Opinion mining for app reviews: an analysis of textual representation and predictive models (2022)
- Authors:
- USP affiliated authors: MARCACINI, RICARDO MARCONDES - ICMC ; ARAUJO, ADAILTON FERREIRA DE - ICMC ; GÔLO, MARCOS PAULO SILVA - ICMC
- Unidade: ICMC
- DOI: 10.1007/s10515-021-00301-1
- Subjects: MINERAÇÃO DE DADOS; RECONHECIMENTO DE TEXTO; APRENDIZADO COMPUTACIONAL; ENGENHARIA DE REQUISITOS
- Keywords: Opinion mining; Mobile applications
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Automated Software Engineering
- ISSN: 0928-8910
- Volume/Número/Paginação/Ano: v. 29, n. 1, p. 1-30, May 2022
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
ARAUJO, Adailton Ferreira de e GÔLO, Marcos Paulo Silva e MARCACINI, Ricardo Marcondes. Opinion mining for app reviews: an analysis of textual representation and predictive models. Automated Software Engineering, v. 29, n. 1, p. 1-30, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10515-021-00301-1. Acesso em: 31 mar. 2026. -
APA
Araujo, A. F. de, Gôlo, M. P. S., & Marcacini, R. M. (2022). Opinion mining for app reviews: an analysis of textual representation and predictive models. Automated Software Engineering, 29( 1), 1-30. doi:10.1007/s10515-021-00301-1 -
NLM
Araujo AF de, Gôlo MPS, Marcacini RM. Opinion mining for app reviews: an analysis of textual representation and predictive models [Internet]. Automated Software Engineering. 2022 ; 29( 1): 1-30.[citado 2026 mar. 31 ] Available from: https://doi.org/10.1007/s10515-021-00301-1 -
Vancouver
Araujo AF de, Gôlo MPS, Marcacini RM. Opinion mining for app reviews: an analysis of textual representation and predictive models [Internet]. Automated Software Engineering. 2022 ; 29( 1): 1-30.[citado 2026 mar. 31 ] Available from: https://doi.org/10.1007/s10515-021-00301-1 - Detecting relevant app reviews for software evolution and maintenance through multimodal one-class learning
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- Temporal dynamics of requirements engineering from mobile app reviews
- Hierarchical cluster labeling of software requirements using contextual word embeddings
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- Text representation through multimodal variational autoencoder for one-class learning
- One-class edge classification through heterogeneous hypergraph for causal discovery
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