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arxiv: 2602.10414 · v2 · submitted 2026-02-11 · 💻 cs.CL

EVOKE: Emotion Vocabulary Of Korean and English

Yoonwon Jung , Hagyeong Shin , Benjamin K. Bergen This is my paper

Pith reviewed 2026-05-16 03:44 UTC · model grok-4.3

classification 💻 cs.CL
keywords emotion vocabularyKorean-English parallel datasetemotion wordspolysemymetaphorsnatural language processingpsycholinguistics
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The pith

A parallel Korean-English dataset catalogs 1400 emotion words with annotations for multiple meanings and metaphors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents EVOKE as a new resource that lists emotion words in Korean and English along with their translations and detailed labels. It covers over a thousand words per language and marks adjectives and verbs for their senses, relationships, and metaphorical uses. The goal is to give researchers a flexible, theory-neutral collection that supports work in emotion science, psycholinguistics, and language processing. By releasing the data publicly, the authors enable others to apply the resource according to their own questions or frameworks.

Core claim

EVOKE supplies the most systematic collection of emotion words available for both Korean and English, containing 1,426 Korean terms and 1,397 English terms, with systematic annotations on 819 Korean and 924 English adjectives and verbs that identify polysemy, sense relations, and emotion-related metaphors.

What carries the argument

The EVOKE dataset itself, a bilingual list of emotion words equipped with many-to-many translations and multi-sense annotations that mark polysemous items and metaphorical expressions.

If this is right

  • Cross-language studies can compare how Korean and English speakers express the same emotions through direct word alignments.
  • Computational systems for emotion detection gain labeled examples that distinguish literal from metaphorical uses.
  • Researchers can filter the data to focus only on language-specific emotion terms or only on adjectives versus verbs.
  • The annotations support tests of whether certain metaphors for emotion appear more often in one language than the other.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The resource could serve as a seed for building emotion lexicons in additional languages by following the same annotation steps.
  • Models trained on the dataset might reveal whether certain emotion concepts are more finely divided in one language than the other.
  • Future extensions could add usage examples or frequency data to make the words more usable for real-world text analysis.

Load-bearing premise

The chosen words and their annotations accurately and comprehensively capture emotion vocabulary without systematic selection bias or annotator disagreement.

What would settle it

Independent reviewers identifying many common emotion words absent from the lists or showing large-scale disagreement on the provided sense and metaphor labels would indicate the dataset is incomplete or inconsistent.

Figures

Figures reproduced from arXiv: 2602.10414 by Benjamin K. Bergen, Hagyeong Shin, Yoonwon Jung.

Figure 1
Figure 1. Figure 1: The structure of the Korean–English parallel emotion word dataset. Words in both languages are [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The percentages of annotation values for each annotation criterion for adjectives in Korean [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Agreement scores across annotation criteria for the English adjective agreement set. [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Agreement scores across annotation criteria for the Korean adjective agreement set. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Agreement scores across annotation criteria for the English verb agreement set. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Agreement scores across annotation criteria for the Korean verb agreement set. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The percentages of annotation values for each criterion for verbs in Korean and English. The [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
read the original abstract

This paper introduces EVOKE (Emotion Vocabulary of Korean and English), a Korean-English parallel dataset of emotion words. The dataset offers comprehensive coverage of emotion words in each language, in addition to many-to-many translations between words in the two languages and identification of language-specific emotion words. The dataset contains 1,426 Korean words and 1,397 English words, and we systematically annotate 819 Korean and 924 English adjectives and verbs. We also annotate multiple meanings of each word and their relationships, identifying polysemous emotion words and emotion-related metaphors. The dataset is, to our knowledge, the most systematic and theory-agnostic dataset of emotion words in both Korean and English to date. It can serve as a practical tool for emotion science, psycholinguistics, computational linguistics, and natural language processing, allowing researchers to adopt different views on the resource reflecting their needs and theoretical perspectives. The dataset is publicly available at https://github.com/yoonwonj/EVOKE.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces EVOKE, a Korean-English parallel dataset of emotion words with 1,426 Korean and 1,397 English entries. It provides many-to-many translations, identifies language-specific words, and annotates 819 Korean and 924 English adjectives/verbs for polysemy, multiple meanings, and emotion-related metaphors. The authors position the resource as the most systematic and theory-agnostic emotion vocabulary dataset available for both languages and release it publicly via GitHub.

