Happiness in Natural language: Words We Relate to Happiness and Descriptions of What Make us Happy

[Speaker] Garcia, Danilo:1,2,3
[Co-author] Kjell, Oscar N. E.:1,4, Sikström, Sverker:1,4
1:Network for Empowerment and Well-Being, Sweden (Sweden), 2:Blekinge Centre for Competence, Karlskrona (Sweden), 3:Centre for Ethics, Law and Mental Health, University of Gothenburg, Gothenburg (Sweden), 4:Department of Psychology, Lund University (Sweden)

Happiness is often assessed using numeric self-rating measures. We, however, were interested in individuals' explicit happiness as expressed in natural language. Participants (N=1000) wrote down ten words they related to happiness and a brief description of what makes them happy. The freely-generated words and descriptions were quantified using the Latent Semantic Analysis algorithm. Participants responded to common self-rating measures of happiness constructs. The words people associate to happiness (r between .13-.35, p <.001) and the descriptions about what makes them happy (r between .07-.17, p<.01) were related to their self-rated happiness. This suggests that it is possible to quantify people's happiness using natural language. The proposed method works more efficiently using the ten words than a descriptive narrative. Probably due the different nature of the questions, but also suggesting that descriptions add unique information about the (un)happy self that is not activated when individuals respond to self-rating measures.

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