Removing punctuation
Now that you've removed stop words from the feedback text, it's time to handle punctuation. The tokens you obtained in the previous exercise still contain punctuation marks, which are often unnecessary when categorizing feedback.
Your task is to remove punctuation from the list of tokens provided, helping to clean up the data even further.
Questo esercizio fa parte del corso
Natural Language Processing (NLP) in Python
Istruzioni dell'esercizio
- Clean the
filtered_tokenslist by removing all punctuation.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
import string
filtered_tokens = ['reached', 'support', 'got', 'helpful', 'response', 'within', 'minutes', '!', '!', '!', '#', 'impressed']
# Remove punctuation
clean_tokens = [____ for word in filtered_tokens if ____ not in ____.____]
print(clean_tokens)