ソーシャル・ウェブ・マイニング(第3版)<br>Mining the Social Web (3TH)

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ソーシャル・ウェブ・マイニング(第3版)
Mining the Social Web (3TH)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 400 p.
  • 言語 ENG
  • 商品コード 9781491985045
  • DDC分類 006.312

Full Description


Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media-including who's connecting with whom, what they're talking about, and where they're located-using Python code examples, Jupyter notebooks, or Docker containers. In part one, each standalone chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blogs and feeds, mailboxes, GitHub, and a newly added chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter. Get a straightforward synopsis of the social web landscape Use Docker to easily run each chapter's example code, packaged as a Jupyter notebook Adapt and contribute to the code's open source GitHub repository Learn how to employ best-in-class Python 3 tools to slice and dice the data you collect Apply advanced mining techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognition Build beautiful data visualizations with Python and JavaScript toolkits

Table of Contents

Preface                                            xi
Part I. A Guided Tour of the Social Web
Prelude 3 (2)
1 Mining Twitter: Exploring Trending Topics, 5 (40)
Discovering What People Are Talking About,
and More
1.1 Overview 5 (1)
1.2 Why Is Twitter All the Rage? 6 (3)
1.3 Exploring Twitter's API 9 (17)
1.3.1 Fundamental Twitter Terminology 9 (2)
1.3.2 Creating a Twitter API Connection 11 (5)
1.3.3 Exploring Trending Topics 16 (4)
1.3.4 Searching for Tweets 20 (6)
1.4 Analyzing the 140 (or More) Characters 26 (16)
1.4.1 Extracting Tweet Entities 28 (2)
1.4.2 Analyzing Tweets and Tweet Entities 30 (3)
with Frequency Analysis
1.4.3 Computing the Lexical Diversity of 33 (2)
Tweets
1.4.4 Examining Patterns in Retweets 35 (2)
1.4.5 Visualizing Frequency Data with 37 (5)
Histograms
1.5 Closing Remarks 42 (1)
1.6 Recommended Exercises 43 (1)
1.7 Online Resources 44 (1)
2 Mining Facebook: Analyzing Fan Pages, 45 (42)
Examining Friendships, and More
2.1 Overview 46 (1)
2.2 Exploring Facebook's Graph API 46 (13)
2.2.1 Understanding the Graph API 48 (4)
2.2.2 Understanding the Open Graph 52 (7)
Protocol
2.3 Analyzing Social Graph Connections 59 (24)
2.3.1 Analyzing Facebook Pages 63 (11)
2.3.2 Manipulating Data Using pandas 74 (9)
2.4 Closing Remarks 83 (1)
2.5 Recommended Exercises 84 (1)
2.6 Online Resources 85 (2)
3 Mining Instagram: Computer Vision, Neural 87 (32)
Networks, Object Recognition, and Face
Detection
3.1 Overview 88 (1)
3.2 Exploring the Instagram API 89 (5)
3.2.1 Making Instagram API Requests 89 (3)
3.2.2 Retrieving Your Own Instagram Feed 92 (1)
3.2.3 Retrieving Media by Hashtag 93 (1)
3.3 Anatomy of an Instagram Post 94 (3)
3.4 Crash Course on Artificial Neural 97 (14)
Networks
3.4.1 Training a Neural Network to 99 (2)
"Look" at Pictures
3.4.2 Recognizing Handwritten Digits 101(6)
3.4.3 Object Recognition Within Photos 107(4)
Using Pretrained Neural Networks
3.5 Applying Neural Networks to Instagram 111(4)
Posts
3.5.1 Tagging the Contents of an Image 111(1)
3.5.2 Detecting Faces in Images 112(3)
3.6 Closing Remarks 115(1)
3.7 Recommended Exercises 115(1)
3.8 Online Resources 116(3)
4 Mining LinkedIn: Faceting Job Titles, 119(44)
Clustering Colleagues, and More
4.1 Overview 120(1)
4.2 Exploring the LinkedIn API 121(5)
4.2.1 Making LinkedIn API Requests 121(4)
4.2.2 Downloading LinkedIn Connections as 125(1)
a CSV File
4.3 Crash Course on Clustering Data 126(33)
4.3.1 Normalizing Data to Enable Analysis 129(12)
4.3.2 Measuring Similarity 141(2)
4.3.3 Clustering Algorithms 143(16)
4.4 Closing Remarks 159(1)
4.5 Recommended Exercises 160(1)
4.6 Online Resources 161(2)
5 Mining Text Files: Computing Document 163(38)
Similarity, Extracting Collocations, and More.
5.1 Overview 164(1)
5.2 Text Files 164(2)
5.3 A Whiz-Bang Introduction to TF-IDF 166(8)
5.3.1 Term Frequency 167(2)
5.3.2 Inverse Document Frequency 169(1)
5.3.3 TF-IDF 170(4)
5.4 Querying Human Language Data with TF-IDF 174(24)
