挖掘社交網(wǎng)絡

內(nèi)容概要

Facebook、Twitter和Linkedln產(chǎn)生了大量的寶貴的社交數(shù)據(jù)，但是你怎樣才能找出誰通過社交媒介進行聯(lián)系？他們在討論些什么？或者他們在哪兒？《挖掘社交網(wǎng)絡(影印版)》這本簡潔而且具有操作性的書將為你展示如何回答這些甚至更多的問題。你將學到如何組合社交網(wǎng)絡數(shù)據(jù)、分析技術(shù)，如何通過可視化幫助你找到你一直在社交世界中尋找的內(nèi)容，以及那些你都不知道存在的有用信息。
每個獨立章節(jié)介紹了在社交網(wǎng)絡的不同領(lǐng)域挖掘數(shù)據(jù)的技術(shù)，這些領(lǐng)域包括博客和電子郵件。你所需要具備的就是一定的編程經(jīng)驗和學習基本的python工具的意愿。

作者簡介

Matthew A.Russell，Digital Reasoning
Systems的工程副總裁和Zaffra的負責人，是熱愛數(shù)據(jù)挖掘、開源和網(wǎng)絡應用技術(shù)的計算機科學家。他是《Dojo：The
Definitive Guide》(O'Reilly出版)的作者。

書籍目錄

Preface
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
  Visualizing Tweet Graphs
  Synthesis: Visualizing Retweets with Protovis
  Closing Remarks
2. Microformats: Semantic Markup and Common Sense Collide
  XFN and Friends
  Exploring Social Connections with XFN
  A Breadth-First Crawl of XFN Data
  Geocoordinates: A Common Thread for Just About Anything
  Wikipedia Articles + Google Maps = Road Trip?
  Slicing and Dicing Recipes (for the Health of It)
  Collecting Restaurant Reviews
  Summary
3. Mailboxes: Oldies but Goodies
  mbox: The Quick and Dirty on Unix Mailboxes
  mbox + CouchDB = Relaxed Email Analysis
  Bulk Loading Documents into CouchDB
  Sensible Sorting
  Map/Reduce-Inspired Frequency Analysis
  Sorting Documents by Value
  cotichdb-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
4. Twitter: Friends, Followers, and Setwise Operations
  RESTful and OAuth-Cladded APIs
  No, You Can't Have My Password
  A Lean, Mean Data-Collecting Machine
  A 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
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
  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?
  Visualizing Tons of Tweets
  Visualizing Tweets with Tricked-Out Tag Clouds
  Visualizing Community Structures in Twitter Search Results
  Closing Remarks
6. Linkedln: Clustering Your Professional Network for Fun (and
Profit?)
  Motivation for Clustering
  Clustering Contacts by Job Title
  Standardizing and Counting Job Titles
  Common Similarity Metrics for Clustering
  A Greedy Approach to Clustering
  Hierarchical and k-Means Clustering
  Fetching Extended Profile Information
  Geographically Clustering Your Network
  Mapping Your Professional Network with Google Earth
  Mapping Your Professional Network with Dorling Cartograms
  Closing Remarks
7. Google Buzz: TF-IDF, Cosine Similarity, and Collocations
  Buzz = Twitter + Blogs (???)
  Data Hacking with NLTK
  Text Mining Fundamentals
  A Whiz-Bang Introduction tO TF-IDF
  Querying Buzz Data with TF-IDF
  Finding Similar Documents
  The Theory Behind Vector Space Models and Cosine Similarity
  Clustering Posts with Cosine Similarity
  Visualizing Similarity with Graph Visualizations
  Buzzing on Bigrams
  How the Collocation Sausage Is Made: Contingency Tables and
Scoring
  Functions
  Tapping into Your Gmail
  Accessing Gmail with OAuth
  Fetching and Parsing Email Messages
  Before You Go Off and Try to Build a Search Engine...
  Closing Remarks
8. Blogs et al.: Natural Language Processing (and Beyond)
  NLP: A Pareto-Like Introduction
  Syntax and Semantics
  A Brief Thought Exercise
  A Typical NLP Pipeline with NLTK
  Sentence Detection in Blogs with NLTK
  Summarizing Documents
  Analysis of Luhn's Summarization Algorithm
  Entity-Centric Analysis: A Deeper Understanding of the Data
  Quality of Analytics
  Closing Remarks
9. Facebook:TheAll-in-OneWonder
  Tapping into Your Social Network Data
  From Zero to Access Token in Under 10 Minutes
  Facebook's Query APIs
  Visualizing Facebook Data
  Visualizing Your Entire Social Network
  Visualizing Mutual Friendships Within Groups
  Where Have My Friends All Gone? (A Data-Driven Game)
  Visualizing Wall Data As a (Rotating) Tag Cloud
  Closing Remarks
10. The Semantic Web: A Cocktail Discussion
  An Evolutionary Revolution?
  Man Cannot Live on Facts Alone
  Open-World Versus Closed-World Assumptions
  Inferencing About an Open World with FuXi
  Hope
Index

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