A deep learning-based approach for multi-label emotion classification in Tweets

Jabreel, Mohammed; Moreno, Antonio

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A deep learning-based approach for multi-label emotion classification in Tweets - imarina:5874004

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https://hdl.handle.net/20.500.11797/imarina5874004

Autor/es de la URV:	Moreno Ribas, Antonio
Autor según el artículo:	Jabreel, Mohammed; Moreno, Antonio
Direcció de correo del autor:	antonio.moreno@urv.cat
Identificador del autor:	0000-0003-3945-2314
Año de publicación de la revista:	2019
Tipo de publicación:	Journal Publications
ISSN:	20763417
e-ISSN:	2076-3417
Referencia de l'ítem segons les normes APA:	Jabreel, Mohammed; Moreno, Antonio (2019). A deep learning-based approach for multi-label emotion classification in Tweets. Applied Sciences-Basel, 9(6), 1123-1123. DOI: 10.3390/app9061123
Referencia al articulo segun fuente origial:	Applied Sciences-Basel. 9 (6): 1123-1123
Resumen:	© 2019 by the authors. Currently, people use online social media such as Twitter or Facebook to share their emotions and thoughts. Detecting and analyzing the emotions expressed in social media content benefits many applications in commerce, public health, social welfare, etc. Most previous work on sentiment and emotion analysis has only focused on single-label classification and ignored the co-existence of multiple emotion labels in one instance. This paper describes the development of a novel deep learning-based system that addresses the multiple emotion classification problem in Twitter. We propose a novel method to transform it to a binary classification problem and exploit a deep learning approach to solve the transformed problem. Our system outperforms the state-of-the-art systems, achieving an accuracy score of 0.59 on the challenging SemEval2018 Task 1:E-cmulti-label emotion classification problem.
DOI del artículo:	10.3390/app9061123
Enlace a la fuente original:	https://www.mdpi.com/2076-3417/9/6/1123
Versión del articulo depositado:	info:eu-repo/semantics/publishedVersion
Acceso a la licencia de uso:	https://creativecommons.org/licenses/by/3.0/es/
Departamento:	Enginyeria Informàtica i Matemàtiques
URL Documento de licencia:	https://repositori.urv.cat/ca/proteccio-de-dades/
Áreas temáticas:	Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física
Palabras clave:	Twitter Sentiment analysis Opinion mining Emotion classification Deep learning
Entidad:	Universitat Rovira i Virgili
Fecha de alta del registro:	2024-10-12
Volumen de revista:	9

Descripción:	© 2019 by the authors. Currently, people use online social media such as Twitter or Facebook to share their emotions and thoughts. Detecting and analyzing the emotions expressed in social media content benefits many applications in commerce, public health, social welfare, etc. Most previous work on sentiment and emotion analysis has only focused on single-label classification and ignored the co-existence of multiple emotion labels in one instance. This paper describes the development of a novel deep learning-based system that addresses the multiple emotion classification problem in Twitter. We propose a novel method to transform it to a binary classification problem and exploit a deep learning approach to solve the transformed problem. Our system outperforms the state-of-the-art systems, achieving an accuracy score of 0.59 on the challenging SemEval2018 Task 1:E-cmulti-label emotion classification problem.
Tipo:	Journal Publications
Coautor:	Universitat Rovira i Virgili
Títol:	A deep learning-based approach for multi-label emotion classification in Tweets
Materia:	Chemistry, Multidisciplinary,Computer Science Applications,Engineering (Miscellaneous),Engineering, Multidisciplinary,Fluid Flow and Transfer Processes,Instrumentation,Materials Science (Miscellaneous),Materials Science, Multidisciplinary,Physics, Applied,Process Chemistry and Technology Twitter Sentiment analysis Opinion mining Emotion classification Deep learning Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física
Fecha:	2019
Autor:	Jabreel, Mohammed Moreno, Antonio
Derechos:	info:eu-repo/semantics/openAccess

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