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dc.contributor.authorDodds, P.-
dc.contributor.authorClark, E.-
dc.contributor.authorDesu, S.-
dc.contributor.authorFrank, M.-
dc.contributor.authorReagan, A.-
dc.contributor.authorWilliams, J.-
dc.contributor.authorMitchell, L.-
dc.contributor.authorHarris, K.-
dc.contributor.authorKloumann, I.-
dc.contributor.authorBagrow, J.-
dc.contributor.authorMegerdoomian, K.-
dc.contributor.authorMcMahon, M.-
dc.contributor.authorTivnan, B.-
dc.contributor.authorDanforth, C.-
dc.identifier.citationProceedings of the National Academy of Sciences of USA, 2015; 112(8):2389-2394-
dc.description.abstractUsing human evaluation of 100,000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (i) the words of natural human language possess a universal positivity bias, (ii) the estimated emotional content of words is consistent between languages under translation, and (iii) this positivity bias is strongly independent of frequency of word use. Alongside these general regularities, we describe interlanguage variations in the emotional spectrum of languages that allow us to rank corpora. We also show how our word evaluations can be used to construct physical-like instruments for both real-time and offline measurement of the emotional content of large-scale texts.-
dc.description.statementofresponsibilityPeter Sheridan Dodds, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow, Karine Megerdoomian, Matthew T. McMahon, Brian F. Tivnan, and Christopher M. Danforth-
dc.publisherNational Academy of Sciences-
dc.rights© Authors-
dc.subjectsocial psychology-
dc.titleHuman language reveals a universal positivity bias-
dc.typeJournal article-
dc.identifier.orcidMitchell, L. [0000-0001-8191-1997]-
Appears in Collections:Aurora harvest 2
Mathematical Sciences publications

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