Stemming
Now that you've cleaned the review text and removed stop words and punctuation, you're ready to normalize the remaining words using stemming to reduce words to their root form. This helps group similar words together, making your analysis more consistent and efficient.
The PorterStemmer class has been provided, along with a list of clean_tokens.
Bu egzersiz
Natural Language Processing (NLP) in Python
kursunun bir parçasıdırEgzersiz talimatları
- Initialize the
PorterStemmer(). - Use a list comprehension to stem each token from the
clean_tokenslist.
Uygulamalı interaktif egzersiz
Bu örnek kodu tamamlayarak bu egzersizi bitirin.
clean_tokens = ['flying', 'lot', 'lately', 'flights', 'keep', 'getting', 'delayed', 'honestly', 'traveling', 'work', 'gets', 'exhausting', 'endless', 'delays', 'every', 'travel', 'teaches', 'something', 'new']
# Create stemmer
stemmer = ____()
# Stem each token
stemmed_tokens = [____.____(____) for ____ in clean_tokens]
print(stemmed_tokens)