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1. | | HONG, J.; KIM, D.; CHO, K.; SA, S.; CHOI, S.; KIM, Y.; PARK, J.; SCHMIDT, G. S.; DAVIS, M. E.; CHUNG, H. Effects of genetic variants for the swine FABP3, HMGA1, MC4R, IGF2, and FABP4 genes on fatty acid composition. Meat Science, v. 110, p. 46-51, 2015. Biblioteca(s): Embrapa Suínos e Aves. |
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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
Biblioteca(s): |
Embrapa Gado de Leite. |
Data corrente: |
23/04/2024 |
Data da última atualização: |
23/04/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
NOGUEIRA, T. da S.; SIQUEIRA, K. B.; GOLIATT, P. V. Z. C. |
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. |
Título: |
Construction of a training dataset for a sentiment analysis model of dairy products tweets in Brazil. |
Ano de publicação: |
2024 |
Fonte/Imprenta: |
Social Network Analysis and Mining, v. 14, 85, 2024. |
DOI: |
https://doi.org/10.1007/s13278-024-01254-5 |
Idioma: |
Inglês |
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. |
Palavras-Chave: |
Artifcial intelligence; Business intelligence; Digital marketing; Inteligência artificial; Market digital; Market research; Sentiment analysis. |
Thesagro: |
Laticínio; Pesquisa de Mercado; Produto Derivado do Leite. |
Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
Marc: |
LEADER 02348naa a2200277 a 4500 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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