Mining the social web:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
December 2018
|
| Ausgabe: | Third edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Auf dem Umschlag: "Data mining Facebook, Twitter, LinkedIn, Instagram, Github and more" |
| Beschreibung: | xxiv, 400 Seiten Illustrationen, Diagramme |
| ISBN: | 9781491985045 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804178220021448704 |
|---|---|
| adam_text | Table of Contents
Preface............................................................................... xi
Parti. A Guided Tour of the Social Web
Prelude.................................................................. 3
1. Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking
About, and More....................................................... 5
1.1 Overview 5
1.2 Why Is Twitter All the Rage? 6
1.3 Exploring Twitters API 9
1.3.1 Fundamental Twitter Terminology 9
1.3.2 Creating a Twitter API Connection 11
1.3.3 Exploring Trending Topics 16
1.3.4 Searching for Tweets 20
1.4 Analyzing the 140 (or More) Characters 26
1.4.1 Extracting Tweet Entities 28
1.4.2 Analyzing Tweets and Tweet Entities with Frequency Analysis 30
1.4.3 Computing the Lexical Diversity of Tweets 33
1.4.4 Examining Patterns in Retweets 35
1.4.5 Visualizing Frequency Data with Histograms 37
1.5 Closing Remarks 42
1.6 Recommended Exercises 43
1.7 Online Resources 44
2. Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More.45
2.1 Overview 46
2.2 Exploring Facebooks Graph API 46
2.2.1 Understanding the Graph API 48
2.2.2 Understanding the Open Graph Protocol 52
2.3 Analyzing Social Graph Connections 59
2.3.1 Analyzing Facebook Pages 63
2.3.2 Manipulating Data Using pandas 74
2.4 Closing Remarks 83
2.5 Recommended Exercises 84
2.6 Online Resources 85
3. Mining Instagram: Computer Vision, Neural Networks, Object Recognition,
and Face Detection..................................................... 87
3.1 Overview 88
3.2 Exploring the Instagram API 89
3.2.1 Making Instagram API Requests 89
3.2.2 Retrieving Your Own Instagram Feed 92
3.2.3 Retrieving Media by Hashtag 93
3.3 Anatomy of an Instagram Post 94
3.4 Crash Course on Artificial Neural Networks 97
3.4.1 Training a Neural Network to ‘Took77 at Pictures 99
3.4.2 Recognizing Handwritten Digits 101
3.4.3 Object Recognition Within Photos Using Pretrained Neural
Networks 107
3.5 Applying Neural Networks to Instagram Posts 111
3.5.1 Tagging the Contents of an Image 111
3.5.2 Detecting Faces in Images 112
3.6 Closing Remarks 115
3.7 Recommended Exercises 115
3.8 Online Resources 116
4. Mining Linkedln; Faceting Job Titles, Clustering Colleagues, and More. 119
4.1 Overview 120
4.2 Exploring the Linkedln API 121
4.2.1 Making Linkedln API Requests 121
4.2.2 Downloading Linkedln Connections as a CSV File 125
4.3 Crash Course on Clustering Data 126
4.3.1 Normalizing Data to Enable Analysis 129
4.3.2 Measuring Similarity 141
4.3.3 Clustering Algorithms 143
4.4 Closing Remarks 159
4.5 Recommended Exercises 160
4.6 Online Resources 161
vi | Table of Contents
5. Mining Text Files: Computing Document Similarity, Extracting Collocations, and More.
..................................................................... 163
5.1 Overview 164
5.2 Text Files 164
5.3 A Whiz-Bang Introduction to TF-IDF 166
5.3.1 Term Frequency 167
5.3.2 Inverse Document Frequency 169
5.3.3 TF-IDF 170
5.4 Querying Human Language Data with TF-IDF 174
5.4.1 Introducing the Natural Language Toolkit 174
5.4.2 Applying TF-IDF to Human Language 177
5.4.3 Finding Similar Documents 179
5.4.4 Analyzing Bigrams in Human Language 187
5.4.5 Reflections on Analyzing Human Language Data 197
5.5 Closing Remarks 198
5.6 Recommended Exercises 199
5.7 Online Resources 200
6. Mining Web Pages: Using Natural Language Processing to Understand Human
Language, Summarize Blog Posts, and More............................. 201
6.1 Overview 202
6.2 Scraping, Parsing, and Crawling the Web 203
6.2.1 Breadth-First Search in Web Crawling 206
6.3 Discovering Semantics by Decoding Syntax 210
6.3.1 Natural Language Processing Illustrated Step-by-Step 212
6.3.2 Sentence Detection in Human Language Data 216
6.3.3 Document Summarization 220
6.4 Entity-Centric Analysis: A Paradigm Shift 230
6.4.1 Gisting Human Language Data 234
6.5 Quality of Analytics for Processing Human Language Data 240
6.6 Closing Remarks 242
6.7 Recommended Exercises 243
6.8 Online Resources 244
7. Mining Mailboxes: Analyzing Who s Talking to Whom About What,
How Often, and More.................................................. 247
7.1 Overview 248
7.2 Obtaining and Processing a Mail Corpus 249
7.2.1 A Primer on Unix Mailboxes 249
7.2.2 Getting the Enron Data 254
7.2.3 Converting a Mail Corpus to a Unix Mailbox 256
