diff --git a/asdf.py b/asdf.py
deleted file mode 100644
index 9c8868d..0000000
--- a/asdf.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import nltk
-from nltk.corpus import wordnet
-from nltk.corpus import movie_reviews
-from nltk.classify import NaiveBayesClassifier
-
-word = "good"
-syns = wordnet.synsets(word)
-for syn in syns:
-  lemmas = syn.lemmas()
-  for lemma in lemmas:
-    if lemma.antonyms() != []: print lemma.antonyms()
\ No newline at end of file
diff --git a/pos.py b/pos.py
deleted file mode 100644
index f6311c5..0000000
--- a/pos.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import nltk
-import os
-import string
-
-"""
-POS tagging is really slow compared to SVM training and prediction.
-This script processes the reviews beforehand, applies the NLTK POS tagger,
-and saves them in a new folder.
-"""
-
-POS_FOLDER   = os.path.join("review_polarity","txt_sentoken","pos")
-NEG_FOLDER   = os.path.join("review_polarity","txt_sentoken","neg")
-POS_TAGGED_FOLDER   = os.path.join("review_polarity","txt_sentoken","pos_tagged")
-NEG_TAGGED_FOLDER   = os.path.join("review_polarity","txt_sentoken","neg_tagged")
-
-for (folder_name, tagged_folder_name) in [(POS_FOLDER, POS_TAGGED_FOLDER), (NEG_FOLDER, NEG_TAGGED_FOLDER)]:
-  filenames = []
-  for (folder, x, folder_filenames) in os.walk(folder_name):
-    for filename in folder_filenames:
-      if filename.endswith(".txt"):
-        filenames.append(os.path.join(folder, filename))
-  for filename in filenames:
-    f = open(filename)
-    lines = f.readlines()
-    f.close()
-    text = string.join(lines, " ")
-    
-    tokens = nltk.word_tokenize(text)
-    tagged = nltk.pos_tag(tokens)
-    tagged = [string.join(t, "_") for t in tagged]
-    tagged = string.join(tagged, " ")
-    tagged_filename = os.path.join(tagged_folder_name, os.path.split(filename)[-1])
-    f = open(tagged_filename, "w")
-    f.write(tagged)
-    f.close()
-    print "Tagged & saved file", tagged_filename
\ No newline at end of file