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Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

Authors :
Syed Thouheed Ahmed
Dollar Konjengbam Singh
Syed Muzamil Basha
Emad Abouel Nasr
Ali K. Kamrani
Mohamed K. Aboudaif
Source :
Frontiers in Public Health, Vol 9 (2021)
Publication Year :
2021
Publisher :
Frontiers Media S.A., 2021.

Abstract

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.

Details

Language :
English
ISSN :
22962565
Volume :
9
Database :
Directory of Open Access Journals
Journal :
Frontiers in Public Health
Publication Type :
Academic Journal
Accession number :
edsdoj.bc1c74ee6d6647bab388ddf40c94027b
Document Type :
article
Full Text :
https://doi.org/10.3389/fpubh.2021.781827