Removing stopwords

In the following exercises you're going to clean the Enron emails, in order to be able to use the data in a topic model. Text cleaning can be challenging, so you'll learn some steps to do this well. The dataframe containing the emails df is available. In a first step you need to define the list of stopwords and punctuations that are to be removed in the next exercise from the text data. Let's give it a try.

This exercise is part of the course

Fraud Detection in Python

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Exercise instructions

Import the stopwords from ntlk.
Define 'english' words to use as stopwords under the variable stop.
Get the punctuation set from the string package and assign it to exclude.

Hands-on interactive exercise

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

# Import nltk packages and string 
from nltk.corpus import ____
import string

# Define stopwords to exclude
stop = set(____.____('____'))
stop.update(("to","cc","subject","http","from","sent", "ect", "u", "fwd", "www", "com"))

# Define punctuations to exclude and lemmatizer
exclude = set(____.____)

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