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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Title
Step1:  Create Text Data
Step2:  Stem Words
Stemming reduces a word to its stem by identifying and removing affixes (e.g. gerunds) while keeping the root meaning of the word. NLTK's PorterStemmer implements the widely used Porter stemming algorithm.
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Python Code:
# Load library
from nltk.stem.porter import PorterStemmer
Explanation: Title: Stemming Words
Slug: stemming_words
Summary: How to stem words in unstructured text data for machine learning in Python. 
Date: 2016-09-09 12:00
Category: Machine Learning
Tags: Preprocessing Text
Authors: Chris Albon
<a alt="Stemming Words" href="https://machinelearningflashcards.com">
    <img src="stemming_words/Stemming_Words_print.png" class="flashcard center-block">
</a>
Preliminaries
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# Create word tokens
tokenized_words = ['i', 'am', 'humbled', 'by', 'this', 'traditional', 'meeting']
Explanation: Create Text Data
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# Create stemmer
porter = PorterStemmer()
# Apply stemmer
[porter.stem(word) for word in tokenized_words]
Explanation: Stem Words
Stemming reduces a word to its stem by identifying and removing affixes (e.g. gerunds) while keeping the root meaning of the word. NLTK's PorterStemmer implements the widely used Porter stemming algorithm.
End of explanation
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