A note on the digital index
A link in an index entry is displayed as the section title in which that entry appears. Because some sections have multiple index markers, it is not unusual for an entry to have several links to the same section. Clicking on any link will take you directly to the place in the text in which the marker appears.
Symbols
- 3D graph visualization, interactive, Interactive 3D Graph Visualization
- 80-20 rule (Pareto principle), To Read This Book?, Visualizing with spreadsheets (the old-fashioned way)
- @ (at symbol), beginning Twitter usernames, Extracting relationships from the tweets
A
- access token (OAuth) for Facebook desktop app, From Zero to Access Token in Under 10 Minutes, From Zero to Access Token in Under 10 Minutes
- access token for Facebook application, Tapping into Your Social Network Data
- ActivePython, Installing Python Development Tools
- address-book data, exporting LinkedIn connections as, Motivation for Clustering
- agglomerative clustering, Hierarchical clustering
- Ajax toolkits, Intelligent clustering enables compelling user experiences
- analytics, quality of (entity-centric analysis), Quality of Analytics, Quality of Analytics
- API calls, Twitter rate limits on, Extracting relationships from the tweets
- Aristotle, syllogisms, Inferencing About an Open World with FuXi
- association metrics, Common Similarity Metrics for Clustering, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- authentication, RESTful and OAuth-Cladded APIs, From Zero to Access Token in Under 10 Minutes
- (see also OAuth)
- Facebook desktop application, From Zero to Access Token in Under 10 Minutes
- authorization, RESTful and OAuth-Cladded APIs (see OAuth)
- “The Automatic Creation of Literature Abstracts”, Summarizing Documents
B
- B-Trees, Frequency by date/time range
- BeautifulSoup, A Breadth-First Crawl of XFN Data
- Berners-Lee, Tim, To Read This Book?
- BigramAssociationMeasures class, Common Similarity Metrics for Clustering
- BigramAssocMeasures class, Bigram Analysis
- bigrams, Common Similarity Metrics for Clustering, Bigram Analysis, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, Bigram Analysis, Bigram Analysis, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- collocations, contingency tables, and scoring functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- computing for a sentence using NLTK, Bigram Analysis
- computing, using NLTK in the interpreter, Common Similarity Metrics for Clustering
- using NLTK to compute collocations, Bigram Analysis
- binomial distribution, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- blogs, Blogs et al.: Natural Language Processing (and Beyond), Sentence Detection in Blogs with NLTK, Sentence Detection in Blogs with NLTK, Summarizing Documents
- harvesting data from by parsing feeds, Sentence Detection in Blogs with NLTK
- summarizing Tim O’Reilly Radar blog post (example), Summarizing Documents
- using NLTK’s tools to parse blog data, Sentence Detection in Blogs with NLTK
- branching-factor calculations for graphs of varying depths, Brief analysis of breadth-first techniques
- breadth-first techniques, A Breadth-First Crawl of XFN Data, Brief analysis of breadth-first techniques
- brief analysis of, Brief analysis of breadth-first techniques
- using breadth-first search to crawl XFN links, A Breadth-First Crawl of XFN Data
- browsers, The Graph Your (Gmail) Inbox Chrome Extension, Visualizing Mutual Friendships Within Groups
- Chrome, Graph Your Inbox Extension, The Graph Your (Gmail) Inbox Chrome Extension
- support for canvas element, Visualizing Mutual Friendships Within Groups
C
- Cantor, Georg, Elementary Set Operations
- canvas element, support by browsers, Visualizing Mutual Friendships Within Groups
- canviz tool, Closing Remarks
- cardinality of sets, Elementary Set Operations
- chi-square, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- Chrome, Graph Your Inbox Extension, The Graph Your (Gmail) Inbox Chrome Extension
- chunking, A Typical NLP Pipeline with NLTK
- chunks, A Brief Thought Exercise
- circo tool, Visualizing Tweet Graphs
- cliques, detecting and analyzing in Twitter friendship data, Clique Detection and Analysis, Clique Detection and Analysis
- closed-world versus open-world assumptions, Open-World Versus Closed-World Assumptions
- cluster module, Hierarchical and k-Means Clustering
- clustering, Clustering Contacts by Job Title, Common Similarity Metrics for Clustering, A Greedy Approach to Clustering, Scalable clustering sure ain’t easy, Scalable clustering sure ain’t easy, Intelligent clustering enables compelling user experiences, Hierarchical and k-Means Clustering, k-means clustering, k-means clustering, Geographically Clustering Your Network, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Dorling Cartograms, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity
- common similarity metrics for, Common Similarity Metrics for Clustering
- contacts by job title, Clustering Contacts by Job Title
- hierarchical, Hierarchical and k-Means Clustering
- incorporating random sampling to improve performance, Scalable clustering sure ain’t easy
- intelligent, enabling compelling user experiences, Intelligent clustering enables compelling user experiences
- k-means, k-means clustering
- LinkedIn network geographically, Geographically Clustering Your Network, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Dorling Cartograms
- mapping with Dorling Cartograms, Mapping Your Professional Network with Dorling Cartograms
- mapping with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth
- scalable, high cost of, Scalable clustering sure ain’t easy
- Tutorial on Clustering Algorithms, k-means clustering
- using cosine similarity to cluster posts, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity
- using greedy approach, A Greedy Approach to Clustering
- code examples for this book, Or Not to Read This Book?
- collections.Counter class, What are people talking about right now?
- collocations, Bigram Analysis, Bigram Analysis, Bigram Analysis, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- computing for a sentence using NLTK, Bigram Analysis
- contingency tables and scoring functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- using NLTK to compute similar to nltk.Text.collocations demo function, Bigram Analysis
- comma-separated values files, Motivation for Clustering (see CSV files)
- community structures, visualizing in Twitter search results, Visualizing Community Structures in Twitter Search Results, Closing Remarks
- company names, standardizing, Motivation for Clustering
- concurrency support, Erlang programming language, Bulk Loading Documents into CouchDB
- confidence intervals, Quality of Analytics
- connected_components( ), Extracting relationships from the tweets
- connections, exporting from LinkedIn, Motivation for Clustering
- consumer_key and consumer_secret, obtaining for Twitter application, A Lean, Mean Data-Collecting Machine
- contexts, Before You Go Off and Try to Build a Search Engine…, Syntax and Semantics
- grounding search terms in, Before You Go Off and Try to Build a Search Engine…
- semantics and, Syntax and Semantics
- contingency tables, Bigram Analysis, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- definition and example of, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- cosine similarity, Finding Similar Documents, The Theory Behind Vector Space Models and Cosine Similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity, Before You Go Off and Try to Build a Search Engine…
- clustering posts, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity
- limitations of, Before You Go Off and Try to Build a Search Engine…
- theory behind, The Theory Behind Vector Space Models and Cosine Similarity
- CouchDB, mbox: The Quick and Dirty on Unix Mailboxes, couchdb-lucene: Full-Text Indexing and More, mbox + CouchDB = Relaxed Email Analysis, mbox + CouchDB = Relaxed Email Analysis, Bulk Loading Documents into CouchDB, Bulk Loading Documents into CouchDB, Sensible Sorting, Sensible Sorting, Map/Reduce-Inspired Frequency Analysis, Frequency by date/time range, Frequency by date/time range, Frequency by sender/recipient fields, Sorting Documents by Value, couchdb-lucene: Full-Text Indexing and More, couchdb-lucene: Full-Text Indexing and More
- aggregate view of test database and an individual document, mbox + CouchDB = Relaxed Email Analysis
- B-Tree data structure, Frequency by date/time range
- bulk loading documents into, Bulk Loading Documents into CouchDB
- configuring for couchdb-lucene awareness, couchdb-lucene: Full-Text Indexing and More
- document analysis with, mbox + CouchDB = Relaxed Email Analysis
- map/reduce-inspired frequency analysis with, Map/Reduce-Inspired Frequency Analysis, Frequency by date/time range, Frequency by sender/recipient fields
- frequency by date/time range, Frequency by date/time range
- frequency by sender and recipient, Frequency by sender/recipient fields
- RESTful API, couchdb-lucene: Full-Text Indexing and More
- sorting documents by date/time value, Sensible Sorting
- sorting documents by value, Sorting Documents by Value
- utilizing map/reduce functionality, Bulk Loading Documents into CouchDB
- view functions in design document, Sensible Sorting
- couchdb-lucene, Sorting Documents by Value, couchdb-lucene: Full-Text Indexing and More, couchdb-lucene: Full-Text Indexing and More, Do frequently appearing user entities imply friendship?
- querying tweet data, Do frequently appearing user entities imply friendship?
- text-based indexing, Sorting Documents by Value
- using to get full-text indexing, couchdb-lucene: Full-Text Indexing and More, couchdb-lucene: Full-Text Indexing and More
- CouchDBBulkReader, Threading Together Conversations
- couchpy executable, Sensible Sorting
- countably infinite sets, Elementary Set Operations
- Counter class, What are people talking about right now?
