Counting words (I)
Once high level information has been recorded you can begin creating features based on the actual content of each text. One way to do this is to approach it in a similar way to how you worked with categorical variables in the earlier lessons.
- For each unique word in the dataset a column is created.
- For each entry, the number of times this word occurs is counted and the count value is entered into the respective column.
These "count" columns can then be used to train machine learning models.
This exercise is part of the course
Feature Engineering for Machine Learning in Python
Exercise instructions
- Import
CountVectorizerfromsklearn.feature_extraction.text. - Instantiate
CountVectorizerand assign it tocv. - Fit the vectorizer to the
text_cleancolumn. - Print the feature names generated by the vectorizer.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import CountVectorizer
____
# Instantiate CountVectorizer
cv = ____
# Fit the vectorizer
cv.____(speech_df['text_clean'])
# Print feature names
print(cv.____)