[1] == "JJR" or token[1] == "JJS"):         # If adjective, check value
+            if len(wn.synsets(token[0], pos=wn.ADJ)) != 0 and swn.senti_synset(wn.synsets(token[0], pos=wn.ADJ)[0].name()) :
+                word = wn.synsets(token[0], pos=wn.ADJ)[0].name()
+                print word
+                print swn.senti_synset(word)
+                positive = positive + swn.senti_synset(word).pos_score()
+                negative = negative + swn.senti_synset(word).neg_score()
+                print "%s, %d, %d" %(word,positive,negative)
+    score = positive - negative
+    if (score < 0):
+        print "Negative at %f" % (score)
+        sentiment = 'neg'
+    else:
+        sentiment = 'pos'
+        print "Positive at %d" % (score)
+    if (sentiment == movie_reviews.categories(fileids=[reviews])[0]):
+        print "Correct"
+        correct = correct + 1.00
+    count = count + 1.00
+
+print correct/count
+print "Seconds: %d" %(time.time() - start_time)
+print "correct:", correct/len(ids)
+print "positive:", positive/len(ids)