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Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study
COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have fo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8685216/ https://www.ncbi.nlm.nih.gov/pubmed/34938711 http://dx.doi.org/10.3389/fpubh.2021.781827 |
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author | Ahmed, Syed Thouheed Singh, Dollar Konjengbam Basha, Syed Muzamil Abouel Nasr, Emad Kamrani, Ali K. Aboudaif, Mohamed K. |
author_facet | Ahmed, Syed Thouheed Singh, Dollar Konjengbam Basha, Syed Muzamil Abouel Nasr, Emad Kamrani, Ali K. Aboudaif, Mohamed K. |
author_sort | Ahmed, Syed Thouheed |
collection | PubMed |
description | COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have focused on multidimensional opportunities and solutions in encountering the COVID-19 pandemic. This study focuses on mental health and sentiment validations caused by the global lockdowns across the countries, resulting in a mental disability among individuals. This paper discusses a technique for identifying the mental state of an individual by sentiment analysis of feelings such as anxiety, depression, and loneliness caused by isolation and pauses to the normal chains of operations in daily life. The research uses a Neural Network (NN) to resolve and extract patterns and validate threshold trained datasets for decision making. This technique was used to validate 2,173 global speech samples, and the resulting accuracy of mental state and sentiments are identified with 93.5% accuracy in classifying the behavioral patterns of patients suffering from COVID-19 and pandemic-influenced depression. |
format | Online Article Text |
id | pubmed-8685216 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86852162021-12-21 Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study Ahmed, Syed Thouheed Singh, Dollar Konjengbam Basha, Syed Muzamil Abouel Nasr, Emad Kamrani, Ali K. Aboudaif, Mohamed K. Front Public Health Public Health COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have focused on multidimensional opportunities and solutions in encountering the COVID-19 pandemic. This study focuses on mental health and sentiment validations caused by the global lockdowns across the countries, resulting in a mental disability among individuals. This paper discusses a technique for identifying the mental state of an individual by sentiment analysis of feelings such as anxiety, depression, and loneliness caused by isolation and pauses to the normal chains of operations in daily life. The research uses a Neural Network (NN) to resolve and extract patterns and validate threshold trained datasets for decision making. This technique was used to validate 2,173 global speech samples, and the resulting accuracy of mental state and sentiments are identified with 93.5% accuracy in classifying the behavioral patterns of patients suffering from COVID-19 and pandemic-influenced depression. Frontiers Media S.A. 2021-12-06 /pmc/articles/PMC8685216/ /pubmed/34938711 http://dx.doi.org/10.3389/fpubh.2021.781827 Text en Copyright © 2021 Ahmed, Singh, Basha, Abouel Nasr, Kamrani and Aboudaif. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Ahmed, Syed Thouheed Singh, Dollar Konjengbam Basha, Syed Muzamil Abouel Nasr, Emad Kamrani, Ali K. Aboudaif, Mohamed K. Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study |
title | Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study |
title_full | Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study |
title_fullStr | Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study |
title_full_unstemmed | Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study |
title_short | Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study |
title_sort | neural network based mental depression identification and sentiments classification technique from speech signals: a covid-19 focused pandemic study |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8685216/ https://www.ncbi.nlm.nih.gov/pubmed/34938711 http://dx.doi.org/10.3389/fpubh.2021.781827 |
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