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dc.contributor.authorArroni, Sergio
dc.contributor.authorGalán, Yerai
dc.contributor.authorGuzmán-Guzmán, Xiomarah
dc.contributor.authorNuñez-Valdez, Edward Rolando
dc.contributor.authorGómez, Alberto
dc.date2023-03
dc.date.accessioned2023-03-07T13:58:13Z
dc.date.available2023-03-07T13:58:13Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/14293
dc.description.abstractSentiment analysis is of great importance to parties who are interested is analyzing the public opinion in social networks. In recent years, deep learning, and particularly, the attention-based architecture, has taken over the field, to the point where most research in Natural Language Processing (NLP) has been shifted towards the development of bigger and bigger attention-based transformer models. However, those models are developed to be all-purpose NLP models, so for a concrete smaller problem, a reduced and specifically studied model can perform better. We propose a simpler attention-based model that makes use of the transformer architecture to predict the sentiment expressed in tweets about hotels in Las Vegas. With their relative predicted performance, we compare the similarity of our ranking to the actual ranking in TripAdvisor to those obtained by more rudimentary sentiment analysis approaches, outperforming them with a 0.64121 Spearman correlation coefficient. We also compare our performance to DistilBERT, obtaining faster and more accurate results and proving that a model designed for a particular problem can perform better than models with several millions of trainable parameters.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 8, nº 1
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/3267es_ES
dc.rightsopenAccesses_ES
dc.subjectartificial intelligencees_ES
dc.subjectmachine learninges_ES
dc.subjectnatural language processinges_ES
dc.subjectsentiment analysises_ES
dc.subjecttransformeres_ES
dc.subjectIJIMAIes_ES
dc.titleSentiment Analysis and Classification of Hotel Opinions in Twitter With the Transformer Architecturees_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2023.02.005


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