TY - GEN
T1 - Automatic opinion leader recognition in group discussions
AU - Ho, Yu Chang
AU - Liu, Hao Min
AU - Hsu, Hui Hsin
AU - Lin, Chun Han
AU - Ho, Yao Hua
AU - Chen, Ling Jyh
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/3/16
Y1 - 2017/3/16
N2 - In this paper, we propose an efficient approach to identify the opinion leader from group discussion. This approach is able to recognize the opinion leader without analyzing semantic and syntactic features, which may cost a lot more computing effort. We firstly propose algorithms to evaluate the degree of participation and the emotion expression from the speaking of each member during group discussion. Moreover, by conducting lab-scale experiment, a well-trained model, which is tested on single dataset as well as on cross dataset, is obtained to recognize the opinion leader. Finally, we conduct a field experiment to evaluate the proposed system in a real world setting. The results show that the accuracy of opinion leader identification could achieve to 94.68% on Berlin dataset, 76% on Youtube data and 73.33% on live group discussion. Thus, with this simple and efficient system, opinion leader can be successfully identified in various conditions.
AB - In this paper, we propose an efficient approach to identify the opinion leader from group discussion. This approach is able to recognize the opinion leader without analyzing semantic and syntactic features, which may cost a lot more computing effort. We firstly propose algorithms to evaluate the degree of participation and the emotion expression from the speaking of each member during group discussion. Moreover, by conducting lab-scale experiment, a well-trained model, which is tested on single dataset as well as on cross dataset, is obtained to recognize the opinion leader. Finally, we conduct a field experiment to evaluate the proposed system in a real world setting. The results show that the accuracy of opinion leader identification could achieve to 94.68% on Berlin dataset, 76% on Youtube data and 73.33% on live group discussion. Thus, with this simple and efficient system, opinion leader can be successfully identified in various conditions.
KW - Word-of-mouth communication
KW - emotion recognition
KW - group discussion
KW - influence
KW - opinion leader
UR - http://www.scopus.com/inward/record.url?scp=85017577314&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85017577314&partnerID=8YFLogxK
U2 - 10.1109/TAAI.2016.7880177
DO - 10.1109/TAAI.2016.7880177
M3 - Conference contribution
AN - SCOPUS:85017577314
T3 - TAAI 2016 - 2016 Conference on Technologies and Applications of Artificial Intelligence, Proceedings
SP - 138
EP - 145
BT - TAAI 2016 - 2016 Conference on Technologies and Applications of Artificial Intelligence, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2016
Y2 - 25 November 2016 through 27 November 2016
ER -