Pre-trained ensemble model for identification of emotion during COVID-19 based on emergency response support system dataset.

Autor: Nimmi K; Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India., Janet B; Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India., Selvan AK; Centre for Development of Advanced Computing (C-DAC), Thiruvananthapuram, India., Sivakumaran N; Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli, India.
Jazyk: angličtina
Zdroj: Applied soft computing [Appl Soft Comput] 2022 Jun; Vol. 122, pp. 108842. Date of Electronic Publication: 2022 Apr 18.
DOI: 10.1016/j.asoc.2022.108842
Abstrakt: The COVID-19 precautions, lockdown, and quarantine implemented throughout the epidemic resulted in a worldwide economic disaster. People are facing unprecedented levels of intense threat, necessitating professional, systematic psychiatric intervention and assistance. New psychological services must be established as quickly as possible to support the mental healthcare needs of people in this pandemic condition. This study examines the contents of calls landed in the emergency response support system (ERSS) during the pandemic. Furthermore, a combined analysis of Twitter patterns connected to emergency services could be valuable in assisting people in this pandemic crisis and understanding and supporting people's emotions. The proposed Average Voting Ensemble Deep Learning model (AVEDL Model) is based on the Average Voting technique. The AVEDL Model is utilized to classify emotion based on COVID-19 associated emergency response support system calls (transcribed) along with tweets. Pre-trained transformer-based models BERT, DistilBERT, and RoBERTa are combined to build the AVEDL Model, which achieves the best results. The AVEDL Model is trained and tested for emotion detection using the COVID-19 labeled tweets and call content of the emergency response support system. This is the first deep learning ensemble model using COVID-19 emotion analysis to the best of our knowledge. The AVEDL Model outperforms standard deep learning and machine learning models by attaining an accuracy of 86.46 percent and Macro-average F1-score of 85.20 percent.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2022 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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