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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
23/04/2024 |
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Data da última atualização: |
23/04/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
NOGUEIRA, T. da S.; SIQUEIRA, K. B.; GOLIATT, P. V. Z. C. |
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Afiliação: |
THALLYS DA SILVA NOGUEIRA, UNIVERSIDADE FEDERAL DE JUIZ DE FORA; KENNYA BEATRIZ SIQUEIRA, CNPGL; PRISCILA VANESSA ZABALA CAPRILES GOLIATT, UNIVERSIDADE FEDERAL DE JUIZ DE FORA. |
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Título: |
Construction of a training dataset for a sentiment analysis model of dairy products tweets in Brazil. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Social Network Analysis and Mining, v. 14, 85, 2024. |
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DOI: |
https://doi.org/10.1007/s13278-024-01254-5 |
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Idioma: |
Inglês |
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Conteúdo: |
Creating specific datasets for machine learning models is a frequent and challenging task, requiring considerable effort in sample collection and maintaining a balanced representation of each class. In this study, our objective was to create a training dataset for a sentiment analysis model by combining results obtained from 5 natural language processing tools through 3 distinct approaches, aiming to automatically label various tweets in the negative, neutral, and positive classes. Additionally, we applied data balancing techniques to assess different methods' impacts on the sentiment analysis models' ability to generalize classes to previously unseen samples. The results demonstrated that the three approaches used to combine tool results and apply balancing techniques provided significantly superior outcomes compared to models with imbalanced datasets. These advancements enabled sentiment analysis models to achieve greater precision and generalization capacity for novel samples. These findings underscore the importance of considering effective data balancing strategies when creating training datasets for machine learning applications, especially in tasks sensitive to class imbalance, such as sentiment analysis. This enhanced approach is crucial to improving the performance and applicability of sentiment analysis models in real-world scenarios, providing more precise data analyses that unveil valuable insights in digital marketing. |
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Palavras-Chave: |
Artifcial intelligence; Business intelligence; Digital marketing; Inteligência artificial; Market digital; Market research; Sentiment analysis. |
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Thesagro: |
Laticínio; Pesquisa de Mercado; Produto Derivado do Leite. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
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001 2163827
005 2024-04-23
008 2024 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s13278-024-01254-5$2DOI
100 1 $aNOGUEIRA, T. da S.
245 $aConstruction of a training dataset for a sentiment analysis model of dairy products tweets in Brazil.$h[electronic resource]
260 $c2024
520 $aCreating specific datasets for machine learning models is a frequent and challenging task, requiring considerable effort in sample collection and maintaining a balanced representation of each class. In this study, our objective was to create a training dataset for a sentiment analysis model by combining results obtained from 5 natural language processing tools through 3 distinct approaches, aiming to automatically label various tweets in the negative, neutral, and positive classes. Additionally, we applied data balancing techniques to assess different methods' impacts on the sentiment analysis models' ability to generalize classes to previously unseen samples. The results demonstrated that the three approaches used to combine tool results and apply balancing techniques provided significantly superior outcomes compared to models with imbalanced datasets. These advancements enabled sentiment analysis models to achieve greater precision and generalization capacity for novel samples. These findings underscore the importance of considering effective data balancing strategies when creating training datasets for machine learning applications, especially in tasks sensitive to class imbalance, such as sentiment analysis. This enhanced approach is crucial to improving the performance and applicability of sentiment analysis models in real-world scenarios, providing more precise data analyses that unveil valuable insights in digital marketing.
650 $aLaticínio
650 $aPesquisa de Mercado
650 $aProduto Derivado do Leite
653 $aArtifcial intelligence
653 $aBusiness intelligence
653 $aDigital marketing
653 $aInteligência artificial
653 $aMarket digital
653 $aMarket research
653 $aSentiment analysis
700 1 $aSIQUEIRA, K. B.
700 1 $aGOLIATT, P. V. Z. C.
773 $tSocial Network Analysis and Mining$gv. 14, 85, 2024.
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