Authors:
Noriko Imafuji Yasui
1
;
Xavier Llorà
2
;
David E. Goldberg
1
;
Yuichi Washida
3
and
Hiroshi Tamura
3
Affiliations:
1
IllGAL, University of Illinois at Urbana-Champaign, United States
;
2
NCSA, University of Illinois at Urbana-Champaign, United States
;
3
Research and Development Division, Hakuhodo Inc., Japan
Keyword(s):
Online collaborative environments, Innovation and Creativity Support, Topic transition, HITS.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Group Decision Support Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
In this paper, we propose some methodologies for delineating topic and discussant transitions in online collaborative environments, more precisely, focus group discussions for product conceptualization. First, we propose KEE (Key Elements Extraction) algorithm, an algorithm for simultaneously finding key terms and key persons in a discussion. Based on KEE algorithm, we propose approaches for analyzing two important factors of discussions: discussion dynamics and emerging social networks. Examining our approaches using actual network-based discussion data generated by real focus groups in a marketing environment, we report interesting results that demonstrate how our approaches could effectively discover knowledge in the discussions.