Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781801073691
Length 414 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
David Knickerbocker David Knickerbocker
Author Profile Icon David Knickerbocker
David Knickerbocker
Arrow right icon
View More author details
Toc

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

Moving on from data visualization to NLP, there are also several Python libraries that will be helpful to us in processing text. Each library has unique offerings as well as strengths and weaknesses, and the documentation should be read.

Natural Language Toolkit

The Natural Language Toolkit (NLTK) is a Python library for working with natural language – language used by humans in our daily interactions and activities. NLTK is an older Python library, and it is often better to use other libraries such as spaCy for things that used to be done with NLTK, such as named-entity recognition or part-of-speech tagging.

However, just because it is older doesn’t mean that it is obsolete. It is still very useful for analyzing text data, and it provides more linguistic capabilities than libraries such as spaCy.

Put simply, NLTK is a foundational library for doing NLP in Python, and you should not skip past it for more recent libraries and approaches.

Setup

Installing...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image