Muraki-2026-Specificity#339
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I got a bit confused about the ratings because it seems to be a "meta-rating". The original question is whether the word is positive/negative and then they ask how specific/generic this first rating is. Correct? I think the description of the dataset needs to make this clear if that's the case.
| MartinezTomas-2026-AffectiveRatings Martínez-Tomás, Celia and Guasch, Marc and Ferré, Pilar and Lázaro, Miguel and Hinojosa, José Antonio 2026 ratings Spanish Spanish https://doi.org/10.3758/s13428-026-02976-4 MartinezTomas2026 This list contains ratings of valence and arousal for 1,200 Spanish words. The original study also included 4,800 pseudowords which were rated for wordlikeliness in addition to valence and arousal. Further, valence and arousal ratings for the real words was listed from multiple sources [(Ferré et al. 2012](:bib:Ferre2012), [Guasch et al. 2016](:bib:Guasch2016), [Hinojosa et al. 2016](:bib:Hinojosa2016), [Stadthagen-Gonzalez et al. 2017])(:bib:StadthagenGonzalez2017), which were not included in here but can be found in separate lists. | ||
| Hinojosa-2024-Emotions Hinojosa, José A. and Guasch, Marc and Montoro, Pedro R. and Albert, Jacobo and Fraga, Isabel and Ferré, Pilar 2024 ratings Spanish Spanish https://doi.org/10.3758/s13428-023-02229-8 Hinojosa2024 This list contains ratings for seven discrete positive emotions for 9,000 Spanish words given by 3437 participants. The positive emotions include: awe, contentment, amusement, excitement, serenity, relief and pleasure. All ratings were given on a 5-point scale (1 = not at all, 5 = extremely). | ||
| Jung-2026-TranslationalEquivalence Jung, Yoonwon and Shin, Hagyeong and Bergen, Benjamin K. 2026 ratings English Korean https://doi.org/10.48550/arXiv.2602.10414 Jung2026 The Emotion Vocabulary of Korean and English (EVOKE) contains translational mappings and annotations for emotion words in both languages. The original English list was evaluated by two bilingual speakers using bilingual and monolingual dictionaries, with additional back-translation checks to ensure semantic equivalence. Translational equivalence is encoded as a binary variable: 0 indicates a translational equivalent exists in both languages (no lexical gap), 1 indicates no translational equivalent exists (lexical gap). A lexical gap is defined as a cross-linguistic lexicalization mismatch at the single-word level, where a concept is expressed by a single lexical item in one language but only by multi-word expressions or idiomatic constructions in the other language, i.e., absence of a single lexical item to express the same meaning in the other language instantiates a lexical gap. EVOKE | ||
| Muraki-2026-Specificity Muraki, Emiko J. and Pexman, Penny M. 2026 ratings English English https://doi.org/10.3758/s13428-026-02949-7 Muraki2026 This list contains concreteness ratings given on a best-worst scale (BWS), scored following [Kiritchenko & Mohammad (2017)](:bib:Kirichtenko2017). Participants were asked to select the most positive and the most negative word out of a 4-tuple. Using the counting procedure, each term’s score was calculated as the percentage of times it was chosen as most positive minus the percentage of times the term was chosen as most negative. The scores range from −1 (most negative) to 1 (most positive). The original dataset also includes scores for value learning , the Elo predict algorithm, Rescorla-Wagner discriminative learning algorithm, best–worst counting, an analytical best–worst solution, the David Rank scoring algorithm and the logit of scores derived from value learning, Rescorla-Wagner discriminative learning algorithm and best–worst counting, which were not included here. No newline at end of file |
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"This list contains concreteness ratings" -> specificity ratings
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Adding Muraki-2026-Specificity based on Muraki & Pexman (2026) Distinguishing abstraction from abstractness: Specificity norms for 8,500 English words. Closes #328.