Speech Communication, Argumentation Theory and Rhetoric

Semantic feature norms (e.g. STIMULUS: car  RESPONSE: <has 4 wheels>) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the researchers into feature types (e.g. perceptual features, taxonomic features, etc.) by means of content analyses, i.e. by using taxonomies of feature types and having independent coders perform the annotation task. However, the way in which such content analyses are typically performed and reported is not consistent across the literature. This constitutes a serious methodological problem that might undermine the theoretical claims based on such annotations.
In this study we first offer a review of some of the released datasets of annotated semantic feature norms and related taxonomies used for the content analysis. We then provide theoretical and methodological insights in relation to the content analysis methodology. Finally, we apply content analysis to a new dataset of semantic features and show how the method should be applied in order to deliver reliable annotations and replicable coding schemes. We tackle the following issues: 1) taxonomy structure; 2) description of categories; 3) coder training; 4) sustainability of the coding scheme, i.e. comparison between the annotations provided by trained vs novice coders.
The outcomes of the project are threefold: we provide methodological guidelines for semantic feature classification; we provide a revised and adapted taxonomy that can (arguably) be applied to concrete and abstract concepts; we provide a dataset of annotated semantic feature norms.