Abstract
We show that, using a Support Vector Machine classifier, it is possible to determine with a 75% success rate who dominated a particular meeting on the basis of a few basic features. We discuss the corpus we have used, the way we had people judge dominance and the features that were used.
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Keywords
- Goal Orientation
- Dominance Ranking
- Meeting Participant
- Dominance Level
- Neural Information Processing System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Rienks, R., Heylen, D. (2006). Dominance Detection in Meetings Using Easily Obtainable Features. In: Renals, S., Bengio, S. (eds) Machine Learning for Multimodal Interaction. MLMI 2005. Lecture Notes in Computer Science, vol 3869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677482_7
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DOI: https://doi.org/10.1007/11677482_7
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