import time
import pickle
import os
import nltk as nl
import random

# Unpickle the classifier
# Open the pickle file
f = open('Vader_classifier.p', 'rb')

# Load the pickle file
classifier = pickle.load(f)

# Close the file once we have the classifier unpickled
f.close()


def create_word_features(words):
    my_dict = dict([(word, True) for word in words])
    return my_dict



msg2 = '''Stupidity'''

msg1 = '''Hello mounish, he was rude'''

msg3 = '''pos'''


words = nl.word_tokenize(msg1)
features = create_word_features(words)

words = nl.word_tokenize(msg2)
features1 = create_word_features(words)

words = nl.word_tokenize(msg3)
features2 = create_word_features(words)

print("Message 1 is :", classifier.classify(features))

print("Message 2 is :", classifier.classify(features1))

print("Message 3 is :", classifier.classify(features2))


ac9bcabc2da2f44785fb6d04d137dd846ff940bf
<mask token>


def create_word_features(words):
    my_dict = dict([(word, True) for word in words])
    return my_dict


<mask token>

<mask token>
f.close()


def create_word_features(words):
    my_dict = dict([(word, True) for word in words])
    return my_dict


<mask token>
print('Message 1 is :', classifier.classify(features))
print('Message 2 is :', classifier.classify(features1))
print('Message 3 is :', classifier.classify(features2))

<mask token>
f = open('Vader_classifier.p', 'rb')
classifier = pickle.load(f)
f.close()


def create_word_features(words):
    my_dict = dict([(word, True) for word in words])
    return my_dict


msg2 = 'Stupidity'
msg1 = 'Hello mounish, he was rude'
msg3 = 'pos'
words = nl.word_tokenize(msg1)
features = create_word_features(words)
words = nl.word_tokenize(msg2)
features1 = create_word_features(words)
words = nl.word_tokenize(msg3)
features2 = create_word_features(words)
print('Message 1 is :', classifier.classify(features))
print('Message 2 is :', classifier.classify(features1))
print('Message 3 is :', classifier.classify(features2))

import time
import pickle
import os
import nltk as nl
import random
f = open('Vader_classifier.p', 'rb')
classifier = pickle.load(f)
f.close()


def create_word_features(words):
    my_dict = dict([(word, True) for word in words])
    return my_dict


msg2 = 'Stupidity'
msg1 = 'Hello mounish, he was rude'
msg3 = 'pos'
words = nl.word_tokenize(msg1)
features = create_word_features(words)
words = nl.word_tokenize(msg2)
features1 = create_word_features(words)
words = nl.word_tokenize(msg3)
features2 = create_word_features(words)
print('Message 1 is :', classifier.classify(features))
print('Message 2 is :', classifier.classify(features1))
print('Message 3 is :', classifier.classify(features2))

import time
import pickle
import os
import nltk as nl
import random

# Unpickle the classifier
# Open the pickle file
f = open('Vader_classifier.p', 'rb')

# Load the pickle file
classifier = pickle.load(f)

# Close the file once we have the classifier unpickled
f.close()


def create_word_features(words):
    my_dict = dict([(word, True) for word in words])
    return my_dict



msg2 = '''Stupidity'''

msg1 = '''Hello mounish, he was rude'''

msg3 = '''pos'''


words = nl.word_tokenize(msg1)
features = create_word_features(words)

words = nl.word_tokenize(msg2)
features1 = create_word_features(words)

words = nl.word_tokenize(msg3)
features2 = create_word_features(words)

print("Message 1 is :", classifier.classify(features))

print("Message 2 is :", classifier.classify(features1))

print("Message 3 is :", classifier.classify(features2))

