Visualizing Group Dynamics based on Multiparty Meeting Understanding

Ni Zhang, Tongtao Zhang, Indrani Bhattacharya, Heng Ji, Rich Radke


Abstract
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.
Anthology ID:
D18-2017
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2018
Address:
Brussels, Belgium
Editors:
Eduardo Blanco, Wei Lu
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
96–101
Language:
URL:
https://aclanthology.org/D18-2017
DOI:
10.18653/v1/D18-2017
Bibkey:
Cite (ACL):
Ni Zhang, Tongtao Zhang, Indrani Bhattacharya, Heng Ji, and Rich Radke. 2018. Visualizing Group Dynamics based on Multiparty Meeting Understanding. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 96–101, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Visualizing Group Dynamics based on Multiparty Meeting Understanding (Zhang et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-2017.pdf