- cPickle module, pickling your data, Frequency Analysis and Lexical Diversity
- cross-validation, Quality of Analytics
- CSV (comma-separated values) files, Motivation for Clustering, Standardizing and Counting Job Titles
- normalizing LinkedIn contact data, Standardizing and Counting Job Titles
- csv module, Motivation for Clustering
- curl, RESTful and OAuth-Cladded APIs
D
- data hacking tutorial, Introduction: Hacking on Twitter Data, Closing Remarks, Installing Python Development Tools, Collecting and Manipulating Twitter Data, Visualizing Tweet Graphs, Tinkering with Twitter’s API, Tinkering with Twitter’s API, Frequency Analysis and Lexical Diversity, Extracting relationships from the tweets, Visualizing Tweet Graphs, Visualizing Tweet Graphs
- collecting and manipulating Twitter data, Collecting and Manipulating Twitter Data, Visualizing Tweet Graphs, Tinkering with Twitter’s API, Tinkering with Twitter’s API, Frequency Analysis and Lexical Diversity, Extracting relationships from the tweets, Visualizing Tweet Graphs, Visualizing Tweet Graphs
- frequency analysis and lexical diversity, Frequency Analysis and Lexical Diversity, Extracting relationships from the tweets
- tinkering with Twitter API, Tinkering with Twitter’s API, Tinkering with Twitter’s API
- visualizing tweet graphs, Visualizing Tweet Graphs, Visualizing Tweet Graphs
- installing Python development tools, Installing Python Development Tools
- Data Structures section, Python tutorial, Tinkering with Twitter’s API
- dateutil package, Sensible Sorting
- degree of a node, Extracting relationships from the tweets
- demo function (NLTK modules), Data Hacking with NLTK
- dendogram of LinkedIn contacts clustered by job title, Hierarchical clustering
- design documents (CouchDB), Sensible Sorting, Sensible Sorting
- inspecting in Futon, Sensible Sorting
- Dice’s coefficient, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- difference operations, Elementary Set Operations
- digraphs, Extracting relationships from the tweets
- dirty records, Motivation for Clustering
- distance metrics (NLTK), Common Similarity Metrics for Clustering, Common Similarity Metrics for Clustering, Common Similarity Metrics for Clustering
- comparison of Jaccard and MASI distances from two sets of items, Common Similarity Metrics for Clustering
- using to compare small sets of items, Common Similarity Metrics for Clustering
- distributions, Extracting relationships from the tweets
- illustrating degree of each node in graph, Extracting relationships from the tweets
- document-centric data, analysis of, mbox + CouchDB = Relaxed Email Analysis
- documentation, Tools and Prerequisites, Tinkering with Twitter’s API, Tinkering with Twitter’s API, A Lean, Mean Data-Collecting Machine
- Python, Tools and Prerequisites
- Twitter API, Tinkering with Twitter’s API
- Twitter social graph APIs, A Lean, Mean Data-Collecting Machine
- viewing using pydoc, Tinkering with Twitter’s API
- documents, Finding Similar Documents, Visualizing Similarity with Graph Visualizations, The Theory Behind Vector Space Models and Cosine Similarity, The Theory Behind Vector Space Models and Cosine Similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity, Visualizing Similarity with Graph Visualizations, Bigram Analysis, Summarizing Documents, Analysis of Luhn’s Summarization Algorithm, Summarizing Documents, Summarizing Documents, Analysis of Luhn’s Summarization Algorithm
- finding similar documents, Finding Similar Documents, Visualizing Similarity with Graph Visualizations, The Theory Behind Vector Space Models and Cosine Similarity, The Theory Behind Vector Space Models and Cosine Similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity, Visualizing Similarity with Graph Visualizations, Bigram Analysis
- clustering posts with cosine similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity
- vector space models and cosine similarity, The Theory Behind Vector Space Models and Cosine Similarity, The Theory Behind Vector Space Models and Cosine Similarity
- visualizing similarity with graphs, Visualizing Similarity with Graph Visualizations, Bigram Analysis
- summarizing, Summarizing Documents, Analysis of Luhn’s Summarization Algorithm, Summarizing Documents, Summarizing Documents, Analysis of Luhn’s Summarization Algorithm
- analysis of Luhn’s algorithm, Summarizing Documents, Analysis of Luhn’s Summarization Algorithm
- Tim O’Reilly Radar blog post (example), Summarizing Documents
- Dojo Tree widget, Intelligent clustering enables compelling user experiences, Intelligent clustering enables compelling user experiences, Where Have My Friends All Gone? (A Data-Driven Game)
- producing JSON consumable by, Intelligent clustering enables compelling user experiences
- template for, Intelligent clustering enables compelling user experiences
- transforming data from FQL query for, Where Have My Friends All Gone? (A Data-Driven Game)
- Dorling Cartograms, Geographically Clustering Your Network, Mapping Your Professional Network with Dorling Cartograms
- mapping LinkedIn professional network with, Mapping Your Professional Network with Dorling Cartograms
- DOT language, Visualizing Tweet Graphs
E
- easy_install, Installing Python Development Tools, Tinkering with Twitter’s API
- installing twitter module, Tinkering with Twitter’s API
- edit distance, Common Similarity Metrics for Clustering
- ego graphs, Visualizing Tweet Graphs
- end of sentence (EOS) detection, Syntax and Semantics, A Typical NLP Pipeline with NLTK
- entity extraction, Entity-Centric Analysis: A Deeper Understanding of the Data
- entity-centric analysis, Entity-Centric Analysis: A Deeper Understanding of the Data, Quality of Analytics, Entity-Centric Analysis: A Deeper Understanding of the Data, Quality of Analytics, Quality of Analytics
- discovering interactions between entities, Entity-Centric Analysis: A Deeper Understanding of the Data
- quality of analytics, Quality of Analytics, Quality of Analytics
- Epicurus, Hope
- Erlang programming language, concurrency support, Bulk Loading Documents into CouchDB
- extended permissions (Facebook), From Zero to Access Token in Under 10 Minutes
- extraction, A Typical NLP Pipeline with NLTK, Entity-Centric Analysis: A Deeper Understanding of the Data
- entity extraction, Entity-Centric Analysis: A Deeper Understanding of the Data
- Extractor class, Pen : Sword :: Tweet : Machine Gun (?!?)
F
- Facebook, XFN and Friends, Facebook: The All-in-One Wonder, Facebook: The All-in-One Wonder, Closing Remarks, Facebook: The All-in-One Wonder, From Zero to Access Token in Under 10 Minutes, From Zero to Access Token in Under 10 Minutes, From Zero to Access Token in Under 10 Minutes, Facebook’s Query APIs, Slicing and dicing data with FQL, Exploring the Graph API one connection at a time, Slicing and dicing data with FQL, Slicing and dicing data with FQL, Slicing and dicing data with FQL, Visualizing Facebook Data, Visualizing Your Entire Social Network, Visualizing with RGraphs, Visualizing with a Sunburst, Visualizing with spreadsheets (the old-fashioned way), Visualizing Mutual Friendships Within Groups, Where Have My Friends All Gone? (A Data-Driven Game), Where Have My Friends All Gone? (A Data-Driven Game), Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud
- authentication documentation, From Zero to Access Token in Under 10 Minutes
- developer principles and policies, Facebook: The All-in-One Wonder
- getting an OAuth 2.0 access token for desktop app, From Zero to Access Token in Under 10 Minutes, From Zero to Access Token in Under 10 Minutes
- Open Graph protocol, XFN and Friends
- query APIs, Facebook’s Query APIs, Slicing and dicing data with FQL, Exploring the Graph API one connection at a time, Slicing and dicing data with FQL, Slicing and dicing data with FQL, Slicing and dicing data with FQL
- exploring Graph API one connection at a time, Exploring the Graph API one connection at a time, Slicing and dicing data with FQL
- writing FQL queries, Slicing and dicing data with FQL, Slicing and dicing data with FQL
- total monthly visits as of August 2010, Facebook: The All-in-One Wonder
- visualizing data, Visualizing Facebook Data, Visualizing Your Entire Social Network, Visualizing with RGraphs, Visualizing with a Sunburst, Visualizing with spreadsheets (the old-fashioned way), Visualizing Mutual Friendships Within Groups, Where Have My Friends All Gone? (A Data-Driven Game), Where Have My Friends All Gone? (A Data-Driven Game), Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud
- mutual friendships within groups, Visualizing Mutual Friendships Within Groups, Where Have My Friends All Gone? (A Data-Driven Game)
- RGraphs visualization of entire network, Visualizing Your Entire Social Network, Visualizing with RGraphs
- rotating tag cloud visualization of wall data, Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud
- spreadsheet visualization of entire network, Visualizing with spreadsheets (the old-fashioned way)
- Sunburst visualization of entire network, Visualizing with a Sunburst
- Where Have My Friends All Gone? (data-driven game), Where Have My Friends All Gone? (A Data-Driven Game), Visualizing Wall Data As a (Rotating) Tag Cloud
- Facebook Query Language, Facebook’s Query APIs (see FQL)
- facebook-python-sdk, installing, Exploring the Graph API one connection at a time
- fail whale, A Lean, Mean Data-Collecting Machine
- false negatives (FN), Quality of Analytics
- false positives (FP), Quality of Analytics
- feedparser module, Sentence Detection in Blogs with NLTK
- findall( ), Extracting relationships from the tweets
- FOAF (Friend of a Friend), XFN and Friends
- folksonomy, How Many of Tim’s Tweets Contain Hashtags?
- Food Network, Slicing and Dicing Recipes (for the Health of It)
- forward chaining, Inferencing About an Open World with FuXi
- FQL (Facebook Query Language), Facebook’s Query APIs, Slicing and dicing data with FQL, Slicing and dicing data with FQL, Slicing and dicing data with FQL, Slicing and dicing data with FQL, Slicing and dicing data with FQL, Visualizing with RGraphs, Where Have My Friends All Gone? (A Data-Driven Game)
- encapsulating FQL queries with small Python class, Slicing and dicing data with FQL
- queries to gather data for RGraph, Visualizing with RGraphs
- query to get names, current locations, and hometowns of friends, Where Have My Friends All Gone? (A Data-Driven Game)
- writing queries, Slicing and dicing data with FQL, Slicing and dicing data with FQL, Slicing and dicing data with FQL, Slicing and dicing data with FQL
- encapsulating FQL queries with Python class, Slicing and dicing data with FQL
- multiquery, Slicing and dicing data with FQL
- frequency analysis, What are people talking about right now?, What are people talking about right now?, Map/Reduce-Inspired Frequency Analysis, couchdb-lucene: Full-Text Indexing and More, Frequency by date/time range, Frequency by date/time range, Frequency by sender/recipient fields, What entities are in Tim’s tweets?, A Whiz-Bang Introduction to TF-IDF, Querying Google+ Data with TF-IDF, Querying Google+ Data with TF-IDF
- map/reduce-inspired, using CouchDB, Map/Reduce-Inspired Frequency Analysis, couchdb-lucene: Full-Text Indexing and More, Frequency by date/time range, Frequency by date/time range, Frequency by sender/recipient fields
- frequency by date/time range, Frequency by date/time range, Frequency by date/time range
- frequency by sender and recipient, Frequency by sender/recipient fields
- performing on Twitter data, using NLTK, What are people talking about right now?
- TF-IDF (term frequency-inverse document frequency), A Whiz-Bang Introduction to TF-IDF, Querying Google+ Data with TF-IDF, Querying Google+ Data with TF-IDF
- tweet entities sorted by frequency, What entities are in Tim’s tweets?
- frequency distribution, Data Hacking with NLTK, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- unigrams and bigrams in a text, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- Friend of a Friend (FOAF), XFN and Friends
- friend/follower metrics, Souping Up the Machine with Basic Friend/Follower Metrics, Calculating Similarity by Computing Common Friends and Followers, Measuring Influence, Measuring Influence, Constructing Friendship Graphs, Clique Detection and Analysis, Clique Detection and Analysis, Clique Detection and Analysis, Do frequently appearing user entities imply friendship?
