import nltk
import random
from nltk.corpus import movie_reviews

documents = [(list(movie_reviews.words(fileid)), category)
             for category in movie_reviews.categories()
             for fileid in movie_reviews.fileids(category)]

random.shuffle(documents)

all_words = []
for w in movie_reviews.words():
    all_words.append(w.lower()) # normalise everything to lower case and append

all_words = nltk.FreqDist(all_words) # converts to a nltk frequency distribution

word_features = list (all_words.keys())[:3000] # from the frequency list we're taking just the words(keys) and only the top 3000

def find_fetures(document):
    words = set(document) # this gives a list of the unique words - removes duplicates
    features = {} # declare an empty dictionary
    for w in word_features:
        features[w] = (w in words) # this checks each word in the top 3000 to see if it is present in the passed text 'document' so gives a true/false against the 3000
    return features

print((find_fetures(movie_reviews.words('neg/cv000_29416.txt'))))

featuresets = [(find_fetures(rev), category) for (rev, category) in documents]