Human language reveals a universal positivity bias
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Dodds-2015-Human language revea.pdf
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2.6 MB
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Author(s)
Dodds, Peter Sheridan
Clark, Eric M.
Desu, Suma
Reagan, Andrew J.
Williams, Jake Ryland
Mitchell, Lewis
Harris, Kameron Decker
Kloumann, Isabel M.
Bagrow, James P.
Megerdoomian, Karine
Date Issued
February 2015
Journal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences (U.S.)
Citation
Dodds, Peter Sheridan, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, et al. “Human Language Reveals a Universal Positivity Bias.” Proc Natl Acad Sci USA 112, no. 8 (February 9, 2015): 2389–2394.
Version
Final published version
Abstract
Using 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.
MIT Department
Massachusetts Institute of Technology. Center for Computational Engineering
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1073/pnas.1411678112
