Dimensional and categorical emotional ratings of Russian nouns: The ENRuN-2 database

Databases with emotional ratings of words are essential for studying emotional information processing in psychology, psycholinguistics, neuroscience, and related disciplines. The emotional content of stimuli is traditionally described within two approaches: dimensional (rating words on continuous scales such as valence and arousal) and categorical (rating associations of words with discrete emotion categories). Databases with emotional ratings of words have been developed for various languages, but only a few integrate both approaches. The present study introduces the first large database with emotional ratings of Russian words, ENRuN-2, which includes 6,708 nouns rated by valence, arousal, and relatedness to five discrete emotion categories: happiness, sadness, fear, anger, and disgust. Ratings were collected from 3,902 native speakers, with data quality procedures yielding a final sample of 2,681 participants. Reliability analyses demonstrated high inter-rater consistency across parameters (mean ICCs = .84-.96) and strong intra-rater stability of ratings. High correlations with existing Russian affective norms (rs = .50-.98) demonstrate the convergent validity of the ratings. Analysis of relationships among emotional parameters revealed that they are mostly nonlinear and consistent with findings from similar databases in other languages, further supporting validity. Gender differences in ratings are negligible, and correlations between them are high; however, male and female ratings diverge substantially for some words. The ENRuN-2 database can be used in a wide array of emotion studies and is freely available on the Open Science Framework ( https://osf.io/sv9mh ).