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Data Science from Scratch, 2nd Edition by Joel Grus

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Chapter 21. Natural Language Processing

They have been at a great feast of languages, and stolen the scraps.

William Shakespeare

Natural language processing (NLP) refers to computational techniques involving language. It’s a broad field, but we’ll look at a few techniques, both simple and not simple.

Word Clouds

In Chapter 1, we computed word counts of users’ interests. One approach to visualizing words and counts is word clouds, which artistically depict the words at sizes proportional to their counts.

Generally, though, data scientists don’t think much of word clouds, in large part because the placement of the words doesn’t mean anything other than “here’s some space where I was able to fit a word.”

If you ever are forced to create a word cloud, think about whether you can make the axes convey something. For example, imagine that, for each of some collection of data science–related buzzwords, you have two numbers between 0 and 100—the first representing how frequently it appears in job postings, and the second how frequently it appears on résumés:

data = [ ("big data", 100, 15), ("Hadoop", 95, 25), ("Python", 75, 50),
         ("R", 50, 40), ("machine learning", 80, 20), ("statistics", 20, 60),
         ("data science", 60, 70), ("analytics", 90, 3),
         ("team player", 85, 85), ("dynamic", 2, 90), ("synergies", 70, 0),
         ("actionable insights", 40, 30), ("think out of the box", 45, 10),
         ("self-starter", 30, 50), ("customer focus", 65, 15),
         ("thought leadership", 35, 35)]

The word cloud approach ...

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