Fixed encoding error for computer classification.

Removed words causing encoding errors.

Scripts/6_recallClassification_Bayes_New_Data.py

@@ -41,8 +41,16 @@ def selectFeatures(train_set, train_text, k):
     #words = [wnl.lemmatize(t, 'v') for t in words]
     print str(len(words))+' words found.'
 
+    clean_words = []
+    for word in words:
+        try:
+            v = nltk.pos_tag(word)
+            clean_words.append(word)
+        except:
+            continue
+
     # Tagging of the parts of speech
-    tagged_words = nltk.pos_tag(words)
+    tagged_words = nltk.pos_tag(clean_words)
     words = [word for (word, tag) in tagged_words if tag in ['NN','JJ','NNS','RB','VB','VBD','VBG','VBN','VBP','VBZ']]
     #words = set(words).intersection(set(unigrams+ bigrams+trigrams));
     # Get the frequency of words

Scripts/7_automatedSteps.py

@@ -10,7 +10,7 @@ import os
 
 #markers for which sections to execute
 pieces = {"Retrieve": False, "Unique": True, "ClassifyS1": True,
-        "ClassifyS2": False, "Procodes": True}
+        "ClassifyS2": True, "Procodes": True}
 
 '''
     SCRIPT 1 --> Retrieve the data
@@ -23,7 +23,7 @@ if(pieces["Retrieve"]):
     basepath = './../Original_Data';
     os.chdir(basepath)
 
-    #get data from 2005-2012
+    #get data from 2006-2012
     for Year in range(2007, 2008):
         print 'Year '+str(Year);
         startYear = Year;

Unique_Data/best_keywords.txt

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