Lemmatization

While continuing your analysis of user reviews, you noticed that stemming sometimes produces non-standard words like "fli" from "flying", which can reduce interpretability. To address this, you'll now use lemmatization, which returns actual words and helps improve the clarity and accuracy of your analysis.

WordNetLemmatizer has been imported, stop_words has been defined, and the necessary NLTK resources have been downloaded.

This exercise is part of the course

Natural Language Processing (NLP) in Python

Exercise instructions

Create an instance lemmatizer of the WordNetLemmatizer() class.
Use the lemmatizer to lemmatize the lower_tokens.

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 lemmatizer
lemmatizer = ____()

# Lemmatize each token
lemmatized_tokens = [____.____(____) for ____ in clean_tokens]

print(lemmatized_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 Exercise 10: Lemmatization

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