Significance. A well-documented, bias-minimized parallel emotion lexicon with explicit handling of polysemy and metaphor could support cross-linguistic studies in emotion science, psycholinguistics, and NLP. The public release and many-to-many alignment are practical strengths that would allow researchers to apply different theoretical filters to the same underlying data.

major comments (3)
  1. [Abstract] Abstract: the central claim that EVOKE is 'the most systematic and theory-agnostic dataset of emotion words in both Korean and English to date' is unsupported because the manuscript supplies neither the source lexicons or corpora from which the 1,426/1,397 words were drawn nor any word-selection or filtering protocol.
  2. [Annotations] Annotations section: the paper reports annotating 819 Korean and 924 English adjectives/verbs for polysemy, multiple meanings, and metaphors but provides no inter-annotator agreement statistics (Cohen/Fleiss kappa, percentage agreement) or disagreement-resolution procedure, leaving the reproducibility of the 819/924 labels unverifiable.
  3. [Dataset Construction] Dataset construction: without explicit criteria for initial word extraction, inclusion thresholds, or coverage validation against existing Korean or English emotion lexicons, it is impossible to evaluate whether the final counts reflect comprehensive coverage or systematic gaps.
minor comments (2)
  1. [Introduction] The abstract and introduction would benefit from a short table comparing EVOKE's size, annotation types, and alignment properties to the most relevant prior English and Korean emotion lexicons.
  2. [Data Availability] The GitHub repository link is given, but the paper does not describe the released file formats, column definitions, or example usage scripts.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight areas where the manuscript can be strengthened for clarity and reproducibility. We address each major comment below and will incorporate the suggested revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that EVOKE is 'the most systematic and theory-agnostic dataset of emotion words in both Korean and English to date' is unsupported because the manuscript supplies neither the source lexicons or corpora from which the 1,426/1,397 words were drawn nor any word-selection or filtering protocol.

    Authors: We agree that the abstract claim requires explicit supporting details to be fully substantiated. In the revised manuscript, we will expand the relevant sections to specify the source lexicons and corpora used for initial word collection, along with the complete word-selection and filtering protocol, including any inclusion/exclusion criteria applied. revision: yes

  2. Referee: [Annotations] Annotations section: the paper reports annotating 819 Korean and 924 English adjectives/verbs for polysemy, multiple meanings, and metaphors but provides no inter-annotator agreement statistics (Cohen/Fleiss kappa, percentage agreement) or disagreement-resolution procedure, leaving the reproducibility of the 819/924 labels unverifiable.

    Authors: We acknowledge that reporting inter-annotator agreement is essential for verifying the annotation process. The revised manuscript will include Cohen's kappa values, percentage agreement figures, and a detailed description of the disagreement-resolution procedure employed during annotation. revision: yes

  3. Referee: [Dataset Construction] Dataset construction: without explicit criteria for initial word extraction, inclusion thresholds, or coverage validation against existing Korean or English emotion lexicons, it is impossible to evaluate whether the final counts reflect comprehensive coverage or systematic gaps.

