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

  • Dades identificatives

    Identificador: imarina:5874004
    Autors:
    Jabreel, MohammedMoreno, Antonio
    Resum:
    © 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.
  • Altres:

    Autor segons l'article: Jabreel, Mohammed; Moreno, Antonio
    Departament: Enginyeria Informàtica i Matemàtiques
    e-ISSN: 2076-3417
    Autor/s de la URV: Moreno Ribas, Antonio
    Paraules clau: Twitter Sentiment analysis Opinion mining Emotion classification Deep learning
    Resum: © 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.
    Àrees temàtiques: 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
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 20763417
    Adreça de correu electrònic de l'autor: antonio.moreno@urv.cat
    Identificador de l'autor: 0000-0003-3945-2314
    Data d'alta del registre: 2024-10-12
    Volum de revista: 9
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/2076-3417/9/6/1123
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Applied Sciences-Basel. 9 (6): 1123-1123
    Referència 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
    DOI de l'article: 10.3390/app9061123
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2019
    Tipus de publicació: Journal Publications
  • Paraules clau:

    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
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