Examining emotions in English and translated Chinese children's literature: a bilingual emotion detection model based on LLMs.
This study investigates the Chinese-English bilingual emotion detection within the context of children's literature. The study utilizes a parallel corpus of classical Chinese-English children's literature and compiles a bilingual dataset of emotionally-labelled text. The dataset is then leveraged to...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 4; pp. 3521 - 3554 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=189912021&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 189912021 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2025 vid: 59 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 189912021 10.1007/s10579-025-09846-z ppf: 3521 ppct: 33 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.8MB tig: atl: Examining emotions in English and translated Chinese children's literature: a bilingual emotion detection model based on LLMs. aug: au: Liu, Yanjin Lee, Sophia Yat Mei Li, Dechao affil: https://ror.org/04gpd4q15 Faculty of Humanities and Social Sciences, City University of Macau, Macau, China https://ror.org/0030zas98 Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China su: Emotion recognition Children's literature Bilingualism Cross-cultural studies Language models Multilingualism Chinese language Facial expression sug: subj: Emotion recognition Children's literature Bilingualism Cross-cultural studies Language models Multilingualism Chinese language Facial expression keyword: Bilingual emotion analysis Communication and Culture Linguistics Literary Studies Emotion detection Fine-tuning Language Large language models ab: This study investigates the Chinese-English bilingual emotion detection within the context of children's literature. The study utilizes a parallel corpus of classical Chinese-English children's literature and compiles a bilingual dataset of emotionally-labelled text. The dataset is then leveraged to fine-tune and evaluate the performance of various Large Language Models (LLMs). The results indicate that the GPT-4o model outperforms alternative LLMs, achieving an F1 Micro score of 0.779 and an F1 Macro score of 0.764 on the evaluation task. These findings substantiate the viability of cross-lingual emotion detection within this domain and underscore the importance of selecting appropriate pre-training techniques. Furthermore, this study addresses specific cross-cultural challenges inherent in bilingual emotion detection, elucidating the complexities posed by language-specific and culturally bound emotional expressions. This study contributes to the expanding body of literature on emotion recognition in multilingual contexts, particularly in relation to the analysis of affective content in cross-cultural translated children's literature, and provides insights for future investigations in this field. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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