- calculating Twitterer’s most popular follower, Measuring Influence
- crawling their connections, Measuring Influence
- exporting Redis data to NetworkX for graph analysis, Constructing Friendship Graphs, Clique Detection and Analysis
- finding cliques, Clique Detection and Analysis, Clique Detection and Analysis
- finding common friends/followers for multiple Twitterers, Calculating Similarity by Computing Common Friends and Followers
- finding tweet entities that are also friends, Do frequently appearing user entities imply friendship?
- friendship analysis, Facebook network, Visualizing with RGraphs, Visualizing with RGraphs, Visualizing Mutual Friendships Within Groups, Where Have My Friends All Gone? (A Data-Driven Game), Where Have My Friends All Gone? (A Data-Driven Game), Visualizing Wall Data As a (Rotating) Tag Cloud
- FQL queries to gather data for RGraph visualization, Visualizing with RGraphs
- harvesting data for JIT’s RGraph visualization, Visualizing with RGraphs
- visualizing mutual friendships within groups, Visualizing Mutual Friendships Within Groups, Where Have My Friends All Gone? (A Data-Driven Game)
- Where Have My Friends All Gone? (data-driven game), Where Have My Friends All Gone? (A Data-Driven Game), Visualizing Wall Data As a (Rotating) Tag Cloud
- full-text indexing, couchdb-lucene: Full-Text Indexing and More, couchdb-lucene: Full-Text Indexing and More, couchdb-lucene: Full-Text Indexing and More
- Lucene library, couchdb-lucene: Full-Text Indexing and More
- using couchdb-lucene, couchdb-lucene: Full-Text Indexing and More, couchdb-lucene: Full-Text Indexing and More
- Futon, mbox + CouchDB = Relaxed Email Analysis, Sensible Sorting
- inspecting design documents in, Sensible Sorting
- viewing CouchDB document collection and individual document, mbox + CouchDB = Relaxed Email Analysis
- FuXi, Inferencing About an Open World with FuXi, Inferencing About an Open World with FuXi
- results of running from command line on knowledge base, Inferencing About an Open World with FuXi
G
- GAE (Google App Engine), Tapping into Your Social Network Data
- generator functions, Sensible Sorting
- geo microformat, XFN and Friends, Geocoordinates: A Common Thread for Just About Anything, Plotting geo data via microform.at and Google Maps, Wikipedia Articles + Google Maps = Road Trip?, Wikipedia Articles + Google Maps = Road Trip?, Plotting geo data via microform.at and Google Maps
- extracting geo data from MapQuest Local page, Wikipedia Articles + Google Maps = Road Trip?
- plotting data via microform.at and Google Maps, Plotting geo data via microform.at and Google Maps
- sample markup, Wikipedia Articles + Google Maps = Road Trip?
- geographically clustering LinkedIn network, Geographically Clustering Your Network, Closing Remarks, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Dorling Cartograms
- mapping network with Dorling Cartograms, Mapping Your Professional Network with Dorling Cartograms
- mapping network with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth
- geopy module, Mapping Your Professional Network with Google Earth
- getmail, Analyzing Your Own Mail Data
- GitHub repository, Or Not to Read This Book?
- Gmail, Tapping into Your Gmail, Accessing Gmail with OAuth, Fetching and Parsing Email Messages, Fetching and Parsing Email Messages
- accessing using OAuth, Accessing Gmail with OAuth
- fetching and parsing email messages, Fetching and Parsing Email Messages, Fetching and Parsing Email Messages
- Google, Brief analysis of breadth-first techniques, Accessing Gmail with OAuth, Facebook: The All-in-One Wonder
- Social Graph API, Brief analysis of breadth-first techniques
- total monthly visits as of August 2010, Facebook: The All-in-One Wonder
- Xoauth implementation of OAuth, Accessing Gmail with OAuth
- Google App Engine (GAE), Tapping into Your Social Network Data
- Google Earth, mapping your LinkedIn network, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth
- Google Maps, Plotting geo data via microform.at and Google Maps
- Google+, Google+: TF-IDF, Cosine Similarity, and Collocations, Harvesting Google+ Data, Harvesting Google+ Data, Harvesting Google+ Data, Harvesting Google+ Data, Data Hacking with NLTK, Data Hacking with NLTK, Querying Google+ Data with TF-IDF, Querying Google+ Data with TF-IDF, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity, Visualizing Similarity with Graph Visualizations, Bigram Analysis, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- activity data, Harvesting Google+ Data
- bigram analysis, Bigram Analysis, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- collocations, contingency tables, and scoring functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- clustering posts with cosine similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity
- combining aspects of Twitter and Facebook, Harvesting Google+ Data
- frequency distribution of terms in small data sample, Data Hacking with NLTK
- hacking data using NLTK, Data Hacking with NLTK
- harvesting data from, Harvesting Google+ Data
- querying data with TF-IDF, Querying Google+ Data with TF-IDF, Querying Google+ Data with TF-IDF
- searching for profiles, Harvesting Google+ Data
- visualizing similarity with graphs, Visualizing Similarity with Graph Visualizations
- Graph API (Facebook), Facebook’s Query APIs, Exploring the Graph API one connection at a time, Slicing and dicing data with FQL, Exploring the Graph API one connection at a time
- documentation, Exploring the Graph API one connection at a time
- exploring one connection at a time, Exploring the Graph API one connection at a time, Slicing and dicing data with FQL
- Graph Your Inbox Chrome Extension, The Graph Your (Gmail) Inbox Chrome Extension
- GraphAPI class, methods, Exploring the Graph API one connection at a time
- graphs, Installing Python Development Tools, Extracting relationships from the tweets, Interactive 3D Graph Visualization, Clustering Posts with Cosine Similarity
- creating graph describing retweet data, Extracting relationships from the tweets
- creating graph of nodes and edges using NetworkX, Installing Python Development Tools
- interactive 3D graph visualization, Interactive 3D Graph Visualization
- visualizing similarity with, Clustering Posts with Cosine Similarity
- Graphviz, Visualizing Tweet Graphs, Visualizing Tweet Graphs, Visualizing Tweet Graphs, Visualizing Community Structures in Twitter Search Results
- downloading and installing, Visualizing Tweet Graphs
- online documentation, Visualizing Tweet Graphs
- showing connectedness of #JustinBieber and #TeaParty search results, Visualizing Community Structures in Twitter Search Results
- Twitter search results rendered in circular layout, Visualizing Tweet Graphs
- greedy heuristic for clustering, A Greedy Approach to Clustering
- group_level argument, db.view function, Frequency by date/time range
- GVedit, Visualizing Tweet Graphs
H
- Hadoop, Before You Go Off and Try to Build a Search Engine…
- hashtags, How Many of Tim’s Tweets Contain Hashtags?, On Average, Do #JustinBieber or #TeaParty Tweets Have More Hashtags?
- counting hashtag entities in tweets, How Many of Tim’s Tweets Contain Hashtags?
- frequency of, in tweets containing #JustinBieber or #TeaParty, On Average, Do #JustinBieber or #TeaParty Tweets Have More Hashtags?
- hCalendar microformat, XFN and Friends
- hCard microformat, XFN and Friends
- help function (NLTK modules), Data Hacking with NLTK
- hierarchical clustering, Hierarchical and k-Means Clustering
- HierarchicalClustering class, setLinkageMethod method, Hierarchical clustering
- histogram showing popularity of each friend in Facebook network, Visualizing with spreadsheets (the old-fashioned way)
- homographs, Syntax and Semantics
- hRecipe microformat, XFN and Friends, Slicing and Dicing Recipes (for the Health of It), Slicing and Dicing Recipes (for the Health of It)
- parsed results for a recipe, Slicing and Dicing Recipes (for the Health of It)
- parsing data for a recipe, Slicing and Dicing Recipes (for the Health of It)
- hResume microformat, XFN and Friends
- hReview microformat, Collecting Restaurant Reviews, Collecting Restaurant Reviews, Collecting Restaurant Reviews
- distribution for recipe review data, Collecting Restaurant Reviews
- parsing data for recipe review, Collecting Restaurant Reviews
- sample results for recipe reviews, Collecting Restaurant Reviews
- HTML, XFN and Friends, Wikipedia Articles + Google Maps = Road Trip?, Visualizing Tweets with Tricked-Out Tag Clouds, An Evolutionary Revolution?
- pages and hyperlinks (Web 1.0), An Evolutionary Revolution?
- sample geo markup, Wikipedia Articles + Google Maps = Road Trip?
- semantic markup, XFN and Friends
- template displaying WP-Columbus tag cloud, Visualizing Tweets with Tricked-Out Tag Clouds
- HTTP, mbox + CouchDB = Relaxed Email Analysis, A Lean, Mean Data-Collecting Machine, An Evolutionary Revolution?
- errors in Twitter, A Lean, Mean Data-Collecting Machine
- methods, acting upon URIs, mbox + CouchDB = Relaxed Email Analysis
- httplib module, couchdb-lucene: Full-Text Indexing and More
- hypertext, An Evolutionary Revolution?
I
- identity consolidation, Brief analysis of breadth-first techniques
- IDF (inverse document frequency), A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF
- (see also TF-IDF)
- calculation of, A Whiz-Bang Introduction to TF-IDF
- idf function, A Whiz-Bang Introduction to TF-IDF
- IETF OAuth 2.0 protocol, No, You Can’t Have My Password
- IMAP (Internet Message Access Protocol), Analyzing Your Own Mail Data, Accessing Gmail with OAuth, Fetching and Parsing Email Messages
- connecting to, using OAuth, Accessing Gmail with OAuth
- constructing an IMAP query, Fetching and Parsing Email Messages
- imaplib, Fetching and Parsing Email Messages
- ImportError, Installing Python Development Tools
- indexing function, JavaScript-based, couchdb-lucene: Full-Text Indexing and More
- inference, Open-World Versus Closed-World Assumptions, Inferencing About an Open World with FuXi
- application to machine knowledge, Inferencing About an Open World with FuXi
- in logic-based programming languages and RDF, Open-World Versus Closed-World Assumptions
- influence, measuring for Twitter users, Measuring Influence, Measuring Influence, Measuring Influence, Measuring Influence
- calculating Twitterer’s most popular followers, Measuring Influence
- crawling friends/followers connections, Measuring Influence
- Infochimps, Strong Links API, The Infochimps “Strong Links” API, Interactive 3D Graph Visualization
- information retrieval industry, Before You Go Off and Try to Build a Search Engine…
- information retrieval theory, Text Mining Fundamentals (see IR theory)
- intelligent clustering, Intelligent clustering enables compelling user experiences
- interactive 3D graph visualization, Interactive 3D Graph Visualization
- interactive 3D tag clouds for tweet entities co-occurring with #JustinBieber and #TeaParty, Visualizing Tweets with Tricked-Out Tag Clouds
- interpreter, Python (IPython), Closing Remarks
- intersection operations, Elementary Set Operations, How Much Overlap Exists Between the Entities of #TeaParty and #JustinBieber Tweets?
