Q:

Separating nltk. FreqDist words into two lists? 

I have a series of texts that are instances of a custom WebText class.  Each text is an object that has a rating (-10 to +10) and a word count (nltk. FreqDist) associated with it:
>>trainingTexts = [WebText('train1.txt'), WebText('train2.txt'), WebText('train3.txt'), WebText('train4.txt')]
>>trainingTexts[1].rating
10
>>trainingTexts[1].freq_dist
<FreqDist: 'the': 60, ',': 49, 'to': 38, 'is': 34,...>

How can you now get two lists (or dictionaries) containing every word used exclusively in the positively rated texts (trainingText[].rating>0), and another list containing every word used exclusively in the negative texts (trainingText[].rating<0).  And have each list contain the total word counts for all the positive or negative texts, so that you get something like this:
>>only_positive_words
[('sky', 10), ('good', 9), ('great', 2)...] 
>>only_negative_words
[('earth', 10), ('ski', 9), ('food', 2)...] 

I considered using sets, as sets contain unique instances, but i can't see how this can be done with nltk. FreqDist, and on top of that, a set wouldn't be ordered by word frequency.  Any ideas? 

A:

Ok, let's say you start with this for the purposes of testing:
class Rated(object): 
  def __init__(self, rating, freq_dist): 
    self.rating = rating
    self.freq_dist = freq_dist

a = Rated(5, nltk. FreqDist('the boy sees the dog'.split()))
b = Rated(8, nltk. FreqDist('the cat sees the mouse'.split()))
c = Rated(-3, nltk. FreqDist('some boy likes nothing'.split()))

trainingTexts = [a,b,c]

Then your code would look like:
from collections import defaultdict
from operator import itemgetter

# dictionaries for keeping track of the counts
pos_dict = defaultdict(int)
neg_dict = defaultdict(int)

for r in trainingTexts:
  rating = r.rating
  freq = r.freq_dist

  # choose the appropriate counts dict
  if rating > 0:
    partition = pos_dict
  elif rating < 0: 
    partition = neg_dict
  else:
    continue

  # add the information to the correct counts dict
  for word,count in freq.iteritems():
    partition[word] += count

# Turn the counts dictionaries into lists of descending-frequency words
def only_list(counts, filtered):
  return sorted(filter(lambda (w,c): w not in filtered, counts.items()), \
                key=itemgetter(1), \
                reverse=True)

only_positive_words = only_list(pos_dict, neg_dict)
only_negative_words = only_list(neg_dict, pos_dict)

And the result is:
>>> only_positive_words
[('the', 4), ('sees', 2), ('dog', 1), ('cat', 1), ('mouse', 1)]
>>> only_negative_words
[('nothing', 1), ('some', 1), ('likes', 1)]

