2016
DOI: 10.1073/pnas.1612058113
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Linguistic positivity in historical texts reflects dynamic environmental and psychological factors

Abstract: People use more positive words than negative words. Referred to as "linguistic positivity bias" (LPB), this effect has been found across cultures and languages, prompting the conclusion that it is a panhuman tendency. However, although multiple competing explanations of LPB have been proposed, there is still no consensus on what mechanism(s) generate LPB or even on whether it is driven primarily by universal cognitive features or by environmental factors. In this work we propose that LPB has remained unresolve… Show more

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Cited by 48 publications
(59 citation statements)
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References 52 publications
(89 reference statements)
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“…However, with appropriate natural language data (e.g., newspaper articles extending many years into the past) such an analysis using our computational techniques is fairly straightforward. Indeed, such an analysis not only provides estimates of the perceived riskiness of a risk source over time but also can be used to calculate changes in the psychological structure of this risk source, including changes in its close associates over time (see, e.g., Iliev et al 2016, Garg et al 2018 for examples of such an approach applied to other domains in psychology).…”
Section: Computational Analysis Of Risk Perceptionmentioning
confidence: 99%
“…However, with appropriate natural language data (e.g., newspaper articles extending many years into the past) such an analysis using our computational techniques is fairly straightforward. Indeed, such an analysis not only provides estimates of the perceived riskiness of a risk source over time but also can be used to calculate changes in the psychological structure of this risk source, including changes in its close associates over time (see, e.g., Iliev et al 2016, Garg et al 2018 for examples of such an approach applied to other domains in psychology).…”
Section: Computational Analysis Of Risk Perceptionmentioning
confidence: 99%
“…In most cases, sentiment analysis has been applied on a short-term time scale, such as social media interactions (Lansdall-Welfare et al 2012). However, some researchers have explored a longer time scale, analysing the expression of emotions in several decades of song lyrics (Dodds and Danforth 2010), newspaper articles (Iliev et al 2016), in Grimm's folktales (Mohammad 2013) or in centuries of literary works (Acerbi et al 2013).…”
Section: Introductionmentioning
confidence: 99%
“…That is, it shows that males are more positively represented in text corpora than females. Such a discrepancy would be surprising in light of the Pollyanna hypothesis (Boucher & Osgood, 1969) also known a linguistic positivity bias (Iliev, Hoover, Dehghani, & Axelrod, 2016). The Pollynana hypothesis states that positive words are more prevalent, easily learned, and used across languages.…”
Section: S(male Positive) -S(male Negative) > S(female Positive) -mentioning
confidence: 99%