LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Proceedings of the 15th ACM on International conference on multimodal interaction
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Be it in our workplace or with our family or friends, negotiation comprises a fundamental fabric of our everyday life, and it is apparent that a system that can automatically predict negotiation outcomes will have substantial implications. In this paper, we focus on finding nonverbal behaviors that are predictive of immediate outcomes (acceptances or rejections of proposals) in a dyadic negotiation. Looking at the nonverbal behaviors of the respondent alone would be inadequate since ample predictive information could also reside in the behaviors of the proposer, as well as the past history between the two parties. With this intuition in mind, we show that a more accurate prediction can be achieved by considering all the three sources (multimodal) of information together. We evaluate our approach on a face-to-face negotiation dataset consisting of 42 dyadic interactions and show that integrating all three sources of information outperforms each individual predictor.