diff --git a/ngrams/term_frequencies.py b/ngrams/term_frequencies.py
index ef2404f..c8c88de 100755
--- a/ngrams/term_frequencies.py
+++ b/ngrams/term_frequencies.py
@@ -84,7 +84,7 @@ def tf_comments(subreddit_weeks, mwe_pass, mwe_tokenize, stopWords):
         tfs = Counter([])
         authors = Counter([])
         for post in posts:
-            tokens = my_tokenizer(post.body, mwe_pass, mwe_tokenize, stopWords)
+            tokens = my_tokenizer(post.selftext, mwe_pass, mwe_tokenize, stopWords)
             tfs.update(tokens)
             authors.update([post.author])
 
@@ -102,9 +102,9 @@ def tf_posts(subreddit_weeks, mwe_pass, mwe_tokenize, stopWords):
         for post in posts:
             title_tokens = my_tokenizer(post.title, mwe_pass, mwe_tokenize, stopWords)
             tfs.update(title_tokens)
-            if post.body is not None and post.body != "":
-                body_tokens = my_tokenizer(post.body, mwe_pass, mwe_tokenize, stopWords)
-                tfs.update(body_tokens)
+            if post.selftext is not None and post.selftext != "":
+                selftext_tokens = my_tokenizer(post.selftext, mwe_pass, mwe_tokenize, stopWords)
+                tfs.update(selftext_tokens)
             authors.update([post.author])
 
         for term, tf in tfs.items():
@@ -147,7 +147,7 @@ def weekly_tf(partition,
     if reddit_dataset == 'comments':
         batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
     if reddit_dataset == 'posts':
-        batches = dataset.to_batches(columns=['CreatedAt','subreddit','title','body','author'])
+        batches = dataset.to_batches(columns=['CreatedAt','subreddit','title','selftext','author'])
 
     schema = pa.schema([pa.field('subreddit', pa.string(), nullable=nullable_schema),
                         pa.field('term', pa.string(), nullable=nullable_schema),