    Authors: We will revise the Dataset Construction section to provide explicit criteria for initial word extraction, the specific inclusion thresholds used, and a validation analysis comparing coverage against established Korean and English emotion lexicons to better demonstrate the dataset's scope and any potential gaps. revision: yes

Circularity Check

0 steps flagged

No circularity: purely descriptive dataset paper with no derivations or fitted predictions

full rationale

The manuscript introduces EVOKE as a parallel Korean-English emotion lexicon with word counts, annotations for adjectives/verbs, polysemy, and metaphors. It contains no equations, no parameter fitting, no predictions of held-out quantities, and no derivation chain. The claim of being 'most systematic and theory-agnostic' is a qualitative assertion about coverage and annotation choices rather than a result obtained by reducing to prior self-citations or by construction from fitted inputs. All load-bearing elements are external data-collection steps whose validity can be checked against the released GitHub resource and independent replication, satisfying the self-contained criterion.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on standard linguistic assumptions about the existence and translatability of emotion vocabulary; no free parameters, invented entities, or ad-hoc axioms are introduced.

axioms (1)
  • domain assumption Emotion words form a definable, alignable set across Korean and English that can be systematically collected and annotated.
    The parallel dataset construction presupposes that such a set exists and can be identified without language-specific theoretical bias.

pith-pipeline@v0.9.0 · 5466 in / 1132 out tokens · 40331 ms · 2026-05-16T03:44:05.866675+00:00 · methodology

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Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages · 1 internal anchor

  1. [1]

    EVOKE: Emotion Vocabulary Of Korean and English

    Introduction Emotion words reveal how humans conceptualize and communicate emotional experiences through language (Wierzbicka, 1999). They also provide a foundation for lexicon-based sentiment analyses and emotion detection (Liapis et al., 2025; Raji and De Melo, 2020). Yet, emotion words are diverse and notoriously difficult to identify (Fehr and Rus- se...

    work page internal anchor Pith review Pith/arXiv arXiv 1999

  2. [2]

    I feel X

    Background 2.1. Theories Defining Emotion Words Psychologicalandlinguisticworkhasproducedvar- ied methods to define emotion words. Some early studiesreliedonunguidedintuition,withratersjudg- ingwordsasemotionalornotwithoutexplicitcriteria (Fehr and Russell, 1984; Storm and Storm, 1987). More recent work uses acceptability judgments on emotion words, placi...

    work page 1984

  3. [3]

    Objective of the Dataset We introduce EVOKE (Emotion Vocabulary Of Korean and English), a Korean-English paral- lel dataset of emotion words with comprehen- sive coverage in both languages plus cross- linguisticmappings(publiclyavailableat https:// github.com/yoonwonj/EVOKE). The dataset is constructed by compiling lists of words to anno- tate from prior ...

    work page 2002

  4. [4]

    Dataset Construction Thedatasetconsistsofthreeseparatecomponents (see Figure 1): (1) Korean-English mappings, (2) annotations for Korean words, and (3) annotations for English words. 4.1. Word Selection and Translation Candidatewordsweregatheredfrompreviouswork on emotion words in English (Baron-Cohen et al., 2010; Morgan and Heise, 1988; Storm and Storm,

    work page 2010

  5. [5]

    To construct translational mappings (illustrated in Figure 1), two Korean-English bilingual speakers judged whether each word had translational equiv- alents in the other language

    and Korean (Jeon et al., 2022; Park and Min, 2005; Rhee and Ko, 2013). To construct translational mappings (illustrated in Figure 1), two Korean-English bilingual speakers judged whether each word had translational equiv- alents in the other language. A manual translation approachwasadoptedtocapturenuancedandpre- cise meanings that are difficult to obtain...

    work page 2022

  6. [6]

    feel” Does “I feel X

    were compared to ensure accurate and se- mantically rich matching, especially when transla- tionsprovidedinbilingualdictionariesvariedacross sources or when forward and backward translation yielded inconsistent results. A word is considered to have a translational equivalent when it can be translated to a single word form (i.e., one or more words exist in...

    work page 2004

  7. [7]

    having ups and downs

    Dataset Analysis and Evaluation 5.1. Word characteristics 5.1.1. Part-of-Speech Statistics Summary word statistics are in Table 2. A total of 1,426 Korean words and 1,397 English words were included. Translation equivalents were identified for all of these, forming many-to-many mappings across Korean and English (Figure 1). 5.1.2. Translational Equivalenc...

