Data mining and analysis of social networking sites

Preface 1
The first part of social network guidance
Prologue 13
The 1th Chapter explores Twitter: Exploring hot topics, discovering what people are talking about, etc. 15
1.1 Overview 15
Reasons for 1.2 Twitter rage 16
1.3 Explore Twitter API 18
1.4 Analysis of 140 word tweets 33
1.5 Summary of this chapter 47
1.6 Recommended Exercises 48
1.7 Resources Online 48
2nd Chapter Mining Facebook: Analyzing fan pages, viewing friends, etc. 50
2.1 Overview 51
2.2 Explore Facebook's social Atlas API 51
2.3 Analyzing the Social Atlas contact 62
2.4 Summary of this chapter 85
2.5 Recommended Exercises 86
2.6 Resources Online 86
The 3rd Chapter mining LinkedIn: Group jobs, cluster peers, etc. 88
3.1 Overview 89
3.2 Explore LinkedIn API 89
3.3 Data Clustering Express 94
3.4 Summary of this chapter 124
3.5 Recommended Exercises 125
3.6 Resources Online 126
The 4th Chapter Mining Google +: Calculate the similarity of documents, extraction and collocation, etc. 127
4.1 Overview 128
4.2 Explore Google + API 128
4.3 TF-IDF Introduction 138
4.4 Querying human language data with TF-IDF 145
4.5 Summary of this chapter 164
4.6 Recommended Exercises 165
4.7 Resources Online 165
The 5th Chapter digs the webpage: uses the natural language processing to understand the human language, summarizes the blog content and so on 167
5.1 Overview 168
5.2 Fetching, parsing, and crawling pages 168
5.3 Exploring semantics by decoding syntax 174
5.4 Entity-centric analysis: Paradigm shift 192
5.5 Quality of human language data processing Analysis 200
5.6 Summary of this chapter 203
5.7 Recommended Exercises 203
5.8 Resources Online 204
The 6th Chapter Mining mailbox: Analyze who and who say what and say frequency etc 206
6.1 Overview 207
6.2 Acquiring and processing a mail corpus 207
6.3 Analysis of the Enron Corpus 225
6.4 Exploring and visualizing timing trends 241
6.5 Analyzing your own mail data 244
6.6 Summary of this chapter 250
6.7 Recommended Exercises 251
6.8 Resources Online 251
The 7th Chapter digs GitHub: Check the software collaboration habits, build the interest map, etc. 253
7.1 Overview 254
7.2 Explore GitHub's API 254
7.3 Modeling data using a property map 260
7.4 Analyzing the GitHub interest map 264
7.5 Summary of this chapter 286
7.6 Recommended Exercises 287
7.7 Resources Online 287
The 8th chapter is mining the tag Semantic Web: Extracting micro-format, inferring resource description frame, etc. 289
8.1 Overview 290
8.2 Micro-format: easy-to-implement meta-data 290
8.3 transition from semantic markup to Semantic Web: an episode 304
8.4 Semantic Web: The change in development 304
8.5 Summary of this chapter 310
8.6 Recommended Exercises 311
8.7 Resources Online 311
Part Two practical guide to Twitter
9th. A practical Guide to Twitter 317
9.1 Accessing the Twitter API (development purpose) 318
9.2 Using OAuth to access the Twitter API (product purpose) 319
9.3 Explore Popular Topics 323
9.4 Find tweets 324
9.5 Constructing a convenient function call 325
9.6 Storing JSON data using a text file 326
9.7 Storing and accessing JSON data using MongoDB 327
9.8 Sampling The Twitter data pipeline using the information flow API 329
9.9 Acquisition Timing Data 330
9.10 extracting a tweet entity 332
9.11 find the most popular tweets within a specific tweet range 333
9.12 find the most popular tweet entities within a specific tweet range 335
9.13 pairs of frequency analysis tabulation 336
9.14 finding the user who has the status of retweets 337
9.15 Extracting properties of retweets 339
9.16 creating a robust Twitter request 340
9.17 Obtaining user profile Information 343
9.18 extracting a tweet entity from any text 344
9.19 get all of your friends and followers 345
9.20 analyzing users ' friends and followers 347
9.21 get tweets from users 348
9.22 crawling a friend diagram 350
9.23 Analyzing tweet Content 351
9.24 Extract Link Target Summary 353
9.25 Analysis of user favorite tweets 356
9.26 Summary of this chapter 357
9.27 Recommended Exercises 358
9.28 Resources Online 359
Part III Appendix
Appendix A information about the virtual machine experience for this book 363
Appendix B OAuth Primer 364
Appendix C use tips for Python and Ipython notebook 368

Data mining and analysis of social networking sites