Abstract
We quantified the semantic content in adolescents’ descriptions of positive and negative life events and studied how these
descriptions are related to the assessment subjective well-being (SWB) at two points in time. The semantic content of the
descriptions was quantified by latent semantic analysis (LSA). LSA is a computational method based on algorithms stemming
from computational linguistics, where a high dimensional semantic representation of words can be generated from co-occurrence
of words in huge text corpora. We investigated if the semantic content of written autobiographical memories of positive and
negative life events predicted traditionally ranked measures of SWB, i.e., self-reports of Positive and Negative Affect, and
thus created semantic measures of SWB. Such measures can be used to investigate the relationship between semantic content and SWB, which could
only indirectly be accomplished by the ranked data. Pupils wrote down positive or negative life events during the last 3 months
and self-reported SWB. Four weeks later, participants were presented with their own description and asked to report current
SWB. The results showed that the semantic representation predicted SWB and experimental conditions. The agreement between
semantic and ranked measures supports the validity of the semantic scores. We argue that our proposed method for studying
SWB provides new and essential information about well-being by the quantification of a richer set of information from adolescents’
own memories.
descriptions are related to the assessment subjective well-being (SWB) at two points in time. The semantic content of the
descriptions was quantified by latent semantic analysis (LSA). LSA is a computational method based on algorithms stemming
from computational linguistics, where a high dimensional semantic representation of words can be generated from co-occurrence
of words in huge text corpora. We investigated if the semantic content of written autobiographical memories of positive and
negative life events predicted traditionally ranked measures of SWB, i.e., self-reports of Positive and Negative Affect, and
thus created semantic measures of SWB. Such measures can be used to investigate the relationship between semantic content and SWB, which could
only indirectly be accomplished by the ranked data. Pupils wrote down positive or negative life events during the last 3 months
and self-reported SWB. Four weeks later, participants were presented with their own description and asked to report current
SWB. The results showed that the semantic representation predicted SWB and experimental conditions. The agreement between
semantic and ranked measures supports the validity of the semantic scores. We argue that our proposed method for studying
SWB provides new and essential information about well-being by the quantification of a richer set of information from adolescents’
own memories.
- Content Type Journal Article
- Category Research Paper
- Pages 1-15
- DOI 10.1007/s10902-012-9385-8
- Authors
- Danilo Garcia, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Sverker Sikström, Department of Psychology, Lund University, 221 00 Lund, Sweden
- Journal Journal of Happiness Studies
- Online ISSN 1573-7780
- Print ISSN 1389-4978