7.2.4 Converting Unix Mailboxes to pandas DataFrames 258
Table of Contents | vii
7.3 Analyzing the Enron Corpus 261
7.3.1 Querying by Date/Time Range 262
7.3.2 Analyzing Patterns in Sender/Recipient Communications 266
7.3.3 Searching Emails by Keywords 269
7.4 Analyzing Your Own Mail Data 271
7.4.1 Accessing Your Gmail with OAuth 273
7.4.2 Fetching and Parsing Email Messages 275
7.4.3 Visualizing Patterns in Email with Immersion 278
7.5 Closing Remarks 278
7.6 Recommended Exercises 279
7.7 Online Resources 280
8. Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs,
and More................................................................ 283
8.1 Overview 284
8.2 Exploring GitHub’s API 285
8.2.1 Creating a GitHub API Connection 286
8.2.2 Making GitHub API Requests 290
8.3 Modeling Data with Property Graphs 292
8.4 Analyzing GitHub Interest Graphs 296
8.4.1 Seeding an Interest Graph 296
8.4.2 Computing Graph Centrality Measures 300
8.4.3 Extending the Interest Graph with “Follows” Edges for Users 303
8.4.4 Using Nodes as Pivots for More Efficient Queries 315
8.4.5 Visualizing Interest Graphs 320
8.5 Closing Remarks 322
8.6 Recommended Exercises 323
8.7 Online Resources 324
Part II. Twitter Cookbook
9. Twitter Cookbook..................................................... 329
9.1 Accessing Twitters API for Development Purposes 330
9.2 Doing the OAuth Dance to Access Twitter s API for Production Purposes 332
9.3 Discovering the Trending Topics 336
9.4 Searching for Tweets 337
9.5 Constructing Convenient Function Calls 339
9.6 Saving and Restoring JSON Data with Text Files 340
9.7 Saving and Accessing JSON Data with MongoDB 341
9.8 Sampling the Twitter Firehose with the Streaming API 344
9.9 Collecting Time-Series Data 346
viii | Table of Contents
9.10 Extracting Tweet Entities 347
9.11 Finding the Most Popular Tweets in a Collection of Tweets 349
9.12 Finding the Most Popular Tweet Entities in a Collection of Tweets 351
9.13 Tabulating Frequency Analysis 352
9.14 Finding Users Who Have Retweeted a Status 353
9.15 Extracting a Retweets Attribution 355
9.16 Making Robust Twitter Requests 357
9.17 Resolving User Profile Information 359
9.18 Extracting Tweet Entities from Arbitrary Text 361
9.19 Getting All Friends or Followers for a User 361
9.20 Analyzing a User’s Friends and Followers 364
9.21 Harvesting a User’s Tweets 365
9.22 Crawling a Friendship Graph 367
9.23 Analyzing Tweet Content 369
9.24 Summarizing Link Targets 371
9.25 Analyzing a Users Favorite Tweets 374
9.26 Closing Remarks 375
9.27 Recommended Exercises 376
9.28 Online Resources 377
Part III. Appendixes
A. Information About This Book s Virtual Machine Experience............ 381
B. OAuth Primer......................................................... 383
C. Python and Jupyter Notebook Tips and Tricks.......................... 389
Index..................................................................... 391
Table of Contents | ix
|
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| spelling | Russell, Matthew A. Verfasser (DE-588)1147076324 aut Mining the social web Matthew A. Russell and Mikhail Klassen Third edition Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly December 2018 © 2019 xxiv, 400 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Auf dem Umschlag: "Data mining Facebook, Twitter, LinkedIn, Instagram, Github and more" Data Mining (DE-588)4428654-5 gnd rswk-swf World Wide Web 2.0 (DE-588)7548364-6 gnd rswk-swf Soziale Software (DE-588)7550143-0 gnd rswk-swf World Wide Web 2.0 (DE-588)7548364-6 s Soziale Software (DE-588)7550143-0 s Data Mining (DE-588)4428654-5 s DE-604 Klassen, Mikhail Verfasser (DE-588)117705129X aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030123428&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Russell, Matthew A. Klassen, Mikhail Mining the social web Data Mining (DE-588)4428654-5 gnd World Wide Web 2.0 (DE-588)7548364-6 gnd Soziale Software (DE-588)7550143-0 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)7548364-6 (DE-588)7550143-0 |
| title | Mining the social web |
| title_auth | Mining the social web |
| title_exact_search | Mining the social web |
| title_full | Mining the social web Matthew A. Russell and Mikhail Klassen |
| title_fullStr | Mining the social web Matthew A. Russell and Mikhail Klassen |
| title_full_unstemmed | Mining the social web Matthew A. Russell and Mikhail Klassen |
| title_short | Mining the social web |
| title_sort | mining the social web |
| topic | Data Mining (DE-588)4428654-5 gnd World Wide Web 2.0 (DE-588)7548364-6 gnd Soziale Software (DE-588)7550143-0 gnd |
| topic_facet | Data Mining World Wide Web 2.0 Soziale Software |
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