Networks of Emotion Concepts

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Toivonen, Riitta
dc.contributor.author Kivela, Mikko
dc.contributor.author Saramaki, Jari
dc.contributor.author Viinikainen, Mikko
dc.contributor.author Vanhatalo, Maija
dc.contributor.author Sams, Mikko
dc.date.accessioned 2017-05-11T08:31:00Z
dc.date.available 2017-05-11T08:31:00Z
dc.date.issued 2012
dc.identifier.citation 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 . DOI: 10.1371/journal.pone.0028883 en
dc.identifier.issn 1932-6203
dc.identifier.other PURE UUID: 7ff1a419-2c23-44ce-b513-f99c5f08c41a
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/networks-of-emotion-concepts(7ff1a419-2c23-44ce-b513-f99c5f08c41a).html
dc.identifier.other PURE LINK: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0028883#authcontrib
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/12866468/journal.pone.0028883.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/25648
dc.description.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/). en
dc.format.extent 14
dc.format.extent 1-14
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries PLOS ONE en
dc.relation.ispartofseries Volume 7, issue 1 en
dc.rights openAccess en
dc.title Networks of Emotion Concepts en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department School services, SCI
dc.contributor.department Department of Neuroscience and Biomedical Engineering
dc.contributor.department Department of Computer Science en
dc.identifier.urn URN:NBN:fi:aalto-201705114032
dc.identifier.doi 10.1371/journal.pone.0028883
dc.type.version publishedVersion


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