Publication: Fine grain emotion analysis in Spanish using linguistic features and transformers
Authors
Salmerón Ríos, Alejandro ; García Díaz, José Antonio ; Pan, Ronghao ; Valencia García, Rafael
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Facultades de la UMU::Facultad de Informática
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Publisher
PeerJ
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DOI
https://doi.org/10.7717/peerj-cs.1992
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info:eu-repo/semantics/article
Description
Abstract
Mental health issues are a global concern, with a particular focus on the rise of
depression. Depression affects millions of people worldwide and is a leading cause
of suicide, particularly among young people. Recent surveys indicate an increase in
cases of depression during the COVID-19 pandemic, which affected approximately
5.4% of the population in Spain in 2020. Social media platforms such as X (formerly
Twitter) have become important hubs for health information as more people turn
to these platforms to share their struggles and seek emotional support. Researchers
have discovered a link between emotions and mental illnesses such as depression. This
correlation provides a valuable opportunity for automated analysis of social media
data to detect changes in mental health status that might otherwise go unnoticed, thus
preventing more serious health consequences. Therefore, this research explores the field
of emotion analysis in Spanish towards mental disorders. There are two contributions
in this area. On the one hand, the compilation, translation, evaluation and correction
of a novel dataset composed of a mixture of other existing datasets in the bibliography.
This dataset compares a total of 16 emotions, with an emphasis on negative emotions.
On the other hand, the in-depth evaluation of this novel dataset with several state-ofthe-
art transformers based on encoder-only and encoder-decoder architectures. The
analysis compromises monolingual, multilingual and distilled models as well as feature
integration techniques. The best results are obtained with the encoder-only MarIA
model, with a macro-average F1 score of 60.4771%.
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Citation
PeerJ Computer Science, 2024, Vol. 10 : e1992
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