圖書信息
出版社: 東南大學出版社; 第1版 (2011年6月1日)
外文書名: Mining the Social Web
平裝: 332頁
正文語種: 英語
開本: 16
ISBN: 9787564126865, 7564126868
條形碼: 9787564126865
產品尺寸及重量: 23 x 18 x 1.6 cm ; 599 g
內容簡介
《挖掘社交網路(影印版)》，本書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路數據、分析技術，如何通過可視化幫助你找到你一直在社交世界中的內容。
編輯推薦
Facebook、Twitter和Linkedln產生了大量的寶貴的社交數據，但是你怎樣才能找出誰通過社交媒介進行聯繫？他們在討論些什麼？或者他們在哪兒？《挖掘社交網路(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路數據、分析技術，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容，以及那些你都不知道存在的有用信息。
每個獨立章節介紹了在社交網路的不同領域挖掘數據的技術，這些領域包括部落格和電子郵件。你所需要具備的就是一定的編程經驗和學習基本的python工具的意願。
作者簡介
作者:(美)拉塞爾
Matthew A.Russell，Digital Reasoning Systems的工程副總裁和Zaffra的負責人，是熱愛數據挖掘、開源和網路套用技術的計算機科學家。他是《Dojo：The Definitive Guide》(O'Reilly出版)的作者。
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
