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Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
Presentation on Affect Analysis and Ranking
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Presentation on Affect Analysis and Ranking

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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

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

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  • Physiological:hartslagBehavioral: dmv questionnaires Vocal: stemhoogteLinguistic: analyseren van text
  • Master project based on rankings, made explicit for a certain application (affect analysis)Thesis written more from application view. PhD research more from data
  • Information utility: suitability of dataset in answering a query (based on complexity measures)
  • 1795 onwardsInformation on municipalities, occupations, housing, etc
  • AERS: adverse event reporting systemCT: clinical trialCDS: clinical decision supportWhere are the enrichted publications of elsevier?
  • Mention implicit vs. explicit
  • Example: online storeSimplified example
  • Avoid full joins
  • In other words: what if we need to apply rules, to get all the values to rank on
  • Transcript

    • 1. AFFECT ANALYSIS OF DUTCH SOCIAL MEDIA ANDRANKING 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 BackgroundAffect Analysis Affect: experience of feeling or emotion[1] Multiple measurements  Physiological  Behavioral  Vocal  Linguistic [1] W. Huitt, The Affective System
    • 4. Project BackgroundAffect 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 BackgroundAffect 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 BackgroundDutch Social Media Used Social Media Types:  Blogs (www.blogspot.com)  Online news item reactions (www.fok.nl)  Micro-blogs (www.twitter.com)
    • 7. Project BackgroundCrowd 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 ANDRANKING OF QUERY RESULTS OVER LINKED DATA Laurens Rietveld
    • 14. Data2Semantics
    • 15. Data2Semantics
    • 16. Data2SemanticsWicherts JM, Bakker M, Molenaar D, 2011Willingness 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 reportHospital 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. OrderingSELECT ?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 1traditional 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

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