Mining the social web:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Cambrige ; Farnham ; Köln ; Sebastopol ; Tokyo
O'Reilly
2014
|
| Ausgabe: | 2nd edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIV, 421 Seiten Illustrationen, Diagramme |
| ISBN: | 9781449367619 |
Internformat
MARC
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| 245 | 1 | 0 | |a Mining the social web |c Matthew A. Russell |
| 246 | 1 | 3 | |a Data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more |
| 250 | |a 2nd edition | ||
| 264 | 1 | |a Beijing ; Cambrige ; Farnham ; Köln ; Sebastopol ; Tokyo |b O'Reilly |c 2014 | |
| 300 | |a XXIV, 421 Seiten |b Illustrationen, Diagramme | ||
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Datensatz im Suchindex
| _version_ | 1804150660281663488 |
|---|---|
| adam_text | Table
of
Contents
Preface
...................................................................... xiii
Part I. 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
6
1.2.
Why Is Twitter All the Rage?
6
1.3.
Exploring Twitter
s
API
9
1.3.1.
Fundamental Twitter Terminology
9
1.3.2.
Creating a Twitter API Connection
12
1.3.3.
Exploring Trending Topics
16
1.3.4.
Searching for Tweets
20
1.4.
Analyzing the
140
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
32
1.4.4.
Examining Patterns in Retweets
34
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 Social Graph API
46
2.2.1.
Understanding the Social Graph API
48
2.2.2.
Understanding the Open Graph Protocol
54
vii
2.3.
Analyzing Social Graph Connections
59
2.3.1.
Analyzing Facebook Pages
63
2.3.2.
Examining Friendships
70
2.4.
Closing Remarks
85
2.5.
Recommended Exercises
85
2.6.
Online Resources
86
3.
Mining Linkedin: Faceting Job Titles, Clustering Colleagues, and More
..............89
3.1.
Overview
90
3.2.
Exploring the Linkedin API
90
3.2.1.
Making Linkedin API Requests
91
3.2.2.
Downloading Linkedin Connections as a CSV File
96
3.3.
Crash Course on Clustering Data
97
3.3.1.
Clustering Enhances User Experiences
100
3.3.2.
Normalizing Data to Enable Analysis
101
3.3.3.
Measuring Similarity
112
3.3.4.
Clustering Algorithms
115
3.4.
Closing Remarks
131
3.5.
Recommended Exercises
132
3.6.
Online Resources
133
4.
Mining Google+i Computing Document Similarity, Extracting Collocations, and More
135
4.1.
Overview
136
4.2.
Exploring the
Goog^
API
136
4.2.1.
Making
Goog^
API Requests
138
4.3.
A Whiz-Bang Introduction to TF-IDF
147
4.3.1.
Term Frequency
148
4.3.2.
Inverse Document Frequency
150
4.3.3.
TF-IDF
151
4.4.
Querying Human Language Data with TF-IDF
155
4.4.1.
Introducing the Natural Language Toolkit
155
4.4.2.
Applying TF-IDF to Human Language
158
4.4.3.
Finding Similar Documents
160
4.4.4.
Analyzing Bigrams in Human Language
167
4.4.5.
Reflections on Analyzing Human Language Data
177
4.5.
Closing Remarks
178
4.6.
Recommended Exercises
179
4.7.
Online Resources
180
5.
Mining Web Pages: Using Natural Language Processing to Understand Human
Language, Summarize Blog Posts, and More
.................................. 181
5.1.
Overview
182
viii
| Table of Contents
5.2.
Scraping, Parsing, and Crawling the Web
183
5.2.1.
Breadth-First Search in Web Crawling
186
5.3.
Discovering Semantics by Decoding Syntax
190
5.3.1.
Natural Language Processing Illustrated Step-by-Step
192
5.3.2.
Sentence Detection in Human Language Data
196
5.3.3.
Document Summarization
200
5.4.
Entity-Centric Analysis: A Paradigm Shift
209
5.4.1.
Gisting
Human Language Data
213
5.5.
Quality of Analytics for Processing Human Language Data
219
5.6.
Closing Remarks
222
5.7.
Recommended Exercises
222
5.8.
Online Resources
223
6.
Mining Mailboxes: Analyzing Who s Talking to Whom About What How Often, and More
......................................................................... 225
6.1.
Overview
226
6.2.
Obtaining and Processing a Mail Corpus
227
6.2.1.
A Primer on Unix Mailboxes
227
6.2.2.
Getting the Enron Data
232
6.2.3.
Converting a Mail Corpus to a Unix Mailbox
235
6.2.4.
Converting Unix Mailboxes to JSON
236
6.2.5.
Importing
a JSONifìed Mail
Corpus into MongoDB
240
6.2.6.
Programmatically Accessing MongoDB with Python
244
6.3.
Analyzing the Enron Corpus
247
6.3.1.
Querying by Date/Time Range
248
6.3.2.
Analyzing Patterns in Sender/Recipient Communications
250
6.3.3.
Writing Advanced Queries
255
6.3.4.
Searching Emails by Keywords
260
6.4.
Discovering and Visualizing Time-Series Trends
264
6.5.
Analyzing Your Own Mail Data
268
6.5.1.
Accessing Your Gmail with OAuth
269
6.5.2.
Fetching and Parsing Email Messages with IMAP
271
6.5.3.
Visualizing Patterns in GMail with the Graph Your Inbox Chrome
Extension
273
6.6.
Closing Remarks
274
6.7.
Recommended Exercises
275
6.8.
Online Resources
276
7.
Mining GitHub: Inspecting Software Collaboration Habits, Building interest Graphs, and
More
.................................................................... 279
7.1.
