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A Multimedia Interface For Facilitating Comparisons Of Opinions (Thesis Presentation)
The document discusses a multimedia interface designed to facilitate the analysis and comparison of opinion data across various entities. It highlights the effectiveness of the interface through user studies, indicating that its visualizations are usable and its summarization resembles human-like choices. Future work includes enhancements such as machine learning for selection and deeper evaluations of the visualization aspects.
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A Multimedia Interface For Facilitating Comparisons Of Opinions (Thesis Presentation)
- 1. A Multimedia Interfacefor Facilitating Comparisons of Opinions Lucas Rizoli Supervisor Giuseppe Carenini University of British Columbia February 11, 2009
- 2. Opinion data is abundant and useful, but analysis is expensive and difficult 2
- 3. Our interface supports analysis of opinion data, particularly comparison across entities 3
- 4. It supports analysisby visualizing the data and summarizing notable comparisons 4
- 5. Our user studyshows the visualization is usable, the summarizer’s choices are human-like 5
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- 7. Text mining (Carenini, Ng,& E. Zwart, 2006) 7
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- 13. +2 +1 +2 –3 13
- 14. Spore Gameplay Technology DRM Creature creation Graphics Cell phase Sound Civilization phase System requirements 14
- 15. vs. 15
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- 17. Applications ● Consumers ● Intelligence – Competitive analysis – Forecasts ● Research – Survey, questionnaire 17
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- 20. Controversiality (Carenini& Cheung, 2008) 20
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- 24. (Dis)similarity of comparisons ● Adapt stats to pairs of distributions ● Aspects of a comparison – counts – means – contros – dists 24
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- 35. Interpreting aspects ● Aspects in range 0–1 ● Interpretation – means 0.6 != contros 0.6 != dists 0.6 35
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- 38. Selecting comparisons 1. Filterout low-count comparisons 2. Rank by # of extreme aspects (VD, VS) 3. Realize top comparisons • Sentence templates 38
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- 40. Study goals ● Evaluate content selection strategy – Matches human selections? ● Better than baseline? – Humans like selections? ● Usability of visualization 40
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- 43. Baseline selection strategies ● Naïve – Randomly select which and how many ● Semi-informed – Likely select same how many as subjects – Randomly select which 43
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- 45. Humans like selections? 0.500 0.449 0.454 45
- 46. Humans like selections? 0.500 0.449 0.454 ● Roughly 20% difference ● 60% of system selections marked as “good” 46
- 47. Usability 47
- 48. Opinion data is abundant and useful, but analysis is expensive and difficult Our interface supports analysis of opinion data, particularly comparison across entities, by visualizing the data and summarizing notable comparisons Our user study shows the visualization is usable, the summarizer’s choices are human-like 48
- 49. Thank you 49
- 50. Future work ● Tune thresholds and aspects ● Analysis of human reasoning ● Machine learning for selection 50
- 51. Future work ● More evaluation of visualization – Interaction – Deeper heirarchies – Different data – Insight ● Multiple entities ● Improved summarization – Visual cues 51
- 52. Future work ● Machine learning–based selection – Trained on study data – Which ● Regression on comparison selection scores – How many ● Max # of comparisons (2) 52
- 53. Sentence construction ● Always main claim: counts ● All other aspects relate to counts – Support: same (dis)similarity as counts – Contrast: different ● Always mention means ● Mention contros, dists when they are extreme 53
- 54. counts rt co o pp nt u ra s st means contros dists 54
- 55. counts rt co o pp nt u ra s st means contros dists 55
- 56. counts rt co o pp nt u ra s st contros means 56
- 57. Study ● 36 subjects – 24 female – 19–43 years old ● 22 different pairs of entities – Subjects saw ~4 each 57
- 58. Data generation ● No existing dataset ● Generated similar to existing datasets – Distribution, modality ● Explore space of possible data – Too large – Representative of larger space 58
- 59. Generated data ● Generic camera features – Consistent with scenario ● Simple heirarchy – Reduce visual and task complication 59
- 60. Generated data ● 9 comparison types – Constraints on aspects – Range of support/contrast ● 22 summary cases – Summaries by type – Range of ● overall, ● how many, ● which, ● others. 60
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