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Mastering Predictive Analytics with R, Second Edition

You're reading from   Mastering Predictive Analytics with R, Second Edition Machine learning techniques for advanced models

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121393
Length 448 pages
Edition 2nd Edition
Languages
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Authors (2):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
Rui Miguel Forte Rui Miguel Forte
Author Profile Icon Rui Miguel Forte
Rui Miguel Forte
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Table of Contents (16) Chapters Close

Preface 1. Gearing Up for Predictive Modeling FREE CHAPTER 2. Tidying Data and Measuring Performance 3. Linear Regression 4. Generalized Linear Models 5. Neural Networks 6. Support Vector Machines 7. Tree-Based Methods 8. Dimensionality Reduction 9. Ensemble Methods 10. Probabilistic Graphical Models 11. Topic Modeling 12. Recommendation Systems 13. Scaling Up 14. Deep Learning Index

Modeling tweet topics


In machine learning and natural language processing, a topic model is a type of statistical model used to discover the abstract topics that occur in a collection of documents. A good example or use case to illustrate this concept is Twitter. Suppose we could analyze an individual's (or an organization's) tweets to discover any overriding trend. Let's look at a simple example.

If you have a Twitter account, you can perform this exercise pretty easily (you can then apply the same process to an archive of tweets you want to focus on and/or model). First, we need to create a tweet archive file.

Under Settings, you can submit a request to receive your tweets in an archive file. Once it's ready, you'll get an email with a link to download it:

And then save your file locally:

Now that we have a data source to work with, we can move the tweets into a list object (we'll call it x) and then convert that into an R data frame object (df1):

The tweets were first converted to a data frame...

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