问如何在nltk naivebayes分类器中增加频率？

EN

featuresets = [(document_features(d), c) for (d,c) in documents]
train_set, test_set = featuresets[100:], featuresets[:100]
classifier = nltk.NaiveBayesClassifier.train(train_set)

all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words())
word_features = list(all_words)[:2000] [1]

def document_features(document): [2]
    document_words = set(document) [3]
    features = {}
    for word in word_features:
        features['contains({})'.format(word)] = (word in document_words)
    return features

def split_and_count_word(data):
    #belongs_to : Main
    #Role : make featuresets from korean words using konlpy.
    #Parameter : dictionary data(dict of contents ex.{'politic':{'parliament': [content,content]}..})
    #Return : list featuresets([{'word':True',...},'politic'] == featureset + category)

    featuresets = []
    twitter = konlpy.tag.Twitter()#Korean word splitter

    for big_cat in data:

        for small_cat in data[big_cat]:
            #save category name needed in featuresets 
            category = str(big_cat[0:3])+'/'+str(small_cat)
            count = 0; print(small_cat)

            for one_news in data[big_cat][small_cat]:
                count+=1; if count%100==0: print(count,end=' ')                
                #one_news is list in list so open it!
                doc = one_news
                #split word as using konlpy
                list_of_splited_word = twitter.morphs(doc[:-63])#delete useless sentences. 
                #get word length is higher than two and get list of splited words
                list_of_up_two_word = [word for word in list_of_splited_word if len(word)>1]
                dict_of_featuresets = make_featuresets(list_of_up_two_word)
                #save 
                featuresets.append((dict_of_featuresets,category))

    return featuresets


def make_featuresets(data):
    #belongs_to : split_and_count_word
    #Role : make featuresets
    #Parameter : list list_of_up_two_word(ex.['비누','떨어','지다']
    #Return : dictionary {word : True for word in data}

    #PROBLEM :(
    #cannot consider the freqency of word
    return {word : True for word in data}

def naive_train(featuresets):
    #belongs_to : Main
    #Role : Learning by naive bayes rule
    #Parameter : list featuresets([{'word':True',...},'pol/pal'])
    #Return : object classifier(nltk naivebayesclassifier object),
    #         list test_set(the featuresets that are randomly selected)

    random.shuffle(featuresets)
    train_set, test_set = featuresets[1000:], featuresets[:1000]
    classifier = naivebayes.NaiveBayesClassifier.train(train_set)

    return classifier,test_set

featuresets = split_and_count_word(data)
classifier,test_set = naive_train(featuresets)