Cited by 1,959Sentiment Analysis and Opinion MiningEmotion and Mood RecognitionCategorization, perception, and language

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Norms of valence, arousal, and dominance for 13,915 English lemmas

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Warriner, Amy Beth, Kuperman, Victor, Brysbaert, Marc·2013·Behavior Research Methods

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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 extended that database to nearly 14,000 English lemmas, providing researchers with a much richer source of information, including gender, age, and educational differences in emotion norms. As an example of the new possibilities, we included stimuli from nearly all of the category norms (e.g., types of diseases, occupations, and taboo words) collected by Van Overschelde, Rawson, and Dunlosky (Journal of Memory and Language 50:289-335, 2004), making it possible to include affect in studies of semantic memory.

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Authors: Warriner, A. B., Kuperman, V., & Brysbaert, M.
Affiliations: McMaster University (CA)
Ghent University (BE)
Corresponding author: Kuperman, Victor
ORCID: Kuperman, Victor
Brysbaert, Marc
Venue: Behavior Research Methods
Publisher: Springer Science and Business Media LLC
Published: 2013-02-13
Volume: 45
Issue: 4
Pages: 1191-1207
ISSN: 1554-351X (ISSN-L)
1554-3528
DOI: 10.3758/s13428-012-0314-x
OpenAlex: W2023736093
Cited by: 1,959
References: 48
Citations by year: 2026: 138 · 2025: 140 · 2024: 194 · 2023: 199 · 2022: 199 · 2021: 238 · 2020: 187 · 2019: 180 · 2018: 155 · 2017: 110 · 2016: 102 · 2015: 70 · 2014: 37 · 2013: 9
FWCI: 80.74
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
Locations: Behavior Research Methods (publishedVersion)
PubMed (publishedVersion)
Ghent University Academic Bibliography (Ghent University) (submittedVersion)
MacSphere (McMaster University) (submittedVersion)
MacSphere (McMaster University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
Keywords: valence, arousal, dominance, ANEW, emotional words, semantic memory, word recognition, sentiment analysis, gender differences, age differences, educational differences, taboo words
MeSH terms: Adolescent, Adult, Affect, Aged, Aged, 80 and over, Arousal, Educational Status, Emotions, Female, Humans, Male, Middle Aged, Reading, Reference Values, Semantics, Sesquiterpenes, Sex Characteristics, Social Dominance, Socioeconomic Factors, Vocabulary, Word Association Tests, Databases, Factual, White People, Young Adult, Crowdsourcing

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Norms of valence, arousal, and dominance for 13,915 English lemmas

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