Presentation for the weekly Artificial Intelligence meeting of the VU. It covers work on affect analysis (master project), and planned work on ranking of Linked Data

1. AFFECT ANALYSIS OF DUTCH SOCIAL
MEDIA
AND
RANKING OF QUERY RESULTS OVER
LINKED DATA
Laurens Rietveld

2. Master Project Background
 Affect analysis of Dutch social media
 Finished July 2010
 VU (Stefan)
 GfK Daphne
 Marketing Research
 Online dashboard
Data Data
Analysis
collection Processing
 Not involved yet in webmining
 Business case: National Railway Company (NS)

3. Project Background
Affect Analysis
 Affect: experience of feeling or emotion[1]
 Multiple measurements
 Physiological
 Behavioral
 Vocal
 Linguistic
[1] W. Huitt, The Affective System

4. Project Background
Affect Analysis
 What is online affect analysis
 Detect emotions on web pages
 Types of emotions[2]:
 Love
 Joy
 Surprise
 Anger
 Sadness
 Fear
[2] W. Parrott, Emotions in Social Psychology

5. Project Background
Affect Analysis
 Main problems
 Unstructured data
 Internet (html)
 Text
 Domain dependencies
 “Goread the book” positive in book reviews, negative
in movie reviews
 Ambivalence
 Text
 Emotion

6. Project Background
Dutch Social Media
 Used Social Media Types:
 Blogs (www.blogspot.com)
 Online news item reactions (www.fok.nl)
 Micro-blogs (www.twitter.com)

7. Project Background
Crowd Sourcing
 Problems:
 Affect analysis needs training data
 Annotating data is time-consuming
 Annotate every domain
 Normally done by researcher
 Solution: Crowd Sourcing
 Mechanical Turks
 Outsourcing simple tasks to large community
+ -
Many tasks English only
Quick Risk of lower quality
Cheap Unethical (debatably)

8. Affect Analysis Approach

9. Research Questions
 Is it possible to apply crowd-sourcing to affect
analysis of Dutch social media
 Are there differences between social media types
in affect analysis

10. Results
 Inter annotator agreement: low
 Neutral outvotes emotion
 Possible causes:
 Missing sentence context
 Too few annotators
 Noise introduced by translation

11. Results
Period Event
July 2007 Problems in the payment system of ticket automats
January 2009 Required chip card payment method for students
December 2009 Train and railway malfunctions due to snow
February 2010 Filthy train stations due to cleaning crew strikes
All social media
9%
8%
7%
% of all documents
6%
5%
4%
3%
2%
1%
0%
-1%
Period
Joy Surprise Anger Sadness

12. Future work
 Other list of emotions
 Improve annotation process
 More voting
 Use other strategies for annotation tasks
 Not sentence annotation but paragraph/document
 Different social media types, different feature-
extraction/classifier/annotation strategies

13. AFFECT ANALYSIS OF DUTCH SOCIAL
MEDIA
AND
RANKING OF QUERY RESULTS OVER
LINKED DATA
Laurens Rietveld

14. Data2Semantics

15. Data2Semantics

16. Data2Semantics
Wicherts JM, Bakker M, Molenaar D, 2011
Willingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results.
PLoS ONE 6(11)

17. Data2Semantics
 Provide semantic infrastructure for e-Science
 How to
share, publish, access, analyze, interpret and
reuse data?
 Querying
 Ranking
 Information utility
 Enriched publications
 Provenance
 Annotation/interpretation

18. Census

19. Clinical Decision Support
Linked Data
Clinical evidence
e.g. CT report
Hospital
AERS
CDS tools
CDS tools
Patient Profile
EMR LIS Elsevier-published
Clinical Guideline

20. My Research
Ranking
http://dbpedia.org/fct/ http://google.com

21. My Research
 Ranking
1. Relevance
 No proper „PageRank‟ equivalent for semantic web
 Heterogeneous and imprecise data
2. Ordering
 Performance

22. Relevance
 What query results are most relevant?
 Semantic web comes with implicit orderings.
Possible indicators:
 Which ontologies are used more often?
 What can we say about these ontologies?
 Which query results are semantically similar?
 Which query results can I trust?

23. Ordering
SELECT ?price ?offer ?product ?vendor ((?rating + ?popularity) AS
?score)
{
?product :hasRating ?rating .
?product :producer ?producer .
?producer :hasPopularity ?popularity .
?offer :product ?product .
?offer :price ?price .
}
ORDER BY DESC(?score)
LIMIT 1
Berlin SPARQL Benchmark

24. Ordering
 Related work: Sara Magliacane
SPARQL-Rank
1
traditional
1
Slice Slice
1 1
1
13205
Order Join
13205 95 1
Ran BG
Join kJoi
P
n ?product
438634 13205 30 29 ?producer
Join BG Ran Ran ?offer
P k k ?price
646 679 ?product 646 679
?producer BG BG
BG BG ?offer
P P ?price
P P
?product ?producer
?product ?producer
?rating ?popularity
?rating ?popularity

25. Current Question
 What if reasoning is required to materialize
information?
 Top-k Closure (Stefan Schlobach)
 Avoid full materialization while still being complete
 Vb materialisatie

26. Thank You