Cleaning a blog post

In this exercise, you have been given an excerpt from a blog post. Your task is to clean this text into a more machine friendly format. This will involve converting to lowercase, lemmatization and removing stopwords, punctuations and non-alphabetic characters.

The excerpt is available as a string blog and has been printed to the console. The list of stopwords are available as stopwords.

This exercise is part of the course

Feature Engineering for NLP in Python

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Exercise instructions

Using list comprehension, loop through doc to extract the lemma_ of each token.
Remove stopwords and non-alphabetic tokens using stopwords and isalpha().

Hands-on interactive exercise

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

# Load model and create Doc object
nlp = spacy.load('en_core_web_sm')
doc = nlp(blog)

# Generate lemmatized tokens
lemmas = [token.____ for token in ____]

# Remove stopwords and non-alphabetic tokens
a_lemmas = [lemma for lemma in lemmas 
            if lemma.____ and lemma not in ____]

# Print string after text cleaning
print(' '.join(a_lemmas))

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