Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.13/5565 |
Resumo: | This 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. |
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Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge ComputingSentiment analysisNatural language processingPortuguese languageEdge computingBERTTransformers.Faculdade de Ciências Exatas e da EngenhariaThis 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.MDPIDigitUMaBranco, AlexandreParada, DanielSilva, MarcosMendonça, FábioMostafa, Sheikh ShanawazDias, Fernando Morgado2024-02-20T11:55:15Z20242024-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.13/5565engBranco, 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/ electronics1303058910.3390/electronics13030589info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-25T04:57:01Zoai:digituma.uma.pt:10400.13/5565Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:11:34.093645Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing |
title |
Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing |
spellingShingle |
Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing Branco, Alexandre Sentiment analysis Natural language processing Portuguese language Edge computing BERT Transformers . Faculdade de Ciências Exatas e da Engenharia |
title_short |
Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing |
title_full |
Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing |
title_fullStr |
Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing |
title_full_unstemmed |
Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing |
title_sort |
Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing |
author |
Branco, Alexandre |
author_facet |
Branco, Alexandre Parada, Daniel Silva, Marcos Mendonça, Fábio Mostafa, Sheikh Shanawaz Dias, Fernando Morgado |
author_role |
author |
author2 |
Parada, Daniel Silva, Marcos Mendonça, Fábio Mostafa, Sheikh Shanawaz Dias, Fernando Morgado |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
DigitUMa |
dc.contributor.author.fl_str_mv |
Branco, Alexandre Parada, Daniel Silva, Marcos Mendonça, Fábio Mostafa, Sheikh Shanawaz Dias, Fernando Morgado |
dc.subject.por.fl_str_mv |
Sentiment analysis Natural language processing Portuguese language Edge computing BERT Transformers . Faculdade de Ciências Exatas e da Engenharia |
topic |
Sentiment analysis Natural language processing Portuguese language Edge computing BERT Transformers . Faculdade de Ciências Exatas e da Engenharia |
description |
This 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. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-02-20T11:55:15Z 2024 2024-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.13/5565 |
url |
http://hdl.handle.net/10400.13/5565 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Branco, 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/ electronics13030589 10.3390/electronics13030589 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137765759123456 |