1. Social Media Sentiment through SAP HANA
and SAP BusinessObjects Analytics
Brandon M Lage
Dickinson + Associates

2. Focus: Delivery of quality SAP ERP, BI/Analytics, Mobility
consulting services to customers across North America,
Europe, and Asia.
Our People: A team of 140+ full-time SAP professionals reflects the
ideal mix of years of relevant business knowledge, very
strong SAP credentials, and solid communication skills.
Our team has an average of 16 years SAP and 19 years
business experience.
Offices: Chicago, IL (Headquarters)
Satellites: New York, NY | Scottsdale, AZ | Cincinnati, OH

4.  SAP Gold Channel Partner
 SAP Services Partner
 SAP All-in-One Certified Solutions
 SAP-Qualified Partner for RDS
 Business Objects
 Sybase Partner
 SuccessFactors Partner

5. Agenda
 Data Epidemic / Structured vs. Unstructured Data
 Social Listening
 SAP HANA Text Analysis
 Example: Sentiment Analysis – “Voice of the Customer”
 Apple Watch
 #Cubs
 #CrosstownClassic
 Best Practices
 Key Learnings

6. Data Epidemic
90% global data
created in last 3
years 2011 - 2014
10% of all
data ever
created

7. Structured vs. Unstructured Data
Structured Data
 Data that resides in a fixed
field within a record or file
 Ex: data in a database table
 Easy to enter, store, and
analyze
Unstructured Data
 Does not reside in a traditional
database
 Ex: e-mail, videos, audio files,
web pages, presentations
 Difficult and costly to analyze

8. Structured vs. Unstructured Data
Data in Organizations
 80%+ is unstructured
 Data about organization is
now in the hands of the
consumer via social media
 Harnessing this data is the
key to uncovering insights
about your organization

9. Social Listening
 Process of identifying and assessing what is being said about a company,
individual, product or brand via social media
 Becoming increasingly popular across organizations geared at tackling
the growing data explosion
Consumer ACME Foods
Customer Service Rep
1. Offer refund
2. Find out
details
3. Send swag
4. Do nothing
This candy
tastes horrible!
#ACMEFOODS
Social Media Monitoring

10. SAP HANA Text AnalysisUnstructured
Data
1. Extract meaning
2. Transform into
structured data for
analysis
Structured Data
Now able to query,
analyze, visualize,
report against, etc.
 Process of analyzing unstructured text, extracting relevant information and then
transforming that information structured that can be leveraged in different ways.
 With the help of text analysis we can model and structure the information content
for the purpose of business analysis, research and investigation.

11. SAP HANA Text Analysis
Example: Voice of Customer Text Analysis
ID Text Lang
1 Bob likes working at SAP EN
2 The innovation from SAP is amazing EN
3 I can’t wait to implement SAP HANA! EN
SAP HANA Linguistics Processor
Bob likes working at SAP
Weak
Positive
Sentiment
Person Topic Organization /
Commercial

12. Simple Text Analysis in SAP HANA
3. Turn On – Text Analysis
2. Turn On – Twitter Streaming API and store Tweets
1. Create HANA Table
4. Connect HANA to Lumira + Visualize

13. Simple Text Analysis in SAP HANA
1. Create HANA Table

14. Simple Text Analysis in SAP HANA
2. Turn On – Twitter Streaming API and store Tweets
Twitter API App Node.JS HANA Destination Table

15. Simple Text Analysis in SAP HANA
3. Turn On – Text Analysis

16. Simple Text Analysis in SAP HANA
4. Connect HANA to Lumira + Visualize

17. Simple Text Analysis in SAP HANA
#AppleWatch

18. Simple Text Analysis in SAP HANA
#AppleWatch

19. Simple Text Analysis in SAP HANA
#AppleWatch

20. Simple Text Analysis in SAP HANA
#AppleWatch

21. Simple Text Analysis in SAP HANA
#AppleWatch

22. Simple Text Analysis in SAP HANA
#AppleWatch

23. Simple Text Analysis in SAP HANA
#Cubs - TOPICS

24. Simple Text Analysis in SAP HANA
#Cubs - Persons

25. Simple Text Analysis in SAP HANA
#Cubs – Sports Organizations

26. Simple Text Analysis in SAP HANA
#Cubs – Major Problems

27. Simple Text Analysis in SAP HANA
#Cubs – Facility/Building Grounds

28. Simple Text Analysis in SAP HANA
#CrosstownClassic

29. Simple Text Analysis in SAP HANA
#CrosstownClassic

30. Simple Text Analysis in SAP HANA
#CrosstownClassic

31. Best Practices
 Utilize the out-of-box system dictionaries to simplify user
experience, enhance after organization understands
usage of entity types.
 Case-sensitivity can skew results, work towards
converting strings to upper-case if necessary.

32. Key Learnings
 Introducing SAP HANA Text Analysis into your
organization requires a change in culture to realize its
benefits.
 The SAP HANA Text Analysis engine is continuously
evolving, slang and tone should be evaluated when
making decisions from the information.
 SAP HANA Text Analysis is extremely simple to
implement.
 Social Media isn’t the only unstructured text that can be
analyzed, this can extend to any type of text (email, blog,
electronic documents, etc.)

33. THANK YOU
 Let’s Go Cubs!