挖掘社交网络

出版时间：2011-5  出版社：东南大学出版社  作者：Matthew A. Russell  页数：332  
Tag标签：无  

内容概要

Facebook、Twitter和Linkedln产生了大量的宝贵的社交数据，但是你怎样才能找出谁通过社交媒介进行联系？他们在讨论些什么？或者他们在哪儿？《挖掘社交网络(影印版)》这本简洁而且具有操作性的书将为你展示如何回答这些甚至更多的问题。你将学到如何组合社交网络数据、分析技术，如何通过可视化帮助你找到你一直在社交世界中寻找的内容，以及那些你都不知道存在的有用信息。
每个独立章节介绍了在社交网络的不同领域挖掘数据的技术，这些领域包括博客和电子邮件。你所需要具备的就是一定的编程经验和学习基本的python工具的意愿。

作者简介

Matthew A.Russell，Digital Reasoning
Systems的工程副总裁和Zaffra的负责人，是热爱数据挖掘、开源和网络应用技术的计算机科学家。他是《Dojo：The
Definitive Guide》(O'Reilly出版)的作者。

书籍目录

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

用户评论 (总计8条)

•   书的内容不错，但是需要有一定数据挖掘或者机器学习的基础，不然挺吃力的，最好对python不陌生，如果是完全不明白可能需要花功夫了，另外在跑实验例子的时候由于twitter的api地址改了，需要google一下
•   不经过翻译的书，经典
•   希望可以用作实践指导！
•   好书，强烈推荐！！
•   这本书对数据挖掘尤其是图的挖掘很有用
•   有点偏技术，不过也能看到一些Ideas
•   书的质量，电商两方面都不错。
•   发现其实没有那么多的价值。并且这本书好贵好贵。。。。
  记得一个老师说过，如果一本书里有太多的源码的话，这本书就没有多少价值，这本书有好多源码。