A graphical, self‐organizing approach to classifying electronic meeting output

Richard E. Orwig, Hsinchun Chen and Jay F. Nunamaker

Journal of the American Society for Information Science, 1997, vol. 48, issue 2, 157-170

Abstract: This article describes research in the application of a Kohonen Self‐Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. Electronic brainstorming is one of the most productive tools in the Electronic Meeting System called GroupSystems. A major step in group problem solving involves the classification of electronic brainstorming output into a manageable list of concepts, topics, or issues that can be further evaluated by the group. This step is problematic due to information overload and the cognitive demand of processing a large quantity of textual data. This research builds upon previous work in automating the meeting classification process using a Hopfield neural network. Evaluation of the Kohonen output comparing it with Hopfield and human expert output using the same set of data found that the Kohonen SOM performed as well as a human expert in representing term association in the meeting output and outperformed the Hopfield neural network algorithm. In addition, recall of consensus meeting concepts and topics using the Kohonen algorithm was equivalent to that of the human expert. However, precision of the Kohonen results was poor. The graphical representation of textual data produced by the Kohonen SOM suggests many opportunities for improving information organization of textual information. Increasing uses of electronic mail, computer‐based bulletin board systems, and world‐wide web services present unique challenges and opportunities for a system‐aided classification approach. This research has shown that the Kohonen SOM may be used to automatically create “a picture that can represent a thousand (or more) words.” © 1997 John Wiley & Sons, Inc.

Date: 1997
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