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Norms of valence, arousal, and dominance for 13,915 English lemmas
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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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“Warriner et al. (2013) present VAD ratings for 13,914 common English words, thereby providing an empirically validated way of assessing the overall emotional impact of a word, making mean VAD easy to calculate.”
From Five studies evaluating the impact on mental health and mood of recalling, reading, and discussing fiction (2022) · cites this paper as Warriner et al. (2013)
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Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957) · Cited by 9,433
Cohen, J. (1992) · Psychological Bulletin · doi:10.1037//0033-2909.112.1.155 · Cited by 8,771
Landauer, T. K., & Dumais, S. T. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.211 · Cited by 6,282
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2017) · Springer eBooks · doi:10.1007/978-1-4899-7687-1_907 · Cited by 3,307
Brysbaert, M., & New, B. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.977 · Cited by 2,821
Self-citationBalota, D. A., Yap, M. J.-M., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Rebecca Treiman, A. (2007) · Behavior Research Methods · doi:10.3758/bf03193014 · Cited by 2,758
Sentiment Analysis and Opinion Mining
Liu, B. (2012) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-02145-9 · Cited by 1,872
Age-of-acquisition ratings for 30,000 English words
Kuperman, V., Stadthagen-González, H., & Brysbaert, M. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0210-4 · Cited by 1,308
Self-citationThe representation of abstract words: Why emotion matters.
Kousta, S. T., Vigliocco, G., Vinson, D., Andrews, M. W., & Campo, E. D. (2010) · Journal of Experimental Psychology General · doi:10.1037/a0021446 · Cited by 958
A meta-analytic review of research on gender differences in sexuality, 1993–2007.
Petersen, J. L., & Hyde, J. S. (2010) · Psychological Bulletin · doi:10.1037/a0017504 · Cited by 931
Research works printed but not linked: 1
- Bradley, M. M., & Lang, P. J. (1999). Affective norms for English words (ANEW): Stimuli, instruction manual and affective ratings (Technical Report No. C-1). Gainesville, FL: University of Florida, NIMH Center for Research in Psychophysiology.
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Gao, S., Zika, O., Rogers, R. D., & Thierry, G. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3622-14.2015 · Cited by 138
Metadata
- Authors
- Warriner, A. B., Kuperman, V., & Brysbaert, M.
- Affiliations
- McMaster University (CA)
- Ghent University (BE)
- Corresponding author
- Kuperman, Victor
- Venue
- Behavior Research Methods
- Published
- 2013-02-13
- Volume
- 45
- Issue
- 4
- Pages
- 1191-1207
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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