TY - GEN
T1 - Visualizing group dynamics based on multiparty meeting understanding
AU - Zhang, Ni
AU - Zhang, Tongtao
AU - Bhattacharya, Indrani
AU - Ji, Heng
AU - Radke, Richard J.
N1 - Funding Information:
Thanks to Mike Foley, Christoph Riedl and Brooke Foucault Welles at Northeastern University for the experimental design. This work was supported by the U.S. National Science Foundation No. IIP-1631674. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2018 Association for Computational Linguistics.
PY - 2018
Y1 - 2018
N2 - Group discussions are usually aimed at sharing opinions, reaching consensus and making good decisions based on group knowledge. During a discussion, participants might adjust their own opinions as well as tune their attitudes towards others’ opinions, based on the unfolding interactions. In this paper, we demonstrate a framework to visualize such dynamics; at each instant of a conversation, the participants’ opinions and potential influence on their counterparts is easily visualized. We use multi-party meeting opinion mining based on bipartite graphs to extract opinions and calculate mutual influential factors, using the Lunar Survival Task as a study case.
AB - Group discussions are usually aimed at sharing opinions, reaching consensus and making good decisions based on group knowledge. During a discussion, participants might adjust their own opinions as well as tune their attitudes towards others’ opinions, based on the unfolding interactions. In this paper, we demonstrate a framework to visualize such dynamics; at each instant of a conversation, the participants’ opinions and potential influence on their counterparts is easily visualized. We use multi-party meeting opinion mining based on bipartite graphs to extract opinions and calculate mutual influential factors, using the Lunar Survival Task as a study case.
UR - http://www.scopus.com/inward/record.url?scp=85069039327&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069039327&partnerID=8YFLogxK
U2 - 10.18653/v1/d18-2017
DO - 10.18653/v1/d18-2017
M3 - Conference contribution
AN - SCOPUS:85069039327
T3 - EMNLP 2018 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings
SP - 96
EP - 101
BT - EMNLP 2018 - Conference on Empirical Methods in Natural Language Processing
PB - Association for Computational Linguistics (ACL)
T2 - 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2018
Y2 - 31 October 2018 through 4 November 2018
ER -