    work page 1999

  8. [8]

    I feel”, “They feel

    Future Applications 6.1. Theory-Driven Selection of Words This dataset is theory-agnostic, as it does not com- mit to a single theoretical stance on how emotion words should be defined. Researchers can apply their preferred annotation criteria to select emotion words that align with their theoretical perspectives and research needs, enabling the curation ...

    work page 2008

  9. [9]

    Fi- nally, the annotations of multiple meanings in this dataset (poly12–14) supports cross-cultural in- vestigations of emotion-related metaphors

    using the emotion terms in this dataset. Fi- nally, the annotations of multiple meanings in this dataset (poly12–14) supports cross-cultural in- vestigations of emotion-related metaphors. Com- paring the identified metaphorical extensions, re- searchers can assess the universality versus speci- ficity of emotion-related metaphors (Kövecses, 2003; Sauciuc, 2009)

    work page 2003

  10. [10]

    Conclusion The present dataset includes 1,426 Korean words and 1,397 English words, with many-to-many trans- lational mappings documented between the two languages. Using a feature-based annotation ap- proach, the dataset provides a theory-agnostic set of adjectives and verbs that can be selected as emotion words based on criteria drawn from prior studies...

    work page

  11. [11]

    All annotators were assigned anonymized coder IDs

    Ethical Considerations and Limitations The dataset does not involve serious ethical con- siderations, as it contains no personal or identi- fiable information. All annotators were assigned anonymized coder IDs. Despite the dataset’s contributions, this study has certain limitations. Although the dataset was constructedtocovermorethanonelanguage,there are ...

    work page

  12. [12]

    Acknowledgements We would like to thank the annotators for their dedi- cationandhardwork.Wealsothankanonymousre- viewers for their valuable feedback and comments

    work page

  13. [13]

    Bibliographical References David J Anderson and Ralph Adolphs. 2014. A framework for studying emotions across species. Cell, 157(1):187–200. Simon Baron-Cohen, Ofer Golan, Sally Wheel- wright, Yael Granader, and Jacqueline Hill. 2010. Emotion word comprehension from 4 to 16 years old: A developmental survey.Frontiers in evolu- tionary neuroscience, 2:109....

    work page 2014

  14. [14]

    Jana Declercq and Lotte van Poppel

    The psychological foundations of the af- fective lexicon.Journal of personality and social psychology, 53(4):751. Jana Declercq and Lotte van Poppel. 2023. Cod- ing metaphors in interaction: A study protocol and reflection on validity and reliability chal- lenges.International Journal of Qualitative Meth- ods, 22:16094069231164608. David Dowty. 1991. Them...

    work page 2023

  15. [15]

    Philip Nicholas Johnson-Laird and Keith Oatley

    User guide for kote: Korean online comments emotions dataset.arXiv preprint arXiv:2205.05300. Philip Nicholas Johnson-Laird and Keith Oatley

    work page arXiv

  16. [16]

    han" and the postcolonial afterlives of

    The language of emotions: An analysis of asemanticfield.Cognitionandemotion,3(2):81– 123. Philip Nicholas Johnson-Laird and Peter Cathcart Wason. 1977.Thinking: Readings in cognitive science. CUP Archive. Sandra So Hee Chi Kim. 2017. Korean" han" and the postcolonial afterlives of" the beauty of sor- row".Korean Studies, pages 253–279. Zoltán Kövecses. 20...

    work page 1977

  17. [17]

    내가/나는 X고느낀다

    Cultural influences on word meanings revealedthroughlarge-scalesemanticalignment. Nature Human Behaviour, 4(10):1029–1038. Ebru Türker. 2013. A corpus-based approach to emotion metaphors in korean: A case study of anger, happiness, and sadness.Review of Cog- nitive Linguistics. Published under the auspices of the Spanish Cognitive Linguistics Association,...

    work page 2013