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Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing

2024Journal article Open access
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dc.contributor.authorBranco, Alexandre
dc.contributor.authorParada, Daniel
dc.contributor.authorSilva, Marcos
dc.contributor.authorMendonça, Fábio
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorDias, Fernando Morgado
dc.date.accessioned2024-02-20T11:55:15Z
dc.date.available2024-02-20T11:55:15Z
dc.date.issued2024
dc.description.abstractThis study focuses on improving sentiment analysis in restaurant reviews by leveraging transfer learning and transformer-based pre-trained models. This work evaluates the suitability of pre trained deep learning models for analyzing Natural Language Processing tasks in Portuguese. It also explores the viability of utilizing edge devices for Natural Language Processing tasks, considering their computational limitations and resource constraints. Specifically, we employ bidirectional encoder representations from transformers and robustly optimized BERT approach, two state-of-the art models, to build a sentiment review classifier. The classifier’s performance is evaluated using accuracy and area under the receiver operating characteristic curve as the primary metrics. Our results demonstrate that the classifier developed using ensemble techniques outperforms the baseline model (from 0.80 to 0.84) in accurately classifying restaurant review sentiments when three classes are considered (negative, neutral, and positive), reaching an accuracy and area under the receiver operating characteristic curve higher than 0.8 when examining a Zomato restaurant review dataset, provided for this work. This study seeks to create a model for the precise classification of Portuguese reviews into positive, negative, or neutral categories. The flexibility of deploying our model on affordable hardware platforms suggests its potential to enable real-time solutions. The deployment of the model on edge computing platforms improves accessibility in resource-constrained environments.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBranco, A.; Parada, D.; Silva, M.; Mendonça, F.; Mostafa, S.S.; Morgado-Dias, F. Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing. Electronics 2024, 13, 589. https://doi.org/10.3390/ electronics13030589pt_PT
dc.identifier.doi10.3390/electronics13030589pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5565
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLaboratory of Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSentiment analysispt_PT
dc.subjectNatural language processingpt_PT
dc.subjectPortuguese languagept_PT
dc.subjectEdge computingpt_PT
dc.subjectBERTpt_PT
dc.subjectTransformerspt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleSentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.citation.issue3pt_PT
oaire.citation.startPage589pt_PT
oaire.citation.titleElectronicspt_PT
oaire.citation.volume13pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSilva Mendonça
person.familyNameMostafa
person.familyNameMorgado-Dias
person.givenNameFábio Rúben
person.givenNameSheikh Shanawaz
person.givenNameFernando
person.identifier34497
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0001-7334-3993
person.identifier.ridN-9228-2015
person.identifier.scopus-author-id55489640900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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