Classification is the fundamental interpretive process found in political cognition, whether individual or collective. Recall that Ronald Reagan's ``freedom fighters'' and Leonid Brezhnev's ``bandits and criminals'' were two classifications for the same Afghanistan resistance. These leaders selected labels to call forth desired political actions in their audiences. The intended perlocutionary effects arise because the labels elicit particular ways of framing the problem, or ``seeing-as'' (Wittgenstein, 1953), and from these stereotypical, or ``natural'' political responses follow. Examples such as this suggest that classification is a fundamental political act (Mefford, 1988a) standing behind conflict and cooperation.
The basic cognitive process at work in classification involves an interplay between the micro-classifications of actions and relationships that call forth particular constructions of situations. These constructions, in turn, demand concordant micro classifications. Paul Ricoeur (1971) has dubbed this the double hermeneutic. When problem framing is freed from temporal anchors in the present, this classificational process shades into analogical reasoning, or precedent logics, which has been proposed to model political understanding and organizational decision-making (Alker &Christensen, 1972; Alker, Bennet &Mefford, 1980; Mallery &Hurwitz, 1987; Mefford, 1987; Mallery, 1988a: 38-47). Because classification is a central cognitive process with high relevance for politics, tools to simulate classification are a high research priority. They can help reveal the cognitive dimensions of the phenomena as they yield methods for computational politics - the subfield of political science that uses symbolic reasoning techniques from artificial intelligence to formally model politics.
This paper introduces semantic content analysis, a methodology whose vehicle is automatic recognition and classification of instances in the kno