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

By Amy Beth Warriner, Victor Kuperman, Marc Brysbaert. 2013

1,933 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

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Citations in the corpus

Statistical Arbitrage in Cryptocurrency Markets (2019, methodology): “Development of an advanced, machine-learning-based statistical arbitrage approach for the cryptocurrency space: we build our approach on the ideas of Fischer and Krauss (2018); The emotional valence and opinion polarization are computed on a daily basis as proposed by Warriner et al. (2013).”
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation (2017, background): “6 Warriner et al. (2013) list a correlation of 0.95 on 1029 common terms with the lexicon by Bradley and Lang (1999).”
DynaSent: A Dynamic Benchmark for Sentiment Analysis (2020, background): “Other important examples are Opin-ionFinder (Wilson et al., 2005), LIWC (Pennebaker et al., 2007), MPQA, Opinion Lexicon (Liu, 2011), SentiWordNet (Baccianella et al., 2010), and the valence and arousal ratings of Warriner et al. (2013).”
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words (2018, methodology): “Observe that the SHR scores for our annotations are markedly higher than those reported by Warriner et al. (2013), especially for arousal and dominance.”
Modeling the interaction between sensory and affective meanings for detecting metaphor (2015, methodology): “…expected to consistently and reliably rate isolated words for their levels of valence (“pleasant-ness”), arousal (“emotional intensity”) and dominance (“control”) (Warriner et al., 2013), the same cannot be expected for more complex expressions such as “the journey of life” or “strings attached”.”
EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis (2017, background): “…models are the predominant format for lexical affective resources in behavioral psychology as evident from the huge number of datasets available for a wide range of languages (see, e.g., Warriner et al. (2013), Stadthagen-Gonzalez et al. (2016), Moors et al. (2013) and Schmidtke et al. (2014)).”
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem (2018, background): “Warriner et al. (2013) (who created EN+) report an inter-study reliability (ISR; i.e., the correlation of the aggregated ratings from two different studies) between the EN and the EN+ lexicon of r = .”
Learning and Evaluating Emotion Lexicons for 91 Languages (2020, methodology): “A.1 Data Preparation The exact design of the Source train-dev-test split is as follows: All entries (words plus ratings) from all splits are taken from Warriner et al. (2013).”
Taste and smell words form an affectively loaded and emotionally flexible part of the English lexicon (2016, neutral): “Linear model fits and 95% confidence intervals for (a) valence and (b) absolute valence for the Warriner et al. (2013) data set.”