5.4.1 Introducing the Natural Language 174(3)
Toolkit
5.4.2 Applying TF-IDF to Human Language 177(2)
5.4.3 Finding Similar Documents 179(8)
5.4.4 Analyzing Bigrams in Human Language 187(10)
5.4.5 Reflections on Analyzing Human 197(1)
Language Data
5.5 Closing Remarks 198(1)
5.6 Recommended Exercises 199(1)
5.7 Online Resources 200(1)
6 Mining Web Pages: Using Natural Language 201(46)
Processing to Understand Human Language,
Summarize Blog Posts, and More
6.1 Overview 202(1)
6.2 Scraping, Parsing, and Crawling the Web 203(7)
6.2.1 Breadth-First Search in Web Crawling 206(4)
6.3 Discovering Semantics by Decoding Syntax 210(20)
6.3.1 Natural Language Processing 212(4)
Illustrated Step-by-Step
6.3.2 Sentence Detection in Human 216(4)
Language Data
6.3.3 Document Summarization 220(10)
6.4 Entity-Centric Analysis: A Paradigm 230(10)
Shift
6.4.1 Gisting Human Language Data 234(6)
6.5 Quality of Analytics for Processing 240(2)
Human Language Data
6.6 Closing Remarks 242(1)
6.7 Recommended Exercises 243(1)
6.8 Online Resources 244(3)
7 Mining Mailboxes: Analyzing Who's 247(36)
Talking to Whom About What, How Often, and
More
7.1 Overview 248(1)
7.2 Obtaining and Processing a Mail Corpus 249(12)
7.2.1 A Primer on Unix Mailboxes 249(5)
7.2.2 Getting the Enron Data 254(2)
7.2.3 Converting a Mail Corpus to a Unix 256(2)
Mailbox
7.2.4 Converting Unix Mailboxes to pandas 258(3)
DataFrames
7.3 Analyzing the Enron Corpus 261(10)
7.3.1 Querying by Date/Time Range 262(4)
7.3.2 Analyzing Patterns in 266(3)
Sender/Recipient Communications
7.3.3 Searching Emails by Keywords 269(2)
7.4 Analyzing Your Own Mail Data 271(7)
7.4.1 Accessing Your Gmail with OAuth 273(2)
7.4.2 Fetching and Parsing Email Messages 275(3)
7.4.3 Visualizing Patterns in Email with 278(1)
Immersion
7.5 Closing Remarks 278(1)
7.6 Recommended Exercises 279(1)
7.7 Online Resources 280(3)
8 Mining GitHub: Inspecting Software 283(46)
Collaboration Habits, Building Interest
Graphs, and More
8.1 Overview 284(1)
8.2 Exploring GitHub's API 285(7)
8.2.1 Creating a GitHub API Connection 286(4)
8.2.2 Making GitHub API Requests 290(2)
8.3 Modeling Data with Property Graphs 292(4)
8.4 Analyzing GitHub Interest Graphs 296(26)
8.4.1 Seeding an Interest Graph 296(4)
8.4.2 Computing Graph Centrality Measures 300(3)
8.4.3 Extending the Interest Graph with 303(12)
"Follows" Edges for Users
8.4.4 Using Nodes as Pivots for More 315(5)
Efficient Queries
8.4.5 Visualizing Interest Graphs 320(2)
8.5 Closing Remarks 322(1)
8.6 Recommended Exercises 323(1)
8.7 Online Resources 324(5)
Part II. Twitter Cookbook
9 Twitter Cookbook 329(52)
9.1 Accessing Twitter's API for 330(2)
Development Purposes
9.2 Doing the OAuth Dance to Access 332(4)
Twitter's API for Production Purposes
9.3 Discovering the Trending Topics 336(1)
9.4 Searching for Tweets 337(2)
9.5 Constructing Convenient Function Calls 339(1)
9.6 Saving and Restoring JSON Data with 340(1)
Text Files
9.7 Saving and Accessing JSON Data with 341(3)
MongoDB
9.8 Sampling the Twitter Firehose with the 344(2)
Streaming API
9.9 Collecting Time Series Data 346(1)
9.10 Extracting Tweet Entities 347(2)
9.11 Finding the Most Popular Tweets in a 349(2)
Collection of Tweets
9.12 Finding the Most Popular Tweet 351(1)
Entities in a Collection of Tweets
9.13 Tabulating Frequency Analysis 352(1)
9.14 Finding Users Who Have Retweeted a 353(2)
Status
9.15 Extracting a Retweet's Attribution 355(2)
9.16 Making Robust Twitter Requests 357(2)
9.17 Resolving User Profile Information 359(2)
9.18 Extracting Tweet Entities from 361(1)
Arbitrary Text
9.19 Getting All Friends or Followers for a 361(3)
User
9.20 Analyzing a User's Friends and 364(1)
Followers
9.21 Harvesting a User's Tweets 365(2)
9.22 Crawling a Friendship Graph 367(2)
9.23 Analyzing Tweet Content 369(2)
9.24 Summarizing Link Targets 371(3)
9.25 Analyzing a User's Favorite Tweets 374(1)
9.26 Closing Remarks 375(1)
9.27 Recommended Exercises 376(1)
9.28 Online Resources 377
Part III. Appendixes
A Information About This Book's Virtual 381(2)
Machine Experience
B OAuth Primer 383(6)
C Python and Jupyter Notebook Tips and Tricks 389(2)
Index 391