Human language reveals a universal positivity bias
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 emotion...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | en_US |
| Published: |
National Academy of Sciences (U.S.)
2015
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| Online Access: | http://hdl.handle.net/1721.1/98030 https://orcid.org/0000-0001-9487-9359 |
| _version_ | 1826194272831930368 |
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| author | 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 McMahon, Matthew T. Tivnan, Brian F. Danforth, Christopher M. Frank, Morgan Ryan |
| author2 | Massachusetts Institute of Technology. Center for Computational Engineering |
| author_facet | Massachusetts Institute of Technology. Center for Computational Engineering 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 McMahon, Matthew T. Tivnan, Brian F. Danforth, Christopher M. Frank, Morgan Ryan |
| author_sort | Dodds, Peter Sheridan |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T09:53:31Z |
| format | Article |
| id | mit-1721.1/98030 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:53:31Z |
| publishDate | 2015 |
| publisher | National Academy of Sciences (U.S.) |
| record_format | dspace |
| spelling | mit-1721.1/980302022-09-26T14:20:39Z Human language reveals a universal positivity bias 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 McMahon, Matthew T. Tivnan, Brian F. Danforth, Christopher M. Frank, Morgan Ryan Massachusetts Institute of Technology. Center for Computational Engineering Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Desu, Suma Frank, Morgan Ryan 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. 2015-08-05T14:58:23Z 2015-08-05T14:58:23Z 2015-02 2014-06 Article http://purl.org/eprint/type/JournalArticle 0027-8424 1091-6490 http://hdl.handle.net/1721.1/98030 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. https://orcid.org/0000-0001-9487-9359 en_US http://dx.doi.org/10.1073/pnas.1411678112 Proceedings of the National Academy of Sciences 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. application/pdf National Academy of Sciences (U.S.) National Academy of Sciences (U.S.) |
| spellingShingle | 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 McMahon, Matthew T. Tivnan, Brian F. Danforth, Christopher M. Frank, Morgan Ryan Human language reveals a universal positivity bias |
| title | Human language reveals a universal positivity bias |
| title_full | Human language reveals a universal positivity bias |
| title_fullStr | Human language reveals a universal positivity bias |
| title_full_unstemmed | Human language reveals a universal positivity bias |
| title_short | Human language reveals a universal positivity bias |
| title_sort | human language reveals a universal positivity bias |
| url | http://hdl.handle.net/1721.1/98030 https://orcid.org/0000-0001-9487-9359 |
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