Data/hora: 04/06/2024 04:40:56

Biblioteca(s): 	Embrapa Gado de Leite.	
Tipo da produção científica: 	Artigo em Periódico Indexado	
Circulação/Nível: 	A - 2	
Data corrente: 	23/04/2024	
Data da última atualização: 	23/04/2024	
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	
Registro original: 	Embrapa Gado de Leite (CNPGL)	
Exemplares: 	CNPGL - (26353-1) - UPC - AP - DD -  -  -  - DIS