Preface PartⅠ.A Guided Tour ofthe SociaIWeb Prelude 1.Mining Twitter： Exploring Trending Topics， Discovering What People Are Talking About， and More 1.1.Overview 1.2.Why Is Twitter All the Rage？ 1.3.Exploring Twitter's API 1.3.1.Fundamental Twitter Terminology 1.3.2.Creating a Twitter API Connection 1.3.3.Exploring Trending Topics 1.3.4.Searching for Tweets 1.4.Analyzing the 140 Characters 1.4.1.Extracting Tweet Entities 1.4.2.Analyzing Tweets and Tweet Entities with Frequency Analysis 1.4.3.Computing the Lexical Diversity of Tweets 1.4.4.Examining Patterns in Retweets 1.4.5.Visualizing Frequency Data with Histograms 1.5.Closing Remarks 1.6.Recommended Exercises 1.7.Online Resources 2.Mining Facebook： Analyzing Fan Pages， Examining Friendships， and More 2.1.Overview 2.2.Exploring Facebook's Social Graph API 2.2.1.Understanding the Social Graph API 2.2.2.Understanding the Open Graph Protocol 2.3.Analyzing Social Graph Connections 2.3.1.Analyzing Facebook Pages 2.3.2.Examining Friendships 2.4.Closing Remarks 2.5.Recommended Exercises 2.6.OnlLne Resources 3.Mining Linked In： Faceting Job Trtles， Clustering Colleagues， and More 3.1.Overview 3.2.Exploring the Linkedln API 3.2.1.Making Linkedln API Requests 3.2.2.Downloading Linkedln Connections as a CSV File 3.3.Crash Course on Clustering Data 3.3.1.Clustering Enhances User Experiences 3.3.2.Normalizing Data to Enable Analysis 3.3.3.Measuring Similarity 3.3.4.Clustering Algorithms 3.4.Closing Remarks 3.5.Recommended Exerases 3.6.Online Resources 4.Mining Google Computing Document Similarity， Extracting Collocations， and More 4.1.Overview 4.2.Exploring the Google+ API 4.2.1.Making Google+ API Requests 4.3.A Whiz—Bang Introduction to TF—IDF 4.3.1.Term Frequency
4.3.2.Inverse Document Frequency 4.3.3.TF—IDF 4.4.Querying Human Language Data with TF—IDF 4.4.1.Introducing the Natural Language Toolkit 4.4.2.Applying TF—IDF to Human Language 4.4.3.Finding Similar Documents 4.4.4.Analyzing Bigrams in Human Language 4.4.5.Reflections on Analyzing Human Language Data 4.5.Closing Remarks 4.6.Recommended Exercises 4.7.Online Resources 5.Mining Web Pages： Using Natural Language Processing to Understand HumanLanguage， Summarize Blog Posts， and More. 5.1.Overview 5.2.Scraping， Parsing， and Crawling the Web 5.2.1.Breadth—First Search in Web Crawling 5.3.Discovering Semantics by Decoding Syntax 5.3.1.Natural Language Processing Illustrated Step—by—Step 5.3.2.Sentence Detection in Human Language Data 5.3.3.Document Summarization 5.4.Entity—Centric Analysis： A Paradigm Shift 5.4.1.Gisting Human Language Data 5.5.Quality ofAnalytics for Processing Human Language Data 5.6.Closing Remarks 5.7.Recommended Exercises 5.8.Online Resources 6.Mining Mailboxes：Analyzing Who's Talking to Whom About What， How Often，and More 6.1.Overview 6.2.Obtaining and Processing a Mail Corpus 6.2.1.A Primer on Unix Mailboxes 6.2.2.Getting the Enron Data 6.2.3.Converting a Mail Corpus to a Unix Mailbox 6.2.4.Converting Unix Mailboxes to JSON 6.2.5.Importing a JSONified Mail Corpus into MongoDB 6.2.6.Programmatically Accessing MongoDB with Python 6.3.Analyzing the Enron Corpus 6.3.1.Querying by Date／Time Range 6.3.2.Analyzing Patterns in Sender／Recipient Communications 6.3.3.Writing Advanced Queries 6.3.4.Searching Emails by Keywords 6.4.Discovering and Visualizing Time—Series Trends 6.5.Analyzing Your Own Mail Data 6.5.1.Accessing Your Gmail with OAuth 6.5.2.Fetching and Parsing Email Messages with IMAP 6.5.3.Visualizing Patterns in GMail with the "Graph Your Inbox Chrome Extension 6.6.Closing Remarks 6.7.Recommended Exercises 6.8.Online Resources 7 Mining GitHub：lnspecting Software Collaboration Habits， Building Interest Graphs， and More 7.1.Overview 7.2.Exploring GitHub's API
7.2.1.Creating a GitHub API Connection 7.2.2.Making GitHub API Requests 7.3.Modeling Data with Property Graphs 7.4.Analyzing GitHub Interest Graphs 7.4.1.Seeding an Interest Graph 7.4.2.Computing Graph Centrality Measures 7.4.3.Extending the Interest Graph with "Follows" Edges for Users 7.4.4.Using Nodes as Pivots for More Efflcient Queries 7.4.5.Visualizing Interest Graphs 7.5.Closing Remarks 7.6.Recommended Exercises 7.7.Online Resources 8.Mining the Semantically Marked—Up Web： Extracting Microformats，lnferencing overRDF， and More. 8.1.Overview 8.2.Microformats： Easy—to—Implement Metadata 8.2.1.Geocoordinates： A Common Thread for Just About Anything 8.2.2.Using Recipe Data to Improve Online Matchmaking 8.2.3.Accessing Linkedln's 200 Million Online Resumes 8.3.From Semantic Markup to Semantic Web： A Brief Interlude 8.4.The Semantic Web： An Evolutionary Revolution 8.4.1.Man Cannot Live on Facts Alone 8.4.2.Inferencing About an Open World 8.5.Closing Remarks 8.6.Recommended Exercises 8.7.Online Resources PartⅡ.Twitter（ookbook 9.TwitterCookbook 9.1.Accessing Twitter's API for Development Purposes 9.2.Doing the OAuth Dance to Access Twitter's API for Production Purposes 9.3.Discovering the Trending Topics 9.4.Searching for Tweets 9.5.Constructing Convenient Function Calls 9.6.Saving and Restoring JSON Data with Text Files 9.7.Saving and Accessing JSON Data with MongoDB 9.8.Sampling the Twitter Firehose with the Streaming API 9.9.Collecting Time—Series Data 9.10.Extracting Tweet Entities 9.11.Finding the Most Popular Tweets in a Collection of Tweets 9.12.Finding the Most Popular Tweet Entities in a Collection of Tweets 9.13.Tabulating Frequency Analysis 9.14.Finding Users Who Have Retweeted a Status 9.15.Extracting a Retweet's Attribution 9.16.Making Robust Twitter Requests 9.17.Resolving User Profile Information 9.18.Extracting Tweet Entities from Arbitrary Text 9.19.Getting All Friends or Followers for a User 9.20.Analyzing a User's Friends and Followers 9.21.Harvesting a User's Tweets 9.22.Crawling a Friendship Graph 9.23.Analyzing Tweet Content
9.24.Summarizing Link Targets 9.25.Analyzing a User's Favorite Tweets 9.26.Closing Remarks 9.27.Recommended Exercises 9.28.Online Resources PartⅢ.Appendixes A.Information About This Book's Virtual Machine Experience B.OAuth Primer C.Python and I Python Notebook Tips & Tricks Index.
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