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Mastering Clojure Data Analysis

You're reading from   Mastering Clojure Data Analysis If you'd like to apply your Clojure skills to performing data analysis, this is the book for you. The example based approach aids fast learning and covers basic to advanced topics. Get deeper into your data.

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Product type Paperback
Published in May 2014
Publisher
ISBN-13 9781783284139
Length 340 pages
Edition Edition
Languages
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Author (1):
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Eric Richard Rochester Eric Richard Rochester
Author Profile Icon Eric Richard Rochester
Eric Richard Rochester
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Toc

Table of Contents (17) Chapters Close

Mastering Clojure Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Network Analysis – The Six Degrees of Kevin Bacon FREE CHAPTER 2. GIS Analysis – Mapping Climate Change 3. Topic Modeling – Changing Concerns in the State of the Union Addresses 4. Classifying UFO Sightings 5. Benford's Law – Detecting Natural Progressions of Numbers 6. Sentiment Analysis – Categorizing Hotel Reviews 7. Null Hypothesis Tests – Analyzing Crime Data 8. A/B Testing – Statistical Experiments for the Web 9. Analyzing Social Data Participation 10. Modeling Stock Data Index

Chapter 3. Topic Modeling – Changing Concerns in the State of the Union Addresses

A huge source of data right now is the volumes of unstructured, natural-language data that's everywhere on the Internet. Think of all the news articles, blog posts, Twitter posts, and YouTube comments as well as the thousands of other ways that people can create and share textual content online. What they're saying may be important to you, and being able to track what subjects they are talking about is incredibly useful to become aware of the trends and conversations.

A tool to explore the information a group of text documents discusses is called topic modeling. This is a technique to identify the "topics" discussed in a collection of documents, although as we'll see, "topics" is defined a little differently here than it is in informal conversation. The strength of these models is that they don't assume that each document talks only about one thing. Instead, they model documents as collections of topics. This...

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