- overlap between entities of #TeaParty and #JustinBieber tweets, How Much Overlap Exists Between the Entities of #TeaParty and #JustinBieber Tweets?
- IR (information retrieval) theory, Text Mining Fundamentals, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF, Querying Google+ Data with TF-IDF, Querying Google+ Data with TF-IDF, The Theory Behind Vector Space Models and Cosine Similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity
- finding similar documents using cosine similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity
- introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF
- vector space models and cosine similarity, The Theory Behind Vector Space Models and Cosine Similarity
- irrational numbers, Elementary Set Operations
J
- Jaccard distance, Common Similarity Metrics for Clustering, Common Similarity Metrics for Clustering
- comparing with MASI distance for two sets of items, Common Similarity Metrics for Clustering
- defined, Common Similarity Metrics for Clustering
- Jaccard Index, Bigram Analysis, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- Java-based search engine library (Lucene), couchdb-lucene: Full-Text Indexing and More
- JavaScript, Sensible Sorting, couchdb-lucene: Full-Text Indexing and More
- indexing function based on, couchdb-lucene: Full-Text Indexing and More
- map/reduce functions in CouchDB, Sensible Sorting
- JavaScript InfoVis Toolkit, Visualizing Your Entire Social Network (see JIT)
- JavaScript Object Notation, Tinkering with Twitter’s API (see JSON)
- JIT (JavaScript InfoVis Toolkit), Visualizing Your Entire Social Network, Visualizing with RGraphs, Visualizing with RGraphs, Visualizing with a Sunburst
- RGraph visualization of Facebook network, Visualizing with RGraphs, Visualizing with RGraphs
- Sunburst visualization of Facebook network, Visualizing with a Sunburst
- job titles, Motivation for Clustering, Motivation for Clustering, Clustering Contacts by Job Title, Common Similarity Metrics for Clustering, A Greedy Approach to Clustering, Hierarchical and k-Means Clustering, k-means clustering
- clustering contacts by, Motivation for Clustering, Clustering Contacts by Job Title, Common Similarity Metrics for Clustering, A Greedy Approach to Clustering, Hierarchical and k-Means Clustering, k-means clustering
- common similarity metrics for clustering, Common Similarity Metrics for Clustering
- greedy approach to clustering, A Greedy Approach to Clustering
- hierarchical clustering, Hierarchical and k-Means Clustering
- k-means clustering, k-means clustering
- standardizing and counting job titles, Motivation for Clustering
- lack of standardization in, Motivation for Clustering
- JSON, Tinkering with Twitter’s API, mbox: The Quick and Dirty on Unix Mailboxes, mbox: The Quick and Dirty on Unix Mailboxes, Bulk Loading Documents into CouchDB, Souping Up the Machine with Basic Friend/Follower Metrics, Intelligent clustering enables compelling user experiences, Where Have My Friends All Gone? (A Data-Driven Game)
- consumable by Dojo’s Tree widget, producing, Intelligent clustering enables compelling user experiences
- converting mbox data to, mbox: The Quick and Dirty on Unix Mailboxes
- data produced for consumption by Dojo Tree widget, Where Have My Friends All Gone? (A Data-Driven Game)
- example user object represented as JSON data, Souping Up the Machine with Basic Friend/Follower Metrics
- pretty-printing Twitter data as, Tinkering with Twitter’s API
- sample output from script converting mbox data to JSON, mbox: The Quick and Dirty on Unix Mailboxes
- script loading data into CouchDB, Bulk Loading Documents into CouchDB
- json package, Tinkering with Twitter’s API
- JVM (Java Virtual Machine), couchdb-lucene: Full-Text Indexing and More
- jwz threading, Threading Together Conversations
K
- k-means clustering, k-means clustering
- keyword search capabilities in applications, couchdb-lucene: Full-Text Indexing and More
- KMeansClustering class, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth
- feeding geocoordinates into, Mapping Your Professional Network with Google Earth
- getclusters method, Mapping Your Professional Network with Google Earth
- KML (Keyhole Markup Language), Plotting geo data via microform.at and Google Maps, Mapping Your Professional Network with Google Earth
- constructing to feed into Google Earth, Mapping Your Professional Network with Google Earth
L
- Levenshtein distance, Common Similarity Metrics for Clustering
- lexical diversity, Frequency Analysis and Lexical Diversity
- likelihood ratio, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- LinkedIn, LinkedIn: Clustering Your Professional Network for Fun (and Profit?), Closing Remarks, Motivation for Clustering, Motivation for Clustering, Motivation for Clustering, Motivation for Clustering, Clustering Contacts by Job Title, k-means clustering, Standardizing and Counting Job Titles, Standardizing and Counting Job Titles, Common Similarity Metrics for Clustering, Common Similarity Metrics for Clustering, A Greedy Approach to Clustering, Intelligent clustering enables compelling user experiences, Hierarchical and k-Means Clustering, k-means clustering, Fetching Extended Profile Information, Fetching Extended Profile Information, Fetching Extended Profile Information, Geographically Clustering Your Network, Closing Remarks, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Dorling Cartograms
- clustering contacts by job title, Motivation for Clustering, Clustering Contacts by Job Title, k-means clustering, Standardizing and Counting Job Titles, Standardizing and Counting Job Titles, Common Similarity Metrics for Clustering, Common Similarity Metrics for Clustering, A Greedy Approach to Clustering, Intelligent clustering enables compelling user experiences, Hierarchical and k-Means Clustering, k-means clustering
- common similarity metrics for clustering, Common Similarity Metrics for Clustering, Common Similarity Metrics for Clustering
- hierarchical clustering, Hierarchical and k-Means Clustering
- k-means clustering, k-means clustering
- standardizing and counting job titles, Standardizing and Counting Job Titles, Standardizing and Counting Job Titles
- standardizing company names, Motivation for Clustering
- using greedy approach, A Greedy Approach to Clustering, Intelligent clustering enables compelling user experiences
- developer signup and getting API credentials, Fetching Extended Profile Information
- fetching extended profile information, Fetching Extended Profile Information, Fetching Extended Profile Information
- geographically clustering your network, Geographically Clustering Your Network, Closing Remarks, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Dorling Cartograms
- mapping network with Dorling Cartograms, Mapping Your Professional Network with Dorling Cartograms
- mapping, using Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth
- job titles, problems with, Motivation for Clustering
- motivation for clustering data, Motivation for Clustering
- normalization of company suffixes from address book data, Motivation for Clustering
- linkedin module, Fetching Extended Profile Information
- Linux/Unix environment, Or Not to Read This Book?
- list comprehensions in Python, Tinkering with Twitter’s API
- logic-based programming languages, Open-World Versus Closed-World Assumptions
- Lucene, Sorting Documents by Value, couchdb-lucene: Full-Text Indexing and More, couchdb-lucene: Full-Text Indexing and More
- (see also couchdb-lucene)
- techniques for scoring documents, couchdb-lucene: Full-Text Indexing and More
- Luhn, H.P., Summarizing Documents, Analysis of Luhn’s Summarization Algorithm
- analysis of summarization algorithm, Analysis of Luhn’s Summarization Algorithm
M
- mail events, visualizing with SIMILE Timeline, Visualizing Mail “Events” with SIMILE Timeline, Analyzing Your Own Mail Data
- mail headers, extended, viewing through clients’ options, mbox: The Quick and Dirty on Unix Mailboxes
- Mail Trends project, Threading Together Conversations
- mailboxes, Mailboxes: Oldies but Goodies, Closing Remarks, mbox: The Quick and Dirty on Unix Mailboxes, mbox: The Quick and Dirty on Unix Mailboxes, mbox + CouchDB = Relaxed Email Analysis, couchdb-lucene: Full-Text Indexing and More, Bulk Loading Documents into CouchDB, Sensible Sorting, Map/Reduce-Inspired Frequency Analysis, Map/Reduce-Inspired Frequency Analysis, Sorting Documents by Value, Sorting Documents by Value, couchdb-lucene: Full-Text Indexing and More, couchdb-lucene: Full-Text Indexing and More, Threading Together Conversations, Look Who’s Talking, Look Who’s Talking, Look Who’s Talking, Visualizing Mail “Events” with SIMILE Timeline, Analyzing Your Own Mail Data, Analyzing Your Own Mail Data, The Graph Your (Gmail) Inbox Chrome Extension
- analyzing your own mail data, Analyzing Your Own Mail Data, The Graph Your (Gmail) Inbox Chrome Extension
- Graph Your Inbox Chrome Extension, The Graph Your (Gmail) Inbox Chrome Extension
- email analysis with mbox and CouchDB, mbox + CouchDB = Relaxed Email Analysis, couchdb-lucene: Full-Text Indexing and More, Bulk Loading Documents into CouchDB, Sensible Sorting, Map/Reduce-Inspired Frequency Analysis, Map/Reduce-Inspired Frequency Analysis, Sorting Documents by Value, Sorting Documents by Value, couchdb-lucene: Full-Text Indexing and More, couchdb-lucene: Full-Text Indexing and More
- bulk loading documents into CouchDB, Bulk Loading Documents into CouchDB
- couchdb-lucene, full-text indexing, couchdb-lucene: Full-Text Indexing and More, couchdb-lucene: Full-Text Indexing and More
- map/reduce-inspired frequency analysis, Map/Reduce-Inspired Frequency Analysis, Sorting Documents by Value
- sensible sorting of documents, Sensible Sorting, Map/Reduce-Inspired Frequency Analysis
- sorting documents by value, Sorting Documents by Value
- mbox, mbox: The Quick and Dirty on Unix Mailboxes, mbox: The Quick and Dirty on Unix Mailboxes
- threading conversations together, Threading Together Conversations, Look Who’s Talking, Look Who’s Talking, Look Who’s Talking
- robust approach, using mbox data, Look Who’s Talking, Look Who’s Talking
- visualizing mail events with SIMILE Timeline, Visualizing Mail “Events” with SIMILE Timeline, Analyzing Your Own Mail Data
- map/reduce, mbox + CouchDB = Relaxed Email Analysis, Bulk Loading Documents into CouchDB, Sensible Sorting, Frequency by date/time range, Frequency by date/time range, Frequency by sender/recipient fields, couchdb-lucene: Full-Text Indexing and More
- CouchDB capabilities, mbox + CouchDB = Relaxed Email Analysis
- counting messages by sender and recipient, Frequency by sender/recipient fields
- counting number of messages written by date, Frequency by date/time range, Frequency by date/time range
- definition of CouchDB functionality, Bulk Loading Documents into CouchDB
- mapper that tokenizes documents, couchdb-lucene: Full-Text Indexing and More
- mapper using Python to map documents by datetime stamps, Sensible Sorting
- mapping, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Dorling Cartograms
- LinkedIn professional network with Dorling Cartograms, Mapping Your Professional Network with Dorling Cartograms
- LinkedIn professional network with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth
- MapQuest Local page, extracting geo data from, Wikipedia Articles + Google Maps = Road Trip?
