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Discovering the Dynamics of Terms’ Semantic Relatedness through Twitter<br />Nikola Milikic, University of Belgrade, Serbi...
Outline<br />Introduction<br />Normalized Micropost Distance<br />Scenarios of Use<br />Example Diagrams – Tweet Dynamics<...
Introduction<br />Micropost = Description of a Moment<br />
Introduction<br />Semantic Relatedness (SR) of terms is also a subject to temporal changes<br />Mutual relationship change...
Normalized Micropost Distance <br />Normalized Micropost Distance (NMD) - semantic similarity measure derived from the num...
Normalized Micropost Distance <br />NMD formula<br />x, y – terms<br />f(x)t , f(y)t – number of microposts for x and y<br...
Normalized Micropost Distance <br />Detecting the significance of change - standard deviation of NMDs<br />
Scenarios of Use<br />Adapting Online Advertising Campaigns to the Changes in Term Relatedness<br />Example: ‘sxsw’ and ‘i...
Example Diagrams<br />Tweet Dynamics – demo application<br />NMD diagram for terms 'ipad' and 'sxsw' for the 5 days period...
Example Diagrams<br />Tweet Dynamics – demo application<br />NMD diagram for terms ‘japan' and ‘nuclear' for the 5 days pe...
Future Work<br />work in progress<br />detection of good candidate term pairs<br />computational efficiency and the limits...
Questions?<br />Nikola Milikic<br />		@milikicn<br />		http://nikola.milikic.info<br />
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Discovering the Dynamics of Terms’ Semantic Relatedness through Twitter

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A presentation given at the Making Sense of Microposts Workshop at the Extended Semantic Web Conference 2011, Crete, Greece

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Transcript of "Discovering the Dynamics of Terms’ Semantic Relatedness through Twitter"

  1. 1. Discovering the Dynamics of Terms’ Semantic Relatedness through Twitter<br />Nikola Milikic, University of Belgrade, Serbia<br />JelenaJovanovic, University of Belgrade, Serbia <br />Milan Stankovic, STIH, Université Paris-Sorbonne, France<br />
  2. 2. Outline<br />Introduction<br />Normalized Micropost Distance<br />Scenarios of Use<br />Example Diagrams – Tweet Dynamics<br />Future Work<br />
  3. 3. Introduction<br />Micropost = Description of a Moment<br />
  4. 4. Introduction<br />Semantic Relatedness (SR) of terms is also a subject to temporal changes<br />Mutual relationship change of terms is not directly evident from simple query results<br />
  5. 5. Normalized Micropost Distance <br />Normalized Micropost Distance (NMD) - semantic similarity measure derived from the number of microposts containing a given set of keywords<br />Inspired by the Normalized Google Distance (NGD)<br />Google search results vs.Microposts<br />
  6. 6. Normalized Micropost Distance <br />NMD formula<br />x, y – terms<br />f(x)t , f(y)t – number of microposts for x and y<br />f(x, y)t– number of microposts containing both x and y<br />t – time interval<br />
  7. 7. Normalized Micropost Distance <br />Detecting the significance of change - standard deviation of NMDs<br />
  8. 8. Scenarios of Use<br />Adapting Online Advertising Campaigns to the Changes in Term Relatedness<br />Example: ‘sxsw’ and ‘ipad’<br />Facilitating Discovery of Relevant Resources in Organizations<br />harmonizing the official and the actual vocabularies within an organization<br />
  9. 9. Example Diagrams<br />Tweet Dynamics – demo application<br />NMD diagram for terms 'ipad' and 'sxsw' for the 5 days period<br />
  10. 10. Example Diagrams<br />Tweet Dynamics – demo application<br />NMD diagram for terms ‘japan' and ‘nuclear' for the 5 days period<br />
  11. 11. Future Work<br />work in progress<br />detection of good candidate term pairs<br />computational efficiency and the limits of Twitter API <br />comprehensive evaluation<br />test on the mass amount of data<br />compare to other approaches<br />Google Correlate<br />
  12. 12. Questions?<br />Nikola Milikic<br /> @milikicn<br /> http://nikola.milikic.info<br />

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