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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 three 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.