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Spanish Sentiment Analysis Using Universal Language Model Fine-Tuning: A Detailed Case of Study

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dc.contributor.author Palomino D. es_PE
dc.contributor.author Ochoa-Luna J. es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2020
dc.description.abstract Transfer Learning has emerged as one of the main image classification techniques for reusing architectures and weights trained on big datasets so as to improve small and specific classification tasks. In Natural Language Processing, a similar effect is obtained by reusing and transferring a language model. In particular, the Universal Language Fine-Tuning (ULMFiT) algorithm has proven to have an impressive performance on several English text classification tasks. In this paper, we aim at improving current state-of-the-art algorithms for Spanish Sentiment Analysis of short texts. In order to do so, we have adapted a ULMFiT algorithm to this setting. Experimental results on benchmark datasets show the potential of our approach. © Springer Nature Switzerland AG 2020.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1007/978-3-030-46140-9_20
dc.identifier.scopus 2-s2.0-85084819494
dc.identifier.uri https://hdl.handle.net/20.500.12390/2591
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Communications in Computer and Information Science
dc.rights info:eu-repo/semantics/openAccess
dc.subject Transfer Learning
dc.subject Language Model es_PE
dc.subject Natural Language Processing es_PE
dc.subject Sentiment Analysis es_PE
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#2.02.04
dc.title Spanish Sentiment Analysis Using Universal Language Model Fine-Tuning: A Detailed Case of Study
dc.type info:eu-repo/semantics/article
dspace.entity.type Publication
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