- MASI distance, Common Similarity Metrics for Clustering, Common Similarity Metrics for Clustering
- comparing with Jaccard distance for two sets of items, Common Similarity Metrics for Clustering
- defined, Common Similarity Metrics for Clustering
- matching, approximate, Motivation for Clustering
- mavens, Measuring Influence
- mbox, Mailboxes: Oldies but Goodies, mbox: The Quick and Dirty on Unix Mailboxes, mbox: The Quick and Dirty on Unix Mailboxes, mbox: The Quick and Dirty on Unix Mailboxes, mbox: The Quick and Dirty on Unix Mailboxes, Threading Together Conversations
- (see also mailboxes)
- converting mbox data into JSON structure, mbox: The Quick and Dirty on Unix Mailboxes
- creating discussion threads from data using jwz threading, Threading Together Conversations
- slice of a sample file, mbox: The Quick and Dirty on Unix Mailboxes
- Measuring Agreement on Set Valued Items, Common Similarity Metrics for Clustering (see MASI distance)
- message threading algorithm (jwz), Threading Together Conversations
- metadata, Facebook’s Query APIs, Facebook’s Query APIs
- delivered in response to OGP query, Facebook’s Query APIs
- RDFa, injection into web page, Facebook’s Query APIs
- Microdata initiative, XFN and Friends
- microformats, Microformats: Semantic Markup and Common Sense Collide, XFN and Friends, XFN and Friends, XFN and Friends, Exploring Social Connections with XFN, Brief analysis of breadth-first techniques, Geocoordinates: A Common Thread for Just About Anything, Plotting geo data via microform.at and Google Maps, Wikipedia Articles + Google Maps = Road Trip?, Plotting geo data via microform.at and Google Maps, Slicing and Dicing Recipes (for the Health of It), Slicing and Dicing Recipes (for the Health of It), Collecting Restaurant Reviews, Collecting Restaurant Reviews
- geo, Geocoordinates: A Common Thread for Just About Anything, Plotting geo data via microform.at and Google Maps, Wikipedia Articles + Google Maps = Road Trip?, Plotting geo data via microform.at and Google Maps
- extracting geo data from Wikipedia article and MapQuest Local page, Wikipedia Articles + Google Maps = Road Trip?
- plotting geo data via microform.at and Google Maps, Plotting geo data via microform.at and Google Maps
- hRecipe, Slicing and Dicing Recipes (for the Health of It), Slicing and Dicing Recipes (for the Health of It)
- hReview data for recipe reviews, Collecting Restaurant Reviews, Collecting Restaurant Reviews
- popular, for embedding structured data into web pages, XFN and Friends
- semantic markup, XFN and Friends
- XFN, XFN and Friends, Exploring Social Connections with XFN, Brief analysis of breadth-first techniques
- using to explore social connections, Exploring Social Connections with XFN, Brief analysis of breadth-first techniques
- multiquery (FQL), Slicing and dicing data with FQL
N
- n-gram similarity, Common Similarity Metrics for Clustering
- n-grams, Common Similarity Metrics for Clustering, Bigram Analysis
- n-squared problem, Motivation for Clustering
- natural language processing, Frequency Analysis and Lexical Diversity (see NLP)
- Natural Language Toolkit, Frequency Analysis and Lexical Diversity (see NLTK)
- natural numbers, Elementary Set Operations
- nested query (FQL), Slicing and dicing data with FQL
- NetworkX, Installing Python Development Tools, Installing Python Development Tools, Extracting relationships from the tweets, Extracting relationships from the tweets, Constructing Friendship Graphs, Clique Detection and Analysis
- building graph describing retweet data, Extracting relationships from the tweets, Extracting relationships from the tweets
- exporting Redis friend/follower data to for graph analysis, Constructing Friendship Graphs
- finding cliques in Twitter friendship data, Clique Detection and Analysis
- installing, Installing Python Development Tools
- using to create graph of nodes and edges, Installing Python Development Tools
- *nix (Linux/Unix) environment, Or Not to Read This Book?
- NLP (natural language processing), Blogs et al.: Natural Language Processing (and Beyond), Closing Remarks, NLP: A Pareto-Like Introduction, A Brief Thought Exercise, A Typical NLP Pipeline with NLTK, A Typical NLP Pipeline with NLTK, Sentence Detection in Blogs with NLTK, Sentence Detection in Blogs with NLTK, Summarizing Documents, Analysis of Luhn’s Summarization Algorithm, Analysis of Luhn’s Summarization Algorithm, Entity-Centric Analysis: A Deeper Understanding of the Data, Quality of Analytics, Quality of Analytics
- entity-centric analysis, Entity-Centric Analysis: A Deeper Understanding of the Data, Quality of Analytics, Quality of Analytics
- quality of analytics, Quality of Analytics
- sentence detection in blogs with NLTK, Sentence Detection in Blogs with NLTK, Sentence Detection in Blogs with NLTK
- summarizing documents, Summarizing Documents, Analysis of Luhn’s Summarization Algorithm, Analysis of Luhn’s Summarization Algorithm
- analysis of Luhn’s algorithm, Analysis of Luhn’s Summarization Algorithm
- syntax and semantics, NLP: A Pareto-Like Introduction
- thought exercise, A Brief Thought Exercise
- typical NLP pipeline with NLTK, A Typical NLP Pipeline with NLTK, A Typical NLP Pipeline with NLTK
- NLTK (Natural Language Toolkit), Frequency Analysis and Lexical Diversity, Frequency Analysis and Lexical Diversity, What are people talking about right now?, Common Similarity Metrics for Clustering, Google+: TF-IDF, Cosine Similarity, and Collocations, Data Hacking with NLTK, Data Hacking with NLTK, Bigram Analysis, A Typical NLP Pipeline with NLTK, A Typical NLP Pipeline with NLTK, Sentence Detection in Blogs with NLTK, Sentence Detection in Blogs with NLTK, Entity-Centric Analysis: A Deeper Understanding of the Data
- computing bigrams in the interpreter, Common Similarity Metrics for Clustering
- documentation, Frequency Analysis and Lexical Diversity
- downloading stopwords data, Data Hacking with NLTK
- extracting entities from a text, Entity-Centric Analysis: A Deeper Understanding of the Data
- performing basic frequency analysis with, What are people talking about right now?
- performing NLP with, A Typical NLP Pipeline with NLTK, A Typical NLP Pipeline with NLTK
- sentence detection in blogs, Sentence Detection in Blogs with NLTK, Sentence Detection in Blogs with NLTK
- using to compute bigrams and collocations, Bigram Analysis
- nltk.batch_ne_chunk function, Entity-Centric Analysis: A Deeper Understanding of the Data
- nltk.cluster.util.cosine_distance function, Clustering Posts with Cosine Similarity
- nltk.metrics.association module, Common Similarity Metrics for Clustering
- nltk.metrics.associations, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- nltk.metrics.BigramAssocMeasures.jaccard, Bigram Analysis
- nltk.stem module, Querying Google+ Data with TF-IDF
- nltk.Text.collocations demo function, Bigram Analysis
- normalization of company suffixes from address book data, Motivation for Clustering
- Notation3 (N3), Inferencing About an Open World with FuXi
- numpy module, Installing Python Development Tools
O
- OAuth, No, You Can’t Have My Password, No, You Can’t Have My Password, A Lean, Mean Data-Collecting Machine, Fetching Extended Profile Information, Accessing Gmail with OAuth, From Zero to Access Token in Under 10 Minutes, From Zero to Access Token in Under 10 Minutes
- access to IMAP and SMTP in Gmail, Accessing Gmail with OAuth
- getting access token for Facebook desktop application, From Zero to Access Token in Under 10 Minutes, From Zero to Access Token in Under 10 Minutes
- IETF OAuth 2.0 protocol, summary of major steps, No, You Can’t Have My Password
- template extracting LinkedIn OAuth verifier and displaying it, Fetching Extended Profile Information
- using to authenticate at Twitter and grab friendship data, A Lean, Mean Data-Collecting Machine
- versions, and Twitter support, No, You Can’t Have My Password
- object types supported by Facebook Graph API, Facebook’s Query APIs
- official Python tutorial, Tools and Prerequisites
- OGP, Facebook’s Query APIs (see Open Graph protocol)
- ontologies, Man Cannot Live on Facts Alone
- open authorization, No, You Can’t Have My Password (see OAuth)
- Open Graph protocol (OGP), XFN and Friends, Facebook’s Query APIs, Facebook’s Query APIs, Facebook’s Query APIs, Facebook’s Query APIs, Exploring the Graph API one connection at a time, Exploring the Graph API one connection at a time
- querying for programming groups, Exploring the Graph API one connection at a time, Exploring the Graph API one connection at a time
- sample results from query, Exploring the Graph API one connection at a time
- sample RDFa for, Facebook’s Query APIs
- sample response for query with optional metadata, Facebook’s Query APIs
- sample response to OGP query, Facebook’s Query APIs
- open-world versus closed-world assumptions, Open-World Versus Closed-World Assumptions
- overfitting training data, Quality of Analytics
- OWL, Inferencing About an Open World with FuXi
P
- Pareto principle (80-20 rule), To Read This Book?, Visualizing with spreadsheets (the old-fashioned way)
- part-of-speech (POS) tagging, A Typical NLP Pipeline with NLTK
- Penn Treebank Project, A Typical NLP Pipeline with NLTK
- Penn Treebank Tags, full listing of, Entity-Centric Analysis: A Deeper Understanding of the Data
- pickling your data, Frequency Analysis and Lexical Diversity
- PMI (Pointwise Mutual Information), How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- POP3 (Post Office Protocol version 3), Analyzing Your Own Mail Data
- POS (part-of-speech) tagging, A Typical NLP Pipeline with NLTK
- Power Law, Extracting relationships from the tweets
- precision, Quality of Analytics, Quality of Analytics
- calculating, Quality of Analytics
- privacy controls, Facebook data, Facebook’s Query APIs
- profiles, Fetching Extended Profile Information, Fetching Extended Profile Information, From Zero to Access Token in Under 10 Minutes
- Facebook users, accessing data from, From Zero to Access Token in Under 10 Minutes
- fetching extended profile information for LinkedIn members, Fetching Extended Profile Information, Fetching Extended Profile Information
- Prolog logic-based programming language, Open-World Versus Closed-World Assumptions
- protocols, used on Internet, An Evolutionary Revolution?
