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Network Science with Python

You're reading from   Network Science with Python Explore the networks around us using network science, social network analysis, and machine learning

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781801073691
Length 414 pages
Edition 1st Edition
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Author (1):
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David Knickerbocker David Knickerbocker
Author Profile Icon David Knickerbocker
David Knickerbocker
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Getting Started with Natural Language Processing and Networks
2. Chapter 1: Introducing Natural Language Processing FREE CHAPTER 3. Chapter 2: Network Analysis 4. Chapter 3: Useful Python Libraries 5. Part 2: Graph Construction and Cleanup
6. Chapter 4: NLP and Network Synergy 7. Chapter 5: Even Easier Scraping! 8. Chapter 6: Graph Construction and Cleaning 9. Part 3: Network Science and Social Network Analysis
10. Chapter 7: Whole Network Analysis 11. Chapter 8: Egocentric Network Analysis 12. Chapter 9: Community Detection 13. Chapter 10: Supervised Machine Learning on Network Data 14. Chapter 11: Unsupervised Machine Learning on Network Data 15. Index 16. Other Books You May Enjoy

NLP and Network Synergy

In the previous chapters, we discussed natural language processing (NLP), network analysis, and the tools used in the Python programming language for both. We also discussed non-programmatic tools for doing network analysis.

In this chapter, we are going to put all of that knowledge to work. I hope to explain the power and insights that can be unveiled by combining NLP and network analysis, which is the theme of this book. In later chapters, we will continue with this theme, but we’ll also discuss other tools for working with NLP and networks, such as unsupervised and supervised machine learning. This chapter will demonstrate techniques for determining who or what a piece of text is talking about.

The following topics will be covered in this chapter:

  • Why are we learning about NLP in a network book?
  • Asking questions to tell a story
  • Introducing web scraping
  • Choosing between libraries, APIs, and source data
  • Using the Natural...
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