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

This article describes research in the application of a ( topics ) in an electronic meeting setting. Electronic meetKohonen Self-Organizing Map (SOM) to the problem of ing systems ( EMSs ) provide support for large groups classification of electronic brainstorming output and an interactively working on a single problem or collection evaluation of the results. Electronic brainstorming is one of problems ( Nunamaker, Dennis, Valacich, & Vogel, of the most productive tools in the Electronic Meeting 1991; Vogel, Nunamaker, Martz, Grohowski, & McGoff, System called GroupSystems. A major step in group problem solving involves the classification of electronic 1989 ) . Large groups of people are thereby enabled to use brainstorming output into a manageable list of concepts, a network of computers to discuss complex organizational topics, or issues that can be further evaluated by the problems electronically. These electronic discussions cregroup. This step is problematic due to information over- ate large quantities of text in a very short period of time. A load and the cognitive demand of processing a large major stage in the group problem solving process involves quantity of textual data. This research builds upon previous work in automating the meeting classification pro- classifying these large quantities of text into a manageable cess using a Hopfield neural network. Evaluation of the list or set of concepts / topics. Experience with this classiKohonen output comparing it with Hopfield and human fication process has shown that meeting convergence is expert output using the same set of data found that the problematic for participants and meeting facilitators. Kohonen SOM performed as well as a human expert in representing term association in the meeting output and The prevailing EMS provides only clerical classificaoutperformed the Hopfield neural network algorithm. In tion support for browsing the text and creating a list of addition, recall of consensus meeting concepts and top- topics for group members. However, it does not provide ics using the Kohonen algorithm was equivalent to that system support for managing or organizing the large volof the human expert. However, precision of the Kohonen results was poor. The graphical representation of textual ume of text that may be created as output from an elecdata produced by the Kohonen SOM suggests many op- tronic brainstorming session. The synthesis of electronic portunities for improving information organization of tex- brainstorming comments is a classification problem. It is tual information. Increasing uses of electronic mail, com- something that humans currently do well, but not willputer-based bulletin board systems, and world-wide ingly, a situation that suggests using an artificial intelliweb services present unique challenges and opportunities for a system-aided classification approach. This re- gence approach to understanding how humans classify search has shown that the Kohonen SOM may be used concepts and developing a system to test whether better to automatically create ‘‘a picture that can represent a classification support for groups can be provided. thousand (or more) words.’’ This research is a continuation of research previously reported in Chen, Hsu, Orwig, Hoopes, & Nunamaker