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.

Cet exercice fait partie du cours

Feature Engineering for NLP in Python

Afficher le cours

Instructions

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

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de 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))

Modifier et exécuter le code