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Multidimensional Affective Analysis for Low-Resource Languages: A Use Case with Guarani-Spanish Code-Switching Language
dc.contributor.author | Agüero-Torales, Marvin M. | |
dc.contributor.author | López-Herrera, Antonio G. | |
dc.contributor.author | Vilares, David | |
dc.date.accessioned | 2023-11-29T17:02:07Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Agüero-Torales, M.M., López-Herrera, A.G. & Vilares, D. Multidimensional Affective Analysis for Low-Resource Languages: A Use Case with Guarani-Spanish Code-Switching Language. Cogn Comput 15, 1391–1406 (2023). https://doi.org/10.1007/s12559-023-10165-0 | es_ES |
dc.identifier.issn | 1866-9964 | |
dc.identifier.uri | http://hdl.handle.net/2183/34367 | |
dc.description.abstract | [Abstract]: This paper focuses on text-based affective computing for Jopara, a code-switching language that combines Guarani and Spanish. First, we collected a dataset of tweets primarily written in Guarani and annotated them for three widely used dimensions in sentiment analysis: (a) emotion recognition, (b) humor detection, and (c) offensive language identification. Then, we developed several neural network models, including large language models specifically designed for Guarani, and compared their performance against off-the-shelf multilingual and Spanish pre-trained models for the aforementioned dimensions. Our experiments show that language models incorporating Guarani during pre-training or pre-fine-tuning consistently achieve the best results, despite limited resources (a single 24-GB GPU and only 800K tokens). Notably, even a Guarani BERT model with just two layers of Transformers shows a favorable balance between accuracy and computational power, likely due to the inherent low-resource nature of the task. We present a comprehensive overview of corpus creation and model development for low-resource languages like Guarani, particularly in the context of its code-switching with Spanish, resulting in Jopara. Our findings shed light on the challenges and strategies involved in analyzing affective language in such linguistic contexts. | es_ES |
dc.description.sponsorship | This work is supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the FBBVA. This paper has also received funding from grant SCANNER-UDC (PID2020-113230RB-C21) funded by MCIN/AEI/10.13039/501100011033, the European Research Council (ERC), which has supported this research under the European Union’s Horizon Europe research and innovation programme (SALSA, grant agreement no. 101100615), Xunta de Galicia (ED431C 2020/11), and Centro de Investigación de Galicia “CITIC,” funded by Xunta de Galicia and the European Union (ERDF — Galicia 2014–2020 Program), by grant ED431G 2019/01. Additionally, the research leading to these results received funding from the University of Granada, Generalitat Valenciana, and the University of Alicante (IDIFEDER/2020/003). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/11 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113230RB-C21/ES/ACCIONES DE DINAMIZACIÓN EUROPA INVESTIGACIÓN | es_ES |
dc.relation.uri | https://doi.org/10.1007/s12559-023-10165-0 | es_ES |
dc.rights | This version of the article has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https: //doi.org/10.1007/s12559-023-10165-0. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/ gp/open-research/policies/acceptedmanuscript-terms | es_ES |
dc.subject | Natural language processing | es_ES |
dc.subject | Sentiment analysis | es_ES |
dc.subject | Affective analysis | es_ES |
dc.subject | Code-switching | es_ES |
dc.subject | Low-resource languages | es_ES |
dc.title | Multidimensional Affective Analysis for Low-Resource Languages: A Use Case with Guarani-Spanish Code-Switching Language | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/embargoedAccess | es_ES |
dc.date.embargoEndDate | 2024-07-15 | es_ES |
dc.date.embargoLift | 2024-07-15 | |
UDC.journalTitle | Cognitive Computation | es_ES |
UDC.volume | 15 | es_ES |
UDC.issue | 4 | es_ES |
UDC.startPage | 1391 | es_ES |
UDC.endPage | 1406 | es_ES |
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