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

Frontiers in Public Health

Dollar Konjengbam Singh¹  Emad Abouel Nasr²  Mohamed K. Aboudaif²  Ali K. Kamrani³  Syed Muzamil Basha⁴  Syed Thouheed Ahmed⁵ 

[1] ICT for Internet and Multimedia, University of Padua, Padua, Italy;Industrial Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia;Industrial Engineering Department, College of Engineering, University of Houston, Houston, TX, United States;School of Computer Science and Engineering, REVA University, Bengaluru, India;School of Computing and Information Technology, REVA University, Bengaluru, India;

关键词: sentiment extraction;    speech signal processing;    COVID-19;    mental depression;    neural network;   

DOI  :  10.3389/fpubh.2021.781827

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown