Mining the social web
Want to tap the tremendous amount of valuable social data in Facebook, Twitter, LinkedIn, and Google+? This refreshed edition helps you discover who's making connections with social media, what they're talking about, and where they're located. You'll learn how to combine social w...
| Autor principal: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Beijing ; Sebastopol, California :
O'Reilly
2011.
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628238406719 |
Tabla de Contenidos:
- Table of Contents; Preface; Content Updates; February 22, 2012; To Read This Book?; Or Not to Read This Book?; Tools and Prerequisites; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments; Chapter 1. Introduction: Hacking on Twitter Data; Installing Python Development Tools; Collecting and Manipulating Twitter Data; Tinkering with Twitter's API; Frequency Analysis and Lexical Diversity; What are people talking about right now?; Extracting relationships from the tweets; Visualizing Tweet Graphs
- Synthesis: Visualizing Retweets with ProtovisClosing Remarks; Chapter 2. Microformats: Semantic Markup and Common Sense Collide; XFN and Friends; Exploring Social Connections with XFN; A Breadth-First Crawl of XFN Data; Brief analysis of breadth-first techniques; Geocoordinates: A Common Thread for Just About Anything; Wikipedia Articles + Google Maps = Road Trip?; Plotting geo data via microform.at and Google Maps; Slicing and Dicing Recipes (for the Health of It); Collecting Restaurant Reviews; Summary; Chapter 3. Mailboxes: Oldies but Goodies; mbox: The Quick and Dirty on Unix Mailboxes
- mbox + CouchDB = Relaxed Email AnalysisBulk Loading Documents into CouchDB; Sensible Sorting; Map/Reduce-Inspired Frequency Analysis; Frequency by date/time range; Frequency by sender/recipient fields; Sorting Documents by Value; couchdb-lucene: Full-Text Indexing and More; Threading Together Conversations; Look Who's Talking; Visualizing Mail "Events" with SIMILE Timeline; Analyzing Your Own Mail Data; The Graph Your (Gmail) Inbox Chrome Extension; Closing Remarks; Chapter 4. Twitter: Friends, Followers, and Setwise Operations; RESTful and OAuth-Cladded APIs; No, You Can't Have My Password
- A Lean, Mean Data-Collecting MachineA Very Brief Refactor Interlude; Redis: A Data Structures Server; Elementary Set Operations; Souping Up the Machine with Basic Friend/Follower Metrics; Calculating Similarity by Computing Common Friends and Followers; Measuring Influence; Constructing Friendship Graphs; Clique Detection and Analysis; The Infochimps "Strong Links" API; Interactive 3D Graph Visualization; Summary; Chapter 5. Twitter: The Tweet, the Whole Tweet, and Nothing but the Tweet; Pen : Sword :: Tweet : Machine Gun (?!?); Analyzing Tweets (One Entity at a Time); Tapping (Tim's) Tweets
- What entities are in Tim's tweets?Do frequently appearing user entities imply friendship?; Splicing in the other half of the conversation; Who Does Tim Retweet Most Often?; What's Tim's Influence?; How Many of Tim's Tweets Contain Hashtags?; Juxtaposing Latent Social Networks (or #JustinBieber Versus #TeaParty); What Entities Co-Occur Most Often with #JustinBieber and #TeaParty Tweets?; On Average, Do #JustinBieber or #TeaParty Tweets Have More Hashtags?; Which Gets Retweeted More Often: #JustinBieber or #TeaParty?
- How Much Overlap Exists Between the Entities of #TeaParty and #JustinBieber Tweets?
