Automatic Annotation of Semantic Fields for Political Science Research

Abstract

This article discusses methods for automatic annotation of political texts for semantic fields—groups of words with related meanings. This type of annotation is useful when studying political communication, such as legislative debate or political speeches. We present three types of automatic annotation: unsupervised clustering, dictionary-based approaches, and a method based on relevant experimental data. All methods are applied to analyzing Margaret Thatcher's political rhetoric. For this data, we find that unsupervised clustering is most useful for tracing topics; dictionary-based methods are most effective in a comparative setting; whereas the last method is the most promising for detecting off-topic, singular uses of semantic domains, which are often rhetorical tools used to achieve a political end. Applicability, strengths, and weaknesses of each method and of their combinations are addressed in detail.

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