- Protovis, Hierarchical clustering, Mapping Your Professional Network with Dorling Cartograms, Clustering Posts with Cosine Similarity, Visualizing Similarity with Graph Visualizations
- mapping professional network with Dorling Cartograms, Mapping Your Professional Network with Dorling Cartograms
- redial tree layout and dendogram of LinkedIn contacts, Hierarchical clustering
- PunktSentenceTokenizer, Sentence Detection in Blogs with NLTK
- pydoc, running from terminal, Tinkering with Twitter’s API
- PyGraphviz, Visualizing Tweet Graphs
- Python, Tools and Prerequisites, Installing Python Development Tools, Installing Python Development Tools, Tinkering with Twitter’s API, mbox + CouchDB = Relaxed Email Analysis, Sensible Sorting, Elementary Set Operations, Slicing and dicing data with FQL, Inferencing About an Open World with FuXi
- couchdb client module, mbox + CouchDB = Relaxed Email Analysis
- encapsulating FQL queries with Python class, Slicing and dicing data with FQL
- inferencing about open world with FuXi, Inferencing About an Open World with FuXi
- installing development tools, Installing Python Development Tools
- list comprehension, Tinkering with Twitter’s API
- map/reduce functions for CouchDB, Sensible Sorting
- support for sets, Elementary Set Operations
- tutorial overview, Installing Python Development Tools
- Python SDK for the Graph API, Exploring the Graph API one connection at a time
Q
- quicksort algorithm, Sorting Documents by Value
- quopri module, mbox: The Quick and Dirty on Unix Mailboxes
- quoted-printable text, decoding, mbox: The Quick and Dirty on Unix Mailboxes
R
- radial tree layout, LinkedIn contacts clustered by job title, Hierarchical clustering
- rate-throttling limits (LinkedIn), Fetching Extended Profile Information
- rational numbers, Elementary Set Operations
- raw frequency, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- RDF (Resource Description Framework), Man Cannot Live on Facts Alone, Open-World Versus Closed-World Assumptions, Inferencing About an Open World with FuXi
- knowledge base expressed with Notation3, Inferencing About an Open World with FuXi
- open-world assumptions, Open-World Versus Closed-World Assumptions
- RDF Schema, Inferencing About an Open World with FuXi
- RDFa, XFN and Friends, Facebook’s Query APIs, Facebook’s Query APIs
- metadata, insertion into web page, Facebook’s Query APIs
- sample, for Open Graph protocol, Facebook’s Query APIs
- re module, Extracting relationships from the tweets
- recall, Quality of Analytics
- recipes and reviews in microformats, Slicing and Dicing Recipes (for the Health of It), Collecting Restaurant Reviews
- Redis, Redis: A Data Structures Server, Elementary Set Operations, Calculating Similarity by Computing Common Friends and Followers, Calculating Similarity by Computing Common Friends and Followers, Constructing Friendship Graphs, The Infochimps “Strong Links” API
- exporting data to NetworkX, Constructing Friendship Graphs
- randomkey function, Calculating Similarity by Computing Common Friends and Followers
- resolving screen names from user ID values, The Infochimps “Strong Links” API
- set operations, Elementary Set Operations
- sinterstore function, Calculating Similarity by Computing Common Friends and Followers
- reduction functions, Bulk Loading Documents into CouchDB
- regular expressions, using to find retweets, Extracting relationships from the tweets
- rereduce, Frequency by date/time range, What entities are in Tim’s tweets?
- REST-based interface, CouchDB, mbox + CouchDB = Relaxed Email Analysis
- RESTful APIs, Tinkering with Twitter’s API, RESTful and OAuth-Cladded APIs
- mapping to twitter module, Tinkering with Twitter’s API
- Twitter, RESTful and OAuth-Cladded APIs
- RESTful Web services, mbox + CouchDB = Relaxed Email Analysis
- results query (FQL), Slicing and dicing data with FQL
- retweeting, What are people talking about right now?
- retweets, Extracting relationships from the tweets, Extracting relationships from the tweets, Who Does Tim Retweet Most Often?, Who Does Tim Retweet Most Often?, What’s Tim’s Influence?, Which Gets Retweeted More Often: #JustinBieber or #TeaParty?, Which Gets Retweeted More Often: #JustinBieber or #TeaParty?
- counting for a Twitterer, Who Does Tim Retweet Most Often?
- finding tweets most often retweeted, What’s Tim’s Influence?
- finding using regular expressions, Extracting relationships from the tweets
- graph describing who retweeted whom, Extracting relationships from the tweets
- most frequent entities appearing in, Who Does Tim Retweet Most Often?
- most frequent retweeters of #JustinBieber, Which Gets Retweeted More Often: #JustinBieber or #TeaParty?
- most frequent retweeters of #TeaParty, Which Gets Retweeted More Often: #JustinBieber or #TeaParty?
- RGraphs, visualizing Facebook network, Visualizing with RGraphs, Visualizing with RGraphs
- Rich Internet Applications (RIAs), An Evolutionary Revolution?
- rotating tag cloud, visualizing Facebook wall data as, Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud
- RT (retweet) token, What are people talking about right now?
- rubhub, Exploring Social Connections with XFN
S
- sample error, Quality of Analytics
- scalable clustering, Scalable clustering sure ain’t easy
- scalable force directed placement (SFDP), Visualizing Community Structures in Twitter Search Results
- scoring functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- screen names, resolving from user IDs, Redis: A Data Structures Server, Souping Up the Machine with Basic Friend/Follower Metrics
- search API (Twitter), Tinkering with Twitter’s API
- search engines, Before You Go Off and Try to Build a Search Engine…
- semantic markup, XFN and Friends
- semantic web, The Semantic Web: A Cocktail Discussion, Hope, An Evolutionary Revolution?, Open-World Versus Closed-World Assumptions, Inferencing About an Open World with FuXi, Hope
- defined, An Evolutionary Revolution?
- inferencing about open world with FuXi, Inferencing About an Open World with FuXi
- open-world versus closed-world assumptions, Open-World Versus Closed-World Assumptions
- social web as catalyst for, Hope
- semantics, defined, An Evolutionary Revolution?
- semi-standardized relational data, Motivation for Clustering
- sentence detection, Syntax and Semantics, Sentence Detection in Blogs with NLTK, Sentence Detection in Blogs with NLTK
- in blogs, using NLTK, Sentence Detection in Blogs with NLTK, Sentence Detection in Blogs with NLTK
- sentence tokenizer, Sentence Detection in Blogs with NLTK
- set operations, Elementary Set Operations, Elementary Set Operations, How Much Overlap Exists Between the Entities of #TeaParty and #JustinBieber Tweets?
- intersection of entities in #TeaParty and #JustinBieber tweets, How Much Overlap Exists Between the Entities of #TeaParty and #JustinBieber Tweets?
- Redis native functions for, Elementary Set Operations
- sample, for Twitter friends and followers, Elementary Set Operations
- set theory, invention by Georg Cantor, Elementary Set Operations
- SFDP (scalable force directed placement), Graphviz, Visualizing Community Structures in Twitter Search Results
- similarity metrics, What Entities Co-Occur Most Often with #JustinBieber and #TeaParty Tweets?, Common Similarity Metrics for Clustering, Finding Similar Documents, The Theory Behind Vector Space Models and Cosine Similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity, Before You Go Off and Try to Build a Search Engine…
- common, for clustering, Common Similarity Metrics for Clustering
- cosine similarity, Finding Similar Documents, The Theory Behind Vector Space Models and Cosine Similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity, Before You Go Off and Try to Build a Search Engine…
- clustering posts using, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity
- limitations of, Before You Go Off and Try to Build a Search Engine…
- theory behind vector space models and, The Theory Behind Vector Space Models and Cosine Similarity
- most frequent entities co-occurring with #JustinBieber and #TeaParty tweets, What Entities Co-Occur Most Often with #JustinBieber and #TeaParty Tweets?
- visualizing similarity with graphs, Clustering Posts with Cosine Similarity
- similarity, calculating by computing common friends/followers, Calculating Similarity by Computing Common Friends and Followers
- SIMILE Timeline, Visualizing Mail “Events” with SIMILE Timeline, Analyzing Your Own Mail Data, Visualizing Mail “Events” with SIMILE Timeline, Visualizing Mail “Events” with SIMILE Timeline
- expected data format, Visualizing Mail “Events” with SIMILE Timeline
- online demonstrations, Visualizing Mail “Events” with SIMILE Timeline
- social graph APIs (Twitter), online documentation, A Lean, Mean Data-Collecting Machine
- social web, An Evolutionary Revolution?
- SocialGraph Node Mapper, Brief analysis of breadth-first techniques
- sorting, Sensible Sorting, Sorting Documents by Value
- documents by value, Sorting Documents by Value
- documents in CouchDB, Sensible Sorting
- split method, using to tokenize text, Data Hacking with NLTK, Before You Go Off and Try to Build a Search Engine…
- spreadsheets, visualizing Facebook network data, Visualizing with spreadsheets (the old-fashioned way)
- statistical models processing natural language, Quality of Analytics
- stemming verbs, Querying Google+ Data with TF-IDF
- stopwords, Data Hacking with NLTK, Analysis of Luhn’s Summarization Algorithm
- downloading NLTK stopword data, Data Hacking with NLTK
- filtering out before document summarization, Analysis of Luhn’s Summarization Algorithm
- streaming API (Twitter), Analyzing Tweets (One Entity at a Time)
- Strong Links API, The Infochimps “Strong Links” API, Interactive 3D Graph Visualization
- student’s t-score, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- subject-verb-object triples, Entity-Centric Analysis: A Deeper Understanding of the Data, Man Cannot Live on Facts Alone
- summarizing documents, Summarizing Documents, Analysis of Luhn’s Summarization Algorithm, Summarizing Documents, Analysis of Luhn’s Summarization Algorithm
- analysis of Luhn’s algorithm, Analysis of Luhn’s Summarization Algorithm
- Tim O’Reilly Radar blog post (example), Summarizing Documents
- summingReducer function, Frequency by date/time range, What entities are in Tim’s tweets?
