Content analytics uses semantic technologies to unlock business value from unstructured content like text. It answers important questions by automatically analyzing text to extract features, trends, quotes, and regulatory clauses. Content analytics offloads tedious manual tasks and provides high-level insights through APIs and a modular architecture that includes parsing, annotation, and classification capabilities.

1. WHAT IS
CONTENT
ANALYTICS ?

2. CONTENT ANALYTICS
UNLOCKS BUSINESS VALUE
FROM UNSTRUCTURED CONTENT
VIA SEMANTIC TECHNOLOGIES
DELIVERING ANSWERS
TO IMPORTANT QUESTIONS

4. ANALYTICS
ADDSVALUE
BUT WHY
?
AND WHY NOW

6. OFFLOADING
THE EASY BUT TEDIOUS

7. FINDING WORDS

8. WHAT
FEATURES
OF THIS SMARTPHONE
ARE USERS INCREASINGLY
LIKING OR HATING
?

9. CONTENT
ANALYTICS

10. WHAT CAUSED
THIS RECENT DROP
SALES FOR
?
IN
PRODUCT X

11. MEDIA PROFILE
GIVE ME A
OF MR. X INCLUDING
TRENDS, QUOTES, ROLES, CONTACTS, …

12. ADVANCED
CONTENT ANALYTICS

13. FIND POSITIVE REVIEWS
OF PRODUCT X
THAT COMPLAIN
ABOUT SPECIFIC FEATURES

14. TRACK THE INFLUENCE
OF THIS PRESS RELEASE
IN BLOGS OVER TIME

15. WHICH REGULATORY CLAUSES
AND SENTENCES FROM PAST CASES
DISCUSS THE FAILURE OF COMPONENT X
?

17. OFFLOADING
THE EASY BUT TEDIOUS

18. Enrich. Classify. Understand

19. High Level Architecture
REST APIs / XML RCP (HTTP)
IN OUT
UIMA
Annotations 3RD party annotator
(Facts & relationships)
Sentiments/Opinion
Key concepts
Categories
Similarity
Parsers
Entities
s
The 3 pillars of content analytics:
1. Languages
2. Industry specific annotations
3. Industry specific documents