Book description
Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re talking about, and where they’re located—using Python code examples, Jupyter notebooks, or Docker containers.
In part one, each standalone chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blogs and feeds, mailboxes, GitHub, and a newly added chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter.
- Get a straightforward synopsis of the social web landscape
- Use Docker to easily run each chapter’s example code, packaged as a Jupyter notebook
- Adapt and contribute to the code’s open source GitHub repository
- Learn how to employ best-in-class Python 3 tools to slice and dice the data you collect
- Apply advanced mining techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognition
- Build beautiful data visualizations with Python and JavaScript toolkits
Publisher resources
Table of contents
-
Preface
- A Note from Matthew Russell
- README.1st
- Managing Your Expectations
- Python-Centric Technology
- Improvements to the Third Edition
- The Ethical Use of Data Mining
- Conventions Used in This Book
- Using Code Examples
- O’Reilly Online Learning
- How to Contact Us
- Acknowledgments for the Third Edition
- Acknowledgments for the Second Edition
- Acknowledgments from the First Edition
- I. A Guided Tour of the Social Web
- Prelude
- 1. Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More
- 2. Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More
- 3. Mining Instagram: Computer Vision, Neural Networks, Object Recognition, and Face Detection
- 4. Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More
- 5. Mining Text Files: Computing Document Similarity, Extracting Collocations, and More
- 6. Mining Web Pages: Using Natural Language Processing to Understand Human Language, Summarize Blog Posts, and More
- 7. Mining Mailboxes: Analyzing Who’s Talking to Whom About What, How Often, and More
- 8. Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs, and More
- II. Twitter Cookbook
-
9. Twitter Cookbook
- Accessing Twitter’s API for Development Purposes
- Doing the OAuth Dance to Access Twitter’s API for Production Purposes
- Discovering the Trending Topics
- Searching for Tweets
- Constructing Convenient Function Calls
- Saving and Restoring JSON Data with Text Files
- Saving and Accessing JSON Data with MongoDB
- Sampling the Twitter Firehose with the Streaming API
- Collecting Time-Series Data
- Extracting Tweet Entities
- Finding the Most Popular Tweets in a Collection of Tweets
- Finding the Most Popular Tweet Entities in a Collection of Tweets
- Tabulating Frequency Analysis
- Finding Users Who Have Retweeted a Status
- Extracting a Retweet’s Attribution
- Making Robust Twitter Requests
- Resolving User Profile Information
- Extracting Tweet Entities from Arbitrary Text
- Getting All Friends or Followers for a User
- Analyzing a User’s Friends and Followers
- Harvesting a User’s Tweets
- Crawling a Friendship Graph
- Analyzing Tweet Content
- Summarizing Link Targets
- Analyzing a User’s Favorite Tweets
- Closing Remarks
- Recommended Exercises
- Online Resources
- III. Appendixes
- A. Information About This Book’s Virtual Machine Experience
- B. OAuth Primer
- C. Python and Jupyter Notebook Tips and Tricks
- Index
Product information
- Title: Mining the Social Web, 3rd Edition
- Author(s):
- Release date: January 2019
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491985045
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