- Sunburst visualizations, Visualizing with a Sunburst, Visualizing with a Sunburst
- harvesting and manipulating data for, Visualizing with a Sunburst
- supervised learning algorithm, Quality of Analytics
- syntax and semantics, NLP: A Pareto-Like Introduction
T
- tag clouds, Visualizing Tweets with Tricked-Out Tag Clouds, Visualizing Tweets with Tricked-Out Tag Clouds, Visualizing Tweets with Tricked-Out Tag Clouds, Visualizing Tweets with Tricked-Out Tag Clouds, Visualizing Tweets with Tricked-Out Tag Clouds, Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud
- crafting, design decisions in, Visualizing Tweets with Tricked-Out Tag Clouds
- interactive 3D tag cloud for tweet entities co-occurring with #JustinBieber and #TeaParty, Visualizing Tweets with Tricked-Out Tag Clouds
- sample HTML template displaying WP-Columbus tag cloud, Visualizing Tweets with Tricked-Out Tag Clouds
- using to visualize large number of tweets, Visualizing Tweets with Tricked-Out Tag Clouds
- visualizing Facebook wall data as rotating tag cloud, Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud
- weighting tags in, Visualizing Tweets with Tricked-Out Tag Clouds
- term frequency-inverse document frequency, A Whiz-Bang Introduction to TF-IDF (see TF-IDF)
- text mining, Google+: TF-IDF, Cosine Similarity, and Collocations, Closing Remarks, Data Hacking with NLTK, Data Hacking with NLTK, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF, Querying Google+ Data with TF-IDF, Querying Google+ Data with TF-IDF, Finding Similar Documents, Visualizing Similarity with Graph Visualizations, The Theory Behind Vector Space Models and Cosine Similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity, Bigram Analysis, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, Tapping into Your Gmail, Fetching and Parsing Email Messages, Fetching and Parsing Email Messages, Fetching and Parsing Email Messages, Before You Go Off and Try to Build a Search Engine…
- bigrams, Bigram Analysis, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- collocations, contingency tables, and scoring functions, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- data hacking with NLTK, Data Hacking with NLTK
- finding similar documents, Finding Similar Documents, Visualizing Similarity with Graph Visualizations, The Theory Behind Vector Space Models and Cosine Similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity
- clustering posts with cosine similarity, Clustering Posts with Cosine Similarity, Clustering Posts with Cosine Similarity
- theory behind vector space models and cosine similarity, The Theory Behind Vector Space Models and Cosine Similarity
- visualizing similarity with graph visualizations, Clustering Posts with Cosine Similarity
- introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF
- limitations of TF-IDF and cosine similarity, Before You Go Off and Try to Build a Search Engine…
- tapping into Gmail, Tapping into Your Gmail, Fetching and Parsing Email Messages, Fetching and Parsing Email Messages, Fetching and Parsing Email Messages
- fetching and parsing email messages, Fetching and Parsing Email Messages, Fetching and Parsing Email Messages
- text-based indexing using couchdb-lucene, Sorting Documents by Value
- TF-IDF, Text Mining Fundamentals, Querying Google+ Data with TF-IDF, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF, Querying Google+ Data with TF-IDF, Querying Google+ Data with TF-IDF, The Theory Behind Vector Space Models and Cosine Similarity, Before You Go Off and Try to Build a Search Engine…
- computing cosine similarity of documents represented as term vectors, The Theory Behind Vector Space Models and Cosine Similarity
- introduction to, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF, A Whiz-Bang Introduction to TF-IDF
- calculations involved in sample queries, A Whiz-Bang Introduction to TF-IDF
- summarized values for sample queries, A Whiz-Bang Introduction to TF-IDF
- limitations of, Before You Go Off and Try to Build a Search Engine…
- thread pools, Threading Together Conversations, Threading Together Conversations
- using to maximize read throughput from CouchDB, Threading Together Conversations
- threading conversations together, Threading Together Conversations, Look Who’s Talking, Threading Together Conversations, Splicing in the other half of the conversation, Who Does Tim Retweet Most Often?
- from mbox data, Threading Together Conversations, Look Who’s Talking, Threading Together Conversations
- thread pool maximizing read throughput from CouchDB, Threading Together Conversations
- tweet discussion threads, Splicing in the other half of the conversation, Who Does Tim Retweet Most Often?
- tokenization, couchdb-lucene: Full-Text Indexing and More, Data Hacking with NLTK, Before You Go Off and Try to Build a Search Engine…, A Typical NLP Pipeline with NLTK, Sentence Detection in Blogs with NLTK, Visualizing Wall Data As a (Rotating) Tag Cloud
- definition and example of, A Typical NLP Pipeline with NLTK
- Facebook Wall data for tag cloud visualization, Visualizing Wall Data As a (Rotating) Tag Cloud
- mapper that tokenizes documents, couchdb-lucene: Full-Text Indexing and More
- NLTK tokenizers, Sentence Detection in Blogs with NLTK
- using split method, Data Hacking with NLTK, Before You Go Off and Try to Build a Search Engine…
- TreebankWord Tokenizer, Sentence Detection in Blogs with NLTK
- trends, Twitter search, Tinkering with Twitter’s API
- TrigramAssociationMeasures class, Common Similarity Metrics for Clustering
- triples, Entity-Centric Analysis: A Deeper Understanding of the Data, Man Cannot Live on Facts Alone
- true negatives (TN), Quality of Analytics
- true positives (TP), Quality of Analytics
- Turing Test, Syntax and Semantics
- tutorials, Installing Python Development Tools, Visualizing Mail “Events” with SIMILE Timeline, k-means clustering
- Getting Started with Timeline, Visualizing Mail “Events” with SIMILE Timeline
- official Python tutorial, Installing Python Development Tools
- Tutorial on Clustering Algorithms, k-means clustering
- tweets, Collecting and Manipulating Twitter Data, What are people talking about right now?, Twitter: The Tweet, the Whole Tweet, and Nothing but the Tweet, Closing Remarks, Analyzing Tweets (One Entity at a Time), Tapping (Tim’s) Tweets, What entities are in 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), Juxtaposing Latent Social Networks (or #JustinBieber Versus #TeaParty), What Entities Co-Occur Most Often with #JustinBieber and #TeaParty Tweets?, 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?, 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
- analyzing, one entity at a time, Analyzing Tweets (One Entity at a Time)
- analyzing, questions for, Tapping (Tim’s) Tweets
- counting hashtag entities in, How Many of Tim’s Tweets Contain Hashtags?
- counting retweets for a Twitterer, Who Does Tim Retweet Most Often?
- entities sorted by frequency from harvested tweets, What entities are in Tim’s tweets?
- extracting entities and performing frequency analysis, What entities are in Tim’s tweets?
- finding tweets most often retweeted, What’s Tim’s Influence?
- information about latent social networks, Juxtaposing Latent Social Networks (or #JustinBieber Versus #TeaParty), Juxtaposing Latent Social Networks (or #JustinBieber Versus #TeaParty), What Entities Co-Occur Most Often with #JustinBieber and #TeaParty Tweets?, 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?, Which Gets Retweeted More Often: #JustinBieber or #TeaParty?, How Much Overlap Exists Between the Entities of #TeaParty and #JustinBieber Tweets?
- harvesting tweets for a topic, Juxtaposing Latent Social Networks (or #JustinBieber Versus #TeaParty)
- hashtags in tweets with #JustinBieber or #TeaParty, On Average, Do #JustinBieber or #TeaParty Tweets Have More Hashtags?
- most frequent entities in tweets containing #JustinBieber, What Entities Co-Occur Most Often with #JustinBieber and #TeaParty Tweets?
- most frequent entities in tweets containing #TeaParty, What Entities Co-Occur Most Often with #JustinBieber and #TeaParty Tweets?
- most frequent retweeters of #JustinBieber, Which Gets Retweeted More Often: #JustinBieber or #TeaParty?
- most frequent retweeters of #TeaParty, Which Gets Retweeted More Often: #JustinBieber or #TeaParty?
- overlap between entities of #TeaParty and #JustinBieber, How Much Overlap Exists Between the Entities of #TeaParty and #JustinBieber Tweets?
- querying data with couchdb-lucene, Do frequently appearing user entities imply friendship?
- reconstructing discussion threads, Splicing in the other half of the conversation
- retweeting, What are people talking about right now?
- visualizing community structures in search results, Visualizing Community Structures in Twitter Search Results, Closing Remarks
- visualizing large number of, Visualizing Tons of Tweets, Visualizing Tweets with Tricked-Out Tag Clouds
- using tag clouds, Visualizing Tweets with Tricked-Out Tag Clouds
- Twitter, Collecting and Manipulating Twitter Data, Tinkering with Twitter’s API, Tinkering with Twitter’s API, Tinkering with Twitter’s API, Tinkering with Twitter’s API, Frequency Analysis and Lexical Diversity, What are people talking about right now?, What are people talking about right now?, Extracting relationships from the tweets, Extracting relationships from the tweets, Visualizing Tweet Graphs, Visualizing Tweet Graphs, 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 Lean, Mean Data-Collecting Machine, Redis: A Data Structures Server, Elementary Set Operations, Souping Up the Machine with Basic Friend/Follower Metrics, Souping Up the Machine with Basic Friend/Follower Metrics, Souping Up the Machine with Basic Friend/Follower Metrics, Calculating Similarity by Computing Common Friends and Followers, Measuring Influence, Measuring Influence, Constructing Friendship Graphs, Interactive 3D Graph Visualization, Clique Detection and Analysis, Clique Detection and Analysis, The Infochimps “Strong Links” API, Interactive 3D Graph Visualization, Interactive 3D Graph Visualization
- collecting data from, A Lean, Mean Data-Collecting Machine, A Lean, Mean Data-Collecting Machine, Redis: A Data Structures Server, Elementary Set Operations, Souping Up the Machine with Basic Friend/Follower Metrics, Souping Up the Machine with Basic Friend/Follower Metrics, Souping Up the Machine with Basic Friend/Follower Metrics, Calculating Similarity by Computing Common Friends and Followers, Measuring Influence, Measuring Influence
- authenticating with OAuth and grabbing friendship data, A Lean, Mean Data-Collecting Machine
- basic friend/follower metrics, Souping Up the Machine with Basic Friend/Follower Metrics
- example user object represented as JSON data, Souping Up the Machine with Basic Friend/Follower Metrics
- finding common friends/followers for multiple users, Calculating Similarity by Computing Common Friends and Followers
- measure relative influence of users, Measuring Influence, Measuring Influence
- resolving user screen names from IDs, Souping Up the Machine with Basic Friend/Follower Metrics
- set operations for friends and followers, Elementary Set Operations
- using Redis, Redis: A Data Structures Server
- exploring the API, Tinkering with Twitter’s API, Tinkering with Twitter’s API, Tinkering with Twitter’s API, Tinkering with Twitter’s API
- printing data as JSON, Tinkering with Twitter’s API
- retrieving search trends, Tinkering with Twitter’s API
- extracting relationships from tweets, Extracting relationships from the tweets
- frequency analysis of tweets, What are people talking about right now?
