2013
DOI: 10.3758/s13428-012-0314-x
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Norms of valence, arousal, and dominance for 13,915 English lemmas

Abstract: Information about the affective meanings of words is used by researchers working on emotions and moods, word recognition and memory, and text-based sentiment analysis. Three components of emotions are traditionally distinguished: valence (the pleasantness of a stimulus), arousal (the intensity of emotion provoked by a stimulus), and dominance (the degree of control exerted by a stimulus). Thus far, nearly all research has been based on the ANEW norms collected by Bradley and Lang (1999) for 1,034 words. We ext… Show more

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Cited by 1,491 publications
(2,027 citation statements)
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References 46 publications
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“…As the Janschewitz (2008) and Eilola and Havelka (2010) norms did not list values for most of the new neutral words we selected for this experiment, valence and arousal measures were instead procured from Warriner, Kuperman, and Brysbaert (2013).…”
Section: Methodsmentioning
confidence: 99%
“…As the Janschewitz (2008) and Eilola and Havelka (2010) norms did not list values for most of the new neutral words we selected for this experiment, valence and arousal measures were instead procured from Warriner, Kuperman, and Brysbaert (2013).…”
Section: Methodsmentioning
confidence: 99%
“…We employed several well-established lexicons, such as Emolex (Mohammad and Turney, 2010) (10), Hedonometer (Dodds et al, 2011) (1), DAL (Whissell, 1989) (3), Warriner's Norms (Warriner et al, 2013) (3), Age of Adquisition (Kuperman et al, 2012) (1), Bristol familiarity and imaginary norms (Stadthagen-Gonzalez and Davis, 2006) (2), and WWBP lexicons (Schwartz et al, 2016(Schwartz et al, , 2013World Well-Being Project, 2017) which includes: PERMA (10), OCEAN (5), time-oriented (3) and affect-intensity lexicons (2). We also used MentalDisLex (Zirikly et al, 2016) (1) (2), determiners (1), word counts (1), mean word length (1), number of webpage links (1), lexical diversity (1)(mean fraction of different words among 100 random subsamples of 10 words) and the fraction of words semantically similar to several keywords 1 (8).…”
Section: Body Content Features (68 Features)mentioning
confidence: 99%
“…The situation is rapidly improving for the English language, where age-of-acquisition ratings have been collected for 30,000 words (Kuperman, Stadthagen-Gonzalez, & Brysbaert, 2012), affective ratings for 14,000 words (Warriner, Kuperman, & Brysbaert, 2013), and concreteness ratings for 40,000 words (Brysbaert, Warriner, & Kuperman, in press). The main reason for this improvement is that in English, one can make use of Amazon Mechanical Turk, a service created by the company Amazon where Internet users can earn a small amount of money by doing so-called Human Intelligence Tasks.…”
Section: Introductionmentioning
confidence: 99%