1Chapter 1. Introduction
416.2 Retrieving trends
21.1 Definition
426.3 Computing the intersection of two sets
31.2 Overview of the Book
43of trends
41.3 Various Social Media Platforms
446.4 Computing the Lexical Diversity of Tweets
51.4 Upcoming Challenges
456.5 Collecting Time-Series Data
61.5 Summary
466.6 Closing Remarks
71.6 Exercise
476.7 Exercise
8Chapter 2. Prerequisites
48Chapter 7. Mining Linkedin
92.1 Graph Fundamentals
497.1 Overview
102.3 Network Models
507.2 Exploring the LinkedIn API
112.4 Attachment Models
517.3 Clustering Data of LinkedIn
122.5 Random Graphs
527.4 Closing Remarks
132.6 Network Evaluation
537.5 Exercise
142.7 Summary
54Chapter 8. Mining Webpages
152.8 Exercise
558.1 Overview
16Chapter 3. Data Mining
568.2 Web Structure Mining
173.1 Data Mining Algorithms
578.3 Web Crawlers
183.2 Data Mining Techniques
588.4 Web Content Mining
193.3 Data Mining Implementation
598.5 Web Content Mining Methods
203.4 Data Mining Tools
608.6 Web Content Mining Techniques
213.5 Summary
618.7 Quality of Analytics for Processing Human
223.6 Exercise
62Language Data
23Chapter 4. Social Media Interactions
638.8 Closing Remarks
244.1 Interaction Strategies
648.9 Exercise
254.2 Engage Your Followers Today
65Chapter 9. Mining Mails
264.3 Social Media Engagement
669.1 Overview
274.4 Behavior Analytics
679.2 Obtaining and Processing a Mail Corpus
284.5 Summary
689.3 Converting a toy mailbox to JSON
294.6 Exercise
699.4 Converting the Enron corpus to a
30Chapter 5. Mining Facebook
70standardized inbox format
315.1 Overview
719.5 Converting a mbox to a JSON structure
325.2 Social graph API
72suitable for import into MongoDB
335.3 What are Different APIs Offered by
739.6 The Mongo DB
34Facebook?
749.7Analyzing the Enron Corpus
355.4 Why is the Facebook API important?
759.8 Analyzing Your Own Mail Data
365.5 Social Graph Connection
769.9 Remarks
375.6 Remarks
779.10 Exercise
385.7 Exercise
78Chapter 10. Case Study
39Chapter 6. Mining Twitter
79Glossary
406.1 Overview
