#IMPORTANT
#IMPORT FIRST EVERY SINGLE LIBRARY YOU NEED !!

from nltk.tokenize import sent_tokenize as st, word_tokenize as wt #tokenizing sentence
from nltk.corpus import stopwords as sw, wordnet #filtering sentence
from nltk.stem import PorterStemmer as ps, WordNetLemmatizer as wnl #stem filter to basic
from nltk.probability import FreqDist #show freq of words
from nltk.chunk import ne_chunk #drawing tree
from nltk.tag import pos_tag #detail tag
from nltk.classify import NaiveBayesClassifier as nbc, accuracy #training and testing data

#import data from nltk data
from nltk.corpus import movie_reviews
import pickle

neg_review = []
pos_review = []

stop_words = sw.words('english')

for x in movie_reviews.categories():

    y = 0

    for i in movie_reviews.fileids(x):
        if x == 'neg':
            neg_review += [word for word in movie_reviews.words(i) if word not in stop_words]
        else:
            pos_review += [word for word in movie_reviews.words(i) if word not in stop_words]

        if y == 10:
            break
        else:
            y += 1

neg_review = neg_review[:5000] # shift + alt + down/up
pos_review = pos_review[:5000]

#FILTERING USING STEM AND LEMMATIZE

def extract(word, list_of_words, category):
    if word not in list_of_words:
        return ({word: False}, category)
    else:
        return ({word: True}, category)

stemmer = ps()
lemmatizer = wnl()

neg_review = [stemmer.stem(w) for w in neg_review]
pos_review = [stemmer.stem(w) for w in pos_review]

# [ key (for key in [list]) (condition if/else) ]

neg_review = [lemmatizer.lemmatize(w) for w in neg_review]

pos_review = [lemmatizer.lemmatize(w) for w in pos_review]

#EXTRACTING FEATURE (IMPORTANT)

neg_review = [ extract(w, pos_review, 'negative') for w in neg_review ]

pos_review = [ extract(w, neg_review, 'positive') for w in pos_review ]

#CREATE TRAIN DATA AND TEST DATA

idx = int(.8 * 5000)
train_data = neg_review[:idx] + pos_review[:idx]
test_data = neg_review[idx:] + pos_review[idx:]

#TRAINING DATA TO CREATE A MODEL

model = nbc.train(train_data)

#CHECK ACCURACY

# acc = accuracy(model, test_data)
# print(acc * 100)

# model.show_most_informative_features()

words_input = input('input words: ')

pos = 0
words = wt(words_input)

def extract_input(w):
    if w not in neg_review:
        return {w: True}
    return {w: False}

for w in words:
    w = stemmer.stem(w)
    w = lemmatizer.lemmatize(w)

    res = model.classify(extract_input(w))

    if res == 'positive':
        pos += 1

if pos > len(words) / 2:
    print('Review is Positive !')
else:
    print('Review is Negative !')

w = words[0]
for syn in wordnet.synsets(w):
    for s in syn.lemmas():
        print(s.name())

        for a in s.antonyms():
            print('\t' + a.name())


#SAVE DATA USING PICKLE
save_data = open('data.pickle', 'wb') #wb = write byte
pickle.dump(model, save_data)
save_data.close()

#LOAD DATA USING PICKLE
load_data = open('data.pickle', 'rb') #rb = read byte
model = pickle.load(load_data)
load_data.close()

#freqdist, pos_tag, ne_chunk

#input

#classify input using model

#show detail synonym and antonym
#show frequency, pos_tag, and tree words