Human language reveals a universal positivity bias
By Peter Sheridan Dodds, E. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow and 4 more. Proceedings of the National Academy of Sciences of the United States of America, 2014
396 citations recorded by the source corpus.
Significance The most commonly used words of 24 corpora across 10 diverse human languages exhibit a clear positive bias, a big data confirmation of the Pollyanna hypothesis. The study’s findings are based on 5 million individual human scores and pave the way for the development of powerful language-based tools for measuring emotion. 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.
Subfields: Cultural Studies, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/b65ed6c8ad2c7197db2aa5eb2a9f2b73b5340ec0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cultural Studies
In Experimental and Cognitive Psychology
Citations in the corpus
- A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods (2015, background): “Recent efforts show that human language have a universal positivity bias [Dodds et al. 2015].”
- Sentiment Analysis: An Overview from Linguistics (2016, neutral): “…have involved forms of blogging and microblogging, such as Twitter or Facebook messages (Kiritchenko et al 2014, Mohammad et al 2013, Ortigosa et al 2014, Thelwall et al to appear, Vilares et al 2015), including the Hedonometer project, an attempt to measure happiness in Twitter (Dodds et al 2015).”
- RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms (2020, background): “To better understand these results, we identify a pervasive intrinsic bias in PTLMs that demonstrates positivity bias in human languages (Dodds et al., 2015).”