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Título
Social Network Sentiment Analysis Using Hybrid Deep Learning Models
Autor
Año del Documento
2023-10-23
Editorial
MDPI
Descripción
Producción Científica
Documento Fuente
Applied Sciences, 2023, vol. 13, n. 20, 11608
Résumé
The exponential growth in information on the Internet, particularly within social networks, highlights the importance of sentiment and opinion analysis. The intrinsic characteristics of the Spanish language coupled with the short length and lack of context of messages on social media pose a challenge for sentiment analysis in social networks. In this study, we present a hybrid deep learning model combining convolutional and long short-term memory layers to detect polarity levels in Twitter for the Spanish language. Our model significantly improved the accuracy of existing approaches by up to 20%, achieving accuracies of around 76% for three polarities (positive, negative, neutral) and 91% for two polarities (positive, negative).
Palabras Clave
deep learning
hybrid strategies
sentiment analysis
social networks
Twitter
Spanish
ISSN
2076-3417
Revisión por pares
SI
Version del Editor
Propietario de los Derechos
© 2023 MDPI
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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