- friendship analysis, Constructing Friendship Graphs, Interactive 3D Graph Visualization, Clique Detection and Analysis, Clique Detection and Analysis, The Infochimps “Strong Links” API, Interactive 3D Graph Visualization, Interactive 3D Graph Visualization
- detecting and analyzing cliques, Clique Detection and Analysis, Clique Detection and Analysis
- interactive 3D graph visualization of clique data, Interactive 3D Graph Visualization
- using Infochimps Strong Links API, The Infochimps “Strong Links” API, Interactive 3D Graph Visualization
- graph describing who retweeted whom, Extracting relationships from the tweets
- lexical diversity for tweets, Frequency Analysis and Lexical Diversity
- NLTK, using for frequency analysis, What are people talking about right now?
- OAuth, No, You Can’t Have My Password
- RESTful API, RESTful and OAuth-Cladded APIs
- visualizing graphs of tweet data, Visualizing Tweet Graphs, Visualizing Tweet Graphs
- Twitter class, Tinkering with Twitter’s API, Tinkering with Twitter’s API
- instantiating and invoking methods on, Tinkering with Twitter’s API
- viewing documentation for, Tinkering with Twitter’s API
- twitter module, installing with easy_install, Tinkering with Twitter’s API
- twitter.oauth module, A Lean, Mean Data-Collecting Machine
- TwitterHTTPError exceptions, A Lean, Mean Data-Collecting Machine
- twitter_search.trends( ), Tinkering with Twitter’s API
- twitter_text package, Pen : Sword :: Tweet : Machine Gun (?!?)
U
- Ubigraph, Interactive 3D Graph Visualization
- UnicodeDecodeError, mbox: The Quick and Dirty on Unix Mailboxes
- unigrams, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
- union operations, Elementary Set Operations
- URIs, HTTP methods acting upon, mbox + CouchDB = Relaxed Email Analysis
- URLs, Brief analysis of breadth-first techniques, Brief analysis of breadth-first techniques
- resolving variations in, Brief analysis of breadth-first techniques
- standardizing and resolving in identity consolidation, Brief analysis of breadth-first techniques
- user information, Extracting relationships from the tweets, Souping Up the Machine with Basic Friend/Follower Metrics
- resolving screen names from IDs, Souping Up the Machine with Basic Friend/Follower Metrics
- Twitter usernames, Extracting relationships from the tweets
- user object represented as JSON data (example), Souping Up the Machine with Basic Friend/Follower Metrics
V
- vector space models, The Theory Behind Vector Space Models and Cosine Similarity, The Theory Behind Vector Space Models and Cosine Similarity
- example vector plotted in 3D space, The Theory Behind Vector Space Models and Cosine Similarity
- vector, defined, The Theory Behind Vector Space Models and Cosine Similarity
- versions, Python, Installing Python Development Tools
- views (CouchDB), Sensible Sorting, Sensible Sorting
- visualization tools, Tools and Prerequisites
- visualizations, Visualizing Tweet Graphs, Synthesis: Visualizing Retweets with Protovis, Synthesis: Visualizing Retweets with Protovis, Visualizing Mail “Events” with SIMILE Timeline, Analyzing Your Own Mail Data, The Graph Your (Gmail) Inbox Chrome Extension, Constructing Friendship Graphs, Summary, Clique Detection and Analysis, Clique Detection and Analysis, The Infochimps “Strong Links” API, Interactive 3D Graph Visualization, Interactive 3D Graph Visualization, Visualizing Tons of Tweets, Visualizing Tweets with Tricked-Out Tag Clouds, Visualizing Community Structures in Twitter Search Results, Visualizing Community Structures in Twitter Search Results, Closing Remarks, Geographically Clustering Your Network, Mapping Your Professional Network with Dorling Cartograms, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Dorling Cartograms, Clustering Posts with Cosine Similarity, Visualizing Facebook Data, Visualizing Your Entire Social Network, Visualizing with RGraphs, Visualizing with a Sunburst, Visualizing with spreadsheets (the old-fashioned way), Visualizing Mutual Friendships Within Groups, Where Have My Friends All Gone? (A Data-Driven Game), Where Have My Friends All Gone? (A Data-Driven Game), Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud
- constructing friendship graphs from Twitter data, Constructing Friendship Graphs, Summary, Clique Detection and Analysis, Clique Detection and Analysis, The Infochimps “Strong Links” API, Interactive 3D Graph Visualization, Interactive 3D Graph Visualization
- clique detection and analysis, Clique Detection and Analysis, Clique Detection and Analysis
- Infochimps Strong Links API, The Infochimps “Strong Links” API, Interactive 3D Graph Visualization
- interactive 3D graph, Interactive 3D Graph Visualization
- Facebook data, Visualizing Facebook Data, Visualizing Your Entire Social Network, Visualizing with RGraphs, Visualizing with a Sunburst, Visualizing with spreadsheets (the old-fashioned way), Visualizing Mutual Friendships Within Groups, Where Have My Friends All Gone? (A Data-Driven Game), Where Have My Friends All Gone? (A Data-Driven Game), Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud
- visualizing mutual friendships within groups, Visualizing Mutual Friendships Within Groups, Where Have My Friends All Gone? (A Data-Driven Game)
- visualizing with RGraphs, Visualizing Your Entire Social Network, Visualizing with RGraphs
- visualizing with spreadsheets, Visualizing with spreadsheets (the old-fashioned way)
- visualizing with Sunburst, Visualizing with a Sunburst
- wall data visualized as rotating tag cloud, Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud
- Where Have My Friends Gone? (data-driven game), Where Have My Friends All Gone? (A Data-Driven Game), Visualizing Wall Data As a (Rotating) Tag Cloud
- geographical clustering of LinkedIn network, Geographically Clustering Your Network, Mapping Your Professional Network with Dorling Cartograms, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Dorling Cartograms
- mapping network with Dorling Cartograms, Mapping Your Professional Network with Dorling Cartograms
- mapping network with Google Earth, Mapping Your Professional Network with Google Earth, Mapping Your Professional Network with Google Earth
- Graph Your Inbox Chrome extension, The Graph Your (Gmail) Inbox Chrome Extension
- large number of tweets, Visualizing Tons of Tweets, Visualizing Tweets with Tricked-Out Tag Clouds, Visualizing Community Structures in Twitter Search Results, Visualizing Community Structures in Twitter Search Results, Closing Remarks
- community structures in Twitter search results, Visualizing Community Structures in Twitter Search Results, Closing Remarks
- using tag clouds, Visualizing Tweets with Tricked-Out Tag Clouds, Visualizing Community Structures in Twitter Search Results
- mail events, using SIMILE Timeline, Visualizing Mail “Events” with SIMILE Timeline, Analyzing Your Own Mail Data
- similarity, visualizing with graphs, Clustering Posts with Cosine Similarity
- tweet graphs, Visualizing Tweet Graphs, Synthesis: Visualizing Retweets with Protovis, Synthesis: Visualizing Retweets with Protovis
- visualizing retweets, using Protovis, Synthesis: Visualizing Retweets with Protovis
W
- wall data (Facebook), visualizing as rotating tag cloud, Visualizing Wall Data As a (Rotating) Tag Cloud, Visualizing Wall Data As a (Rotating) Tag Cloud
- web page for this book, How to Contact Us
- weighting tags in tag clouds, Visualizing Tweets with Tricked-Out Tag Clouds
- WhitespaceTokenizer, Sentence Detection in Blogs with NLTK
- Wikipedia articles with geo markup, Plotting geo data via microform.at and Google Maps
- Windows systems, Installing Python Development Tools, Installing Python Development Tools, Visualizing Tweet Graphs, couchdb-lucene: Full-Text Indexing and More, Visualizing Community Structures in Twitter Search Results
- ActivePython, Installing Python Development Tools
- couchdb-lucene service wrapper, couchdb-lucene: Full-Text Indexing and More
- DOT language output for Graphviz, Visualizing Community Structures in Twitter Search Results
- GVedit application, Visualizing Tweet Graphs
- installing easy_install, Installing Python Development Tools
- WolframAlpha, entity analysis with, Entity-Centric Analysis: A Deeper Understanding of the Data
- word tokenizer, Sentence Detection in Blogs with NLTK
- WP-Cumulus tag cloud, Visualizing Tweets with Tricked-Out Tag Clouds, Visualizing Tweets with Tricked-Out Tag Clouds, Visualizing Wall Data As a (Rotating) Tag Cloud
- sample HTML template displaying, Visualizing Tweets with Tricked-Out Tag Clouds
X
- XFN (XHTML Friends Network), XFN and Friends, Exploring Social Connections with XFN, Brief analysis of breadth-first techniques, Exploring Social Connections with XFN, A Breadth-First Crawl of XFN Data, A Breadth-First Crawl of XFN Data, A Breadth-First Crawl of XFN Data
- example markup, Exploring Social Connections with XFN
- pseudocode for breadth-first search, A Breadth-First Crawl of XFN Data
- scraping XFN content from a web page, A Breadth-First Crawl of XFN Data
- using breadth-first search to crawl XFN links, A Breadth-First Crawl of XFN Data
- XHTML, Microformats: Semantic Markup and Common Sense Collide, XFN and Friends
- (see also XFN)
- semantic markup versus, XFN and Friends
- Xoauth, Accessing Gmail with OAuth
- xoauth.py command-line utility, Accessing Gmail with OAuth
Z
- Zipf’s law, Extracting relationships from the tweets, Data Hacking with NLTK
- frequency distribution of words in a corpus, Data Hacking with NLTK
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