Semantic network analysis of abstract and concrete word associations

TL;DR

This study applies semantic network analysis to French word association data, revealing structural differences between abstract and concrete concepts and confirming the small-world properties of these networks.

Contribution

First to analyze French word association networks for abstract and concrete concepts, demonstrating their small-world structure and differing organizational patterns.

Findings

Both networks exhibit small-world properties.

Concrete concepts form densely connected communities.

Abstract concepts show more dispersed structural organization.

Abstract

In recent years, a new interest for the use of graph-theory based networks has emerged within the field of cognitive science. This has played a key role in mining the large amount of data generated by word association norms. In the present work, we applied semantic network analyses to explore norms of French word associations for concrete and abstract concepts (Lakhzoum et al., 2021). Graph analyses have shown that the network exhibits high clustering coefficient, sparse density, and small average shortest path length for both the concrete and abstract networks. These characteristics are consistent with a small-world structure. Comparisons between local node statistics and global structural topology showed that abstract and concrete concepts present a similar local connectivity but different overall patterns of structural organisation with concrete concepts presenting an organisation in densely connected communities compared to abstract concepts. These patterns confirm previously acquired knowledge about the dichotomy of abstract and concrete concepts on a larger scale. To the best of our knowledge, this is the first attempt to confirm the generalisability of these properties to the French language and with an emphasis on abstract and concrete concepts.