In [1]:

    

# Import Dependencies
import nltk
from nltk.corpus import wordnet


    

In [2]:

    

text = 'I was not happy with the teams performance.'


    

In [3]:

    

words = nltk.word_tokenize(text)


    

In [4]:

    

words


    

    
Out[4]:





['I', 'was', 'not', 'happy', 'with', 'the', 'teams', 'performance', '.']

In [5]:

    

new_words = []


    

In [6]:

    

temp_word = ""
antonyms = []


    

In [7]:

    

for word in words:
    if word == 'not':
        temp_word = 'not_'
    elif temp_word == 'not_':
        for syn in wordnet.synsets(word):
            for s in syn.lemmas():
                for a in s.antonyms():
                    antonyms.append(a.name())
        if len(antonyms) >= 1:
            word = antonyms[0]
        else:
            word = temp_word + word
        temp_word = ''
    if word != 'not':
        new_words.append(word)


    

In [8]:

    

new_words


    

    
Out[8]:





['I', 'was', 'unhappy', 'with', 'the', 'teams', 'performance', '.']

In [9]:

    

sentence = ' '.join(new_words)


    

In [10]:

    

sentence


    

    
Out[10]:





'I was unhappy with the teams performance .'