Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Espoo, Finland.
PLoS One. 2012;7(1):e28883. doi: 10.1371/journal.pone.0028883. Epub 2012 Jan 20.
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/).
这项工作的目的是研究芬兰情感概念的相似网络和层次聚类。芬兰语母语者评估了 50 个最常用来描述情感体验的芬兰语单词之间的相似性。我们假设，网络理论中开发的方法，如识别聚类和特定的局部网络结构，可以揭示使用传统方法（如多维标度（MDS）和普通聚类分析）难以发现的结构。这些概念分为三个主要聚类，可以描述为负面、正面和惊喜。负面和正面聚类进一步分为有意义的子聚类，与之前的研究结果相对应。重要的是，这种方法允许同一个概念成为多个聚类的成员。我们的结果表明，研究不符合低维描述的特定网络结构可以进一步阐明为什么受试者会评估某些概念相似。为了鼓励在分析相似性数据时使用网络方法，我们免费提供分析软件（http://www.becs.tkk.fi/similaritynets/）。
