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Dominance detection in Meetings Using Support Vector Machines

This study explores the use of support vector machines to automatically detect dominance in meetings. It examines the concept of dominance, gathers dominance judgments from participants, and extracts features to build a classifier. The results show promising performance in predicting dominance levels.

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Dominance detection in Meetings Using Support Vector Machines

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  1. Dominance detection in Meetings Using Support Vector Machines Rutger Rienks and Dirk Heylen University of Twente MLMI 2005

  2. Outline • Dominance • Dominance Judgements • Towards automatic dominance detection • Results • Conclusion

  3. Cooperative • For a meeting to be effective, participants should be cooperative • The Grice’s cooperation principle is founded on four maxims [Grice,1975] • Speak no more or less than required • Only say things for which you have evidence • Only say things relevant for the discussion • Formulate such, that you are clearly understood

  4. Uncooperative • The chairman of a meeting should e.g. • Make sure that everybody can get the floor • Cut off people when speaking too long • Intervene when contributions are irrelevant • People that are too dominant frustrate the cooperative principles and hence the meeting process • Can we automatically detect if one person in a meeting is more or less dominant than someone else?

  5. Dominance The Dominator is a group member trying to assert authority by manipulating the group or certain individuals in the group. Dominators may use flattery or proclaim their superior status to gain attention and interrupt contributions of others [Hoffman,1979]

  6. Dominance Behavioral features displayed by people that behave dominantly [Bales & Cohen,1979, Bales, 1951]

  7. But, do people agree on the concept of dominance?

  8. Dominance Judgements • Corpus of eight four-person meetings (95 minutes) • Ten people to rank the meeting participants of four meetings on their conveyed dominance • Result: a total of five rankings for every meeting

  9. Dominance Judgements ‘μ’ = Reranking of the sum of all annotators for each participant ‘σ’ = The sum of the differences for all annotators with `μ’ for each participant

  10. Dominance Judgements • When comparing the variance of the judges with the variance resulting from randomly generated rankings, the distribution of the variance of the annotators significantly differs (p<0.001) from a distribution with randomly generated rankings. • So, people seem to agree.

  11. Towards Automatic Dominance Detection • Dominance can be regarded as a higher level concept that may be deduced automatically from a subset of lower level observations [Reidsma et al., 2004]. • Most of the features we’ve seen from Bales and Bales and Choen are hard to operationalize and measure. (c.f. `Alienated’ and `Purposeful’) • For our classifier we considered some common sense features that possibly might reveal us something about the dominance of a person in in meetings.

  12. Towards Automatic Dominance Detection • Types of extracted features from the meetings • The speaking time in seconds • The number of words spoken in the whole meeting • The number of turns in a meeting • The number of times addressed • The number of times privately addressed • The number of successful interruptions • The number of times interrupted • The ratio of successful interruptions and the number of times being interrupted • The number of times the floor is grabbed by a participant • The number of questions asked

  13. Towards Automatic Dominance Detection • The obtained feature values were normalized and made comparable by mapping them on a (High, Normal, Low) scale • The `average’ judgement rankings were mapped onto the same (High, Normal, Low) scale and used as class labels • This resulted in a data-set of 32 samples with twelve samples receiving the class label ‘High’, ten ‘Normal’ and ten ‘Low’. • We define our baseline performance as the share of the most frequent class label (‘High’), which was 37.5% of all labels.

  14. Towards Automatic Dominance Detection • We aimed to predict the dominance level of the meeting participants with the least number of possible features. As obtaining some features is expected to be easier than obtaining all features.

  15. Towards Automatic Dominance Detection • To decrease our amount of features we used Weka’s SVM Attribute Evaluator • The top five features appeared to be: • The number of floorgrabs • The number of turns • The number of successful interruptions • The number of words used • The number of questions asked

  16. Results • The classifier obtained the highest performance (75%) using the best two features (10 f.c.) • The Confusion Matrics: • The 90% confidence interval for our classifier lies between a performance of 62% and 88%. Having a lower bound higher than the 37.5% baseline. Actual Predicted

  17. Conclusion • Aware of the fact that our sample size is relatively small and that not all meetings follow the same format, we do think that our results suggest that: • It is possible to have a system analyzing the level of dominance of the meeting participants.

  18. Further research • Run tests on more data • Use semantically oriented features • Try to apply a system in real time • Investigate te impact of the results on various types of players (chairman, participants, agents)

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