Overview
280
7.2.
Exploring GitHubs API
281
Table of Contents I
ix
7.2.1.
Creating a GitHub API Connection
282
7.2.2.
Making GitHub API Requests
286
7.3.
Modeling Data with Property Graphs
288
7.4.
Analyzing GitHub Interest Graphs
292
7.4.
ł
.
Seeding an Interest Graph
292
7.4.2.
Computing Graph Centraiity Measures
296
7.4.3.
Extending the Interest Graph with Follows Edges for Users
299
7.4.4.
Using Nodes as Pivots for More Efficient Queries
311
7.4.5.
Visualizing Interest Graphs
316
7.5.
Closing Remarks
318
7.6.
Recommended Exercises
318
7.7.
Online Resources
320
8.
Mining the Semantically Marked-Up Web: Extracting
Microformats,
Inferencing over
RDF,andMore
............................................................ 321
8.1.
Overview
322
8.2.
Microformats:
Easy-to-Implement Metadata
322
8.2.1.
Geocoordinates: A Common Thread for Just About Anything
325
8.2.2.
Using Recipe Data to Improve Online Matchmaking
331
8.2.3.
Accessing
Linkedlrís
200
Million Online Resumes
336
8.3.
From Semantic Markup to Semantic Web: A Brief Interlude
338
8.4.
The Semantic Web: An Evolutionary Revolution
339
8.4.1.
Man Cannot Live on Facts Alone
340
8.4.2.
Inferencing About an Open World
342
8.5.
Closing Remarks
345
8.6.
Recommended Exercises
346
8.7.
Online Resources
347
Partii.
Twitter Cookbook
9.
Twitter Cookbook
......................................................... 351
9.1.
Accessing Twitter
s
API for Development Purposes
352
9.2.
Doing the OAuth Dance to Access Twitters API for Production Purposes
353
9.3.
Discovering the Trending Topics
358
9.4.
Searching for Tweets
359
9.5.
Constructing Convenient Function Calls
361
9.6.
Saving and Restoring JSON Data with Text Files
362
9.7.
Saving and Accessing JSON Data with MongoDB
363
9.8.
Sampling the Twitter Firehose with the Streaming API
365
9.9.
Collecting Time-Series Data
367
9.10.
Extracting Tweet Entities
368
χ
| Table of Contents
9.11.
Finding the Most Popular Tweets in a Collection of Tweets
370
9.12.
Finding the Most Popular Tweet Entities in a Collection of Tweets
372
9.13.
Tabulating Frequency Analysis
373
9.14.
Finding Users Who Have Retweeted a Status
374
9.15.
Extracting
a Retweeťs
Attribution
376
9.16.
Making Robust Twitter Requests
378
9.17.
Resolving User Profile Information
380
9.18.
Extracting Tweet Entities from Arbitrary Text
381
9.19.
Getting All Friends or Followers for a User
382
9.20.
Analyzing a User s Friends and Followers
384
9.21.
Harvesting a User s Tweets
386
9.22.
Crawling a Friendship Graph
388
9.23.
Analyzing Tweet Content
390
9.24.
Summarizing Link Targets
391
9.25.
Analyzing a Users Favorite Tweets
395
9.26.
Closing Remarks
396
9.27.
Recommended Exercises
397
9.28.
Online Resources
398
Partili.
Appendixes
A. Information About This Book s Virtual Machine Experience
...................... 401
B. OAuth Primer
............................................................. 403
С
Python and IPython Notebook Tips
&
Tricks
.................................... 409
Index
....................................................................... 411
Table of Contents I
xi
|
| any_adam_object | 1 |
| author | Russell, Matthew A. |
| author_GND | (DE-588)1147076324 |
| author_facet | Russell, Matthew A. |
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| bvnumber | BV041222672 |
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| discipline | Informatik |
| edition | 2nd edition |
| format | Book |
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| indexdate | 2024-07-10T00:42:27Z |
| institution | BVB |
| isbn | 9781449367619 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026197184 |
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| physical | XXIV, 421 Seiten Illustrationen, Diagramme |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | O'Reilly |
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| spelling | Russell, Matthew A. Verfasser (DE-588)1147076324 aut Mining the social web Matthew A. Russell Data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more 2nd edition Beijing ; Cambrige ; Farnham ; Köln ; Sebastopol ; Tokyo O'Reilly 2014 XXIV, 421 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Soziale Software (DE-588)7550143-0 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf World Wide Web 2.0 (DE-588)7548364-6 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 1\p DE-604 Erscheint auch als Online-Ausgabe Mining the social web 978-1-491-98504-5 (DE-604)BV044727295 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026197184&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Russell, Matthew A. Mining the social web Soziale Software (DE-588)7550143-0 gnd Data Mining (DE-588)4428654-5 gnd World Wide Web 2.0 (DE-588)7548364-6 gnd |
| subject_GND | (DE-588)7550143-0 (DE-588)4428654-5 (DE-588)7548364-6 |
| title | Mining the social web |
| title_alt | Data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more |
| title_auth | Mining the social web |
| title_exact_search | Mining the social web |
| title_full | Mining the social web Matthew A. Russell |
| title_fullStr | Mining the social web Matthew A. Russell |
| title_full_unstemmed | Mining the social web Matthew A. Russell |
| title_short | Mining the social web |
| title_sort | mining the social web |
| topic | Soziale Software (DE-588)7550143-0 gnd Data Mining (DE-588)4428654-5 gnd World Wide Web 2.0 (DE-588)7548364-6 gnd |
| topic_facet | Soziale Software Data Mining World Wide Web 2.0 |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026197184&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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