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Part I: Data Identification
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Part I: Data Identification
by Avinash Kohirkar, Matthew Ganis
Social Media Analytics: Techniques and Insights for Extracting Business Value Out of Social Media
About This E-Book
Title Page
Copyright Page
Dedication Page
Contents
Foreword
Preface: Mining for Gold (or Digging in the Mud)
Just What Do We Mean When We Say Social Media?
Why Look at This Data?
How Does This Translate into Business Value?
The Book’s Approach
Data Identification
Data Analysis
Information Interpretation
Why You Should Read This Book
What This Book Does and Does Not Focus On
Endnotes
Acknowledgments
Matt Ganis
Avinash Kohirkar
Joint Acknowledgments
About the Authors
Part I: Data Identification
1. Looking for Data in all the Right Places
What Data Do We Mean?
What Subset of Content Are We Interested In?
Whose Comments Are We Interested In?
What Window of Time Are We Interested In?
Attributes of Data That Need to Be Considered
Structure
Language
Region
Type of Content
Venue
Time
Ownership of Data
Summary
Endnotes
2. Separating the Wheat from the Chaff
It All Starts with Data
Casting a Net
Regular Expressions
A Few Words of Caution
It’s Not What You Say but WHERE You Say It
Summary
Endnotes
3. Whose Comments Are We Interested In?
Looking for the Right Subset of People
Employment
Sentiment
Location or Geography
Language
Age
Gender
Profession/Expertise
Eminence or Popularity
Role
Specific People or Groups
Do We Really Want ALL the Comments?
Are They Happy or Unhappy?
Location and Language
Age and Gender
Eminence, Prestige, or Popularity
Summary
Endnotes
4. Timing Is Everything
Predictive Versus Descriptive
Predictive Analytics
Descriptive Analytics
Sentiment
Time as Your Friend
Summary
Endnotes
5. Social Data: Where and Why
Structured Data Versus Unstructured Data
Big Data
Social Media as Big Data
Where to Look for Big Data
Paradox of Choice: Sifting Through Big Data
Identifying Data in Social Media Outlets
Professional Networking Sites
Social Sites
Information Sharing Sites
Microblogging Sites
Blogs/Wikis
Summary
Endnotes
Part II: Data Analysis
6. The Right Tool for the Right Job
The Four Dimensions of Analysis Taxonomy
Depth of Analysis
Machine Capacity
Domain of Analysis
External Social Media
Internal Social Media
Velocity of Data
Data in Motion
Data at Rest
Summary
Endnotes
7. Reading Tea Leaves: Discovering Themes, Topics, or Trends
Validating the Hypothesis
Youth Unemployment
Cannes Lions 2013
56th Grammy Awards
Discovering Themes and Topics
Business Value of Projects
Analysis of the Information in the Business Value Field
Our Findings
Using Iterative Methods
Summary
Endnotes
8. Fishing in a Fast-Flowing River
Is There Value in Real Time?
Real Time Versus Near Real Time
Forewarned Is Forearmed
Stream Computing
IBM InfoSphere Streams
SPL Applications
Directed Graphs
Streams Example: SSM
Step 1
Step 2
Step 3
Step 4
Steps 5 and 6
Steps 7 and 8
Value Derived from a Conference Using Real-Time Analytics
Summary
Endnotes
9. If You Don’t Know What You Want, You Just May Find It: Ad Hoc Analysis
Ad Hoc Analysis
An Example of Ad Hoc Analysis
Data Integrity
Summary
Endnotes
10. Rivers Run Deep: Deep Analysis
Responding to Leads Identified in Social Media
Identifying Leads
Qualifying/Classifying Leads
Suggested Action
Support for Deep Analysis in Analytics Software
Topic Evolution
Affinity Analysis in Reporting
Summary
Endnotes
11. The Enterprise Social Network
Social Is Much More Than Just Collaboration
Transparency of Communication
Frictionless Redistribution of Knowledge
Deconstructing Knowledge Creation
Serendipitous Discovery and Innovation
Enterprise Social Network Is the Memory of the Organization
Understanding the Enterprise Graph
Personal Social Dashboard: Details of Implementation
Key Performance Indicators (KPIs)
Assessing Business Benefits from Social Graph Data
What’s Next for the Enterprise Graph?
Summary
Endnotes
Part III: Information Interpretation
12. Murphy Was Right! The Art of What Could Go Wrong
Recap: The Social Analytics Process
Finding the Right Data
Communicating Clearly
Choosing Filter Words Carefully
Understanding That Sometimes Less Is More
Customizing and Modifying Tools
Using the Right Tool for the Right Job
Analyzing Consumer Reaction During Hurricane Sandy
Summary
Endnotes
13. Visualization as an Aid to Analytics
Common Visualizations
Pie Charts
Bar Charts
Line Charts
Scatter Plots
Common Pitfalls
Information Overload
The Unintended Consequences of Using 3D
Using Too Much Color
Visually Representing Unstructured Data
Summary
Endnotes
Appendices
A. Case Study
Introduction to the Case Study: IBMAmplify
Data Identification
Taking a First Pass at the Analysis
Data Analysis
A Second Attempt at Analyzing the Data
Information Interpretation
Conclusions
Index
Code Snippets
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About the Authors
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1. Looking for Data in all the Right Places
Part I: Data Identification
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