Networks of Emotion Concepts

Research output: Contribution to journalArticle

Standard

Networks of Emotion Concepts. / Toivonen, Riitta; Kivela, Mikko; Saramaki, Jari; Viinikainen, Mikko; Vanhatalo, Maija; Sams, Mikko.

In: PloS one, Vol. 7, No. 1, e28883, 2012, p. 1-14.

Research output: Contribution to journalArticle

Harvard

Toivonen, R, Kivela, M, Saramaki, J, Viinikainen, M, Vanhatalo, M & Sams, M 2012, 'Networks of Emotion Concepts', PloS one, vol. 7, no. 1, e28883, pp. 1-14. https://doi.org/10.1371/journal.pone.0028883

APA

Toivonen, R., Kivela, M., Saramaki, J., Viinikainen, M., Vanhatalo, M., & Sams, M. (2012). Networks of Emotion Concepts. PloS one, 7(1), 1-14. [e28883]. https://doi.org/10.1371/journal.pone.0028883

Vancouver

Author

Toivonen, Riitta ; Kivela, Mikko ; Saramaki, Jari ; Viinikainen, Mikko ; Vanhatalo, Maija ; Sams, Mikko. / Networks of Emotion Concepts. In: PloS one. 2012 ; Vol. 7, No. 1. pp. 1-14.

Bibtex - Download

@article{7ff1a4192c2344ceb513f99c5f08c41a,
title = "Networks of Emotion Concepts",
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/).",
author = "Riitta Toivonen and Mikko Kivela and Jari Saramaki and Mikko Viinikainen and Maija Vanhatalo and Mikko Sams",
year = "2012",
doi = "10.1371/journal.pone.0028883",
language = "English",
volume = "7",
pages = "1--14",
journal = "PloS one",
issn = "1932-6203",
number = "1",

}

RIS - Download

TY - JOUR

T1 - Networks of Emotion Concepts

AU - Toivonen, Riitta

AU - Kivela, Mikko

AU - Saramaki, Jari

AU - Viinikainen, Mikko

AU - Vanhatalo, Maija

AU - Sams, Mikko

PY - 2012

Y1 - 2012

N2 - 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/).

AB - 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/).

UR - http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0028883#authcontrib

U2 - 10.1371/journal.pone.0028883

DO - 10.1371/journal.pone.0028883

M3 - Article

VL - 7

SP - 1

EP - 14

JO - PloS one

JF - PloS one

SN - 1932-6203

IS - 1

M1 - e28883

ER -

ID: 909316