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.
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References
Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study: Final report (1998)
Beeferman, D., Berger, A., Lafferty, J.D.: Statistical models for text segmentation. Machine Learning 34(1-3), 177–210 (1999)
Goldberg, D.E., Welge, M., Llorà, X.: DISCUS: Distributed Innovation and Scalable Collaboratio. In: Uncertain Settings. IlliGAL Report No. 2003017, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL (2003)
Kamimaeda, N., Izumi, N., Hasida, K.: Discovery of key persons in knowledge creation based on semantic authoring. In: KMAP (2005)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)
Lawrie, D., Croft, W.B., Rosenberg, A.: Finding topic words for hierarchical summarization. In: SIGIR 2001: the 24th ACM SIGIR conference on Research and development in information retrieval, pp. 349–357 (2001)
Lin, D.: An information-theoretic definition of similarity. In: Proc. 15th International Conf. on Machine Learning, pp. 296–304. Morgan Kaufmann, San Francisco (1998)
Llorà, X., Goldberg, D., Ohsawa, Y., Matsumura, N., Washida, Y., Tamura, H., Masataka, Y., Welge, M., Auvil, L., Searsmith, D., Ohnishi, K., Chao, C.-J.: Innovation and creativity support via chance discovery, genetic algorithms, and data mining. New Mathematics and Natural Computation 2(1), 85–100 (2006)
Matsumura, N., Ohsawa, Y., Ishizuka, M.: Influence diffusion model in text-based communication. In: WWW 2002: Special interest tracks and posters of the 11th international conference on World Wide Web (2002)
Ohsawa, Y., Benson, N.E., Yachida, M.: KeyGraph: Automatic indexing by co-occurencd graph based on building construction metaphor. In: Proceedings of Advances in Digital Libraries, pp. 12–18 (1998)
Porter, M.F.: An algorithm for suffix stripping, pp. 313–316 (1997)
Reich, J.R., Brockhausen, P., Lau, T., Reimer, U.: Ontology-based skills management: Goals, opportunities and challenges. Universal Computer Science 8(5), 506–515 (2002)
Salton, G., Buckley, C.: Term weighting approaches in automatic text retrieval. Technical report (1987)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1986)
Strehl, A., Ghosh, J.: Value-based customer grouping from large retail data-sets. In: Proceedings of the SPIE Conference on Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, Orlando, Florida, USA, vol. 4057, pp. 33–42. SPIE (2000)
Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: Kea: practical automatic keyphrase extraction. In: DL 1999: the fourth ACM conference on Digital libraries, pp. 254–255 (1999)
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Yasui, N.I., Llorà, X., Goldberg, D.E., Washida, Y., Tamura, H. (2008). Key Elements Extraction in Online Collaborative Environments. In: Filipe, J., Cordeiro, J., Cardoso, J. (eds) Enterprise Information Systems. ICEIS 2007. Lecture Notes in Business Information Processing, vol 12. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88710-2_12
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DOI: https://doi.org/10.1007/978-3-540-88710-2_12
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