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。
目录 Preface 1. Introduction: Hacking on Twitter Data Installing Python Development Tools Collecting and Manipulating Twitter Data Tinkering with Twitters 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 So 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 Whos 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 Cant 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 (Tims) Tweets Who Does Tim Retweet Most Often? Whats Tims Influence? How Many of Tims Tweets Contain Hashtags? Juxtaposing Latent So 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 Luhns Summarization Algorithm Entity-Centric Analysis: A Deeper Understanding of the Data Quality of Analytics Closing Remarks 9. Facebook:TheAll-in-OneWonder Tapping into Your So Network Data From Zero to Access Token in Under 10 Minutes Facebooks Query APIs Visualizing Facebook Data Visualizing Your Entire So 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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