Abstract
We propose a broadcating agent system called {\it POC caster} that generates understandable conversational representation from heterogeneous text-based opinions. POC caster introduces an opinion of a community member by a conversational method in Public Opinion Channel(POC) that is an interactive broadcasting system supporting community knowledge creation. The way to generate conversational representation from an opinion is consist of two processes. The first process is an analysis of an intention of the opinion by referring the last word of a sentence. The second process is applying some rules about intentions and positions of the sentences to make an understandable conversation. The psychological experiments about understandability of generated conversations are described.
