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Networks of Emotion Concepts

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Listed:
  • Riitta Toivonen
  • Mikko Kivelä
  • Jari Saramäki
  • Mikko Viinikainen
  • Maija Vanhatalo
  • Mikko Sams

Abstract

The aim of this work was to study the similarity network and hierarchical clustering of Finnish emotion concepts. Native speakers of Finnish evaluated similarity between the 50 most frequently used Finnish words describing emotional experiences. We hypothesized that methods developed within network theory, such as identifying clusters and specific local network structures, can reveal structures that would be difficult to discover using traditional methods such as multidimensional scaling (MDS) and ordinary cluster analysis. The concepts divided into three main clusters, which can be described as negative, positive, and surprise. Negative and positive clusters divided further into meaningful sub-clusters, corresponding to those found in previous studies. Importantly, this method allowed the same concept to be a member in more than one cluster. Our results suggest that studying particular network structures that do not fit into a low-dimensional description can shed additional light on why subjects evaluate certain concepts as similar. To encourage the use of network methods in analyzing similarity data, we provide the analysis software for free use (http://www.becs.tkk.fi/similaritynets/).

Suggested Citation

  • Riitta Toivonen & Mikko Kivelä & Jari Saramäki & Mikko Viinikainen & Maija Vanhatalo & Mikko Sams, 2012. "Networks of Emotion Concepts," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-14, January.
    • Handle: RePEc:plo:pone00:0028883
    DOI: 10.1371/journal.pone.0028883
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    Cited by:

    1. Srebrenka Letina & Tessa F. Blanken & Marie K. Deserno & Denny Borsboom, 2019. "Expanding Network Analysis Tools in Psychological Networks: Minimal Spanning Trees, Participation Coefficients, and Motif Analysis Applied to a Network of 26 Psychological Attributes," Complexity, Hindawi, vol. 2019, pages 1-27, February.

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