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.

This exercise is part of the course

Natural Language Processing (NLP) in Python

Exercise instructions

Initialize the PorterStemmer().
Use a list comprehension to stem each token from the clean_tokens list.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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)

Edit and Run Code

This exercise is part of the course

Natural Language Processing (NLP) in Python

IntermediateSkill Level

4.8+

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Learn the essentials of text processing in Natural Language Processing (NLP). Master techniques such as tokenization, stop word and punctuation removal, and text normalization with lowercasing, stemming, and lemmatization to prepare text data for further analysis and insight extraction.

Exercise 1: Introduction to natural language processing Exercise 2: Sentence and word tokenization Exercise 3: NLP workflow Exercise 4: Stop words and punctuation handling Exercise 5: Removing stop words Exercise 6: Removing punctuation Exercise 7: Text normalization techniques Exercise 8: Lowercasing